Difference between revisions of "Apple Watch"

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An '''Apple Watch''' is a wearable smartwatch that allows users to perform a variety of tasks. In order to function, the watch needs to be paired with an iPhone 5 or later model. <ref> https://searchmobilecomputing.techtarget.com/definition/Apple-Watch </ref>.
 
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Processes like cooking a meal or reading a manual to assemble a new piece of furniture are examples of algorithms in everyday life<ref>T.C. (August 29, 2017). [https://www.economist.com/the-economist-explains/2017/08/29/what-are-algorithms "What Are Algorithms?"] ''The Economist''. Retrieved April 28, 2019.</ref>. Algorithms are grounded in logic. The increase in their logical complexity via advancements in technology and human effort have provided the foundations for technological concepts such as artificial intelligence and machine learning<ref>McClelland, Calum. (December 4, 2017). [https://medium.com/iotforall/the-difference-between-artificial-intelligence-machine-learning-and-deep-learning-3aa67bff5991 "The Difference Between Artificial Intelligence, Machine Learning, and Deep Learning"]. ''Medium''. Retrieved April 28, 2019.</ref>. The influence of algorithms is pervasive and in computer science so it leads to an increase in ethical concerns in the areas of bias, privacy, and accountability.
 
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=== Apple Watch Features ===
 
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The Apple Watch offers a variety of features that allows the users to engage with the watch in their everyday lives.
 
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== Fitness Tracking ==
 
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The Apple watch offers fitness tracking in the form of '''Activity Rings''' which is broken down into three colored rings. The first is the red '''Move''' ring which allows users to track how many calories they have burned by moving throughout a 24 hour period. The second is the green '''Exercise''' ring that allows users to track the minutes of brisk activity they have completed that day. Finally, the blue'''Stand''' ring allows users to track how many hours they have stood in a 24 hour period. <ref>https://learning-oreilly-com.proxy.lib.umich.edu/library/view/apple-watch-for/9781119658665/c08.xhtml#h2-6<ref> The user can define their activity goals for each type of ring, and the ring visually closes once the user achieves that goal.
 
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[[File:appleWatchRings.png|500px]]
 
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The visualization shown above demonstrates the different types of rings, as well as the users progress in completing the goals associated with each ring.
 
  
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== History ==
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[[File:AppleWatch7.png|thumb|200px|right|Image of the Apple Watch Series 7, from AT&T website. <ref name="AppleWatchDocs">"Apple Watch Documentation."  <https://www.att.com/buy/wearables/apple-watch-series-7-41mm-starlight-aluminum-starlight-sport.html?source=EC1NSpDES0000000P&tfn=wireless&WT.srch=1&wtExtndSource=PRODUCT_GROUP&gclsrc=aw.ds&&gclid=Cj0KCQiAjJOQBhCkARIsAEKMtO04UBmqiHJg_8MSWh-dvlXea5u8BGIgdlFT4PSreL3DnDeL_tfK11AaAs5REALw_wcB&gclsrc=aw.ds>.</ref>]]
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The Apple Watch was first released in 2015, with three distinct models; however, the genesis of the Apple Watch reportedly started with Jony Ive, Apple's chief designer, in 2011. <ref>https://appleinsider.com/inside/apple-watch</ref>. Since 2015, Apple has released multiple other models which provide different features to the user.  
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Apple Watch is a line of smartwatches, or computers that users can wear around their wrists, from the technology company Apple[https://www.apple.com/] and allows users to perform a variety of tasks <ref>https://www.apple.com/</ref>. In order to function, the watch needs to be paired with other Apple products, specifically an iPhone5 model or later. <ref> https://searchmobilecomputing.techtarget.com/definition/Apple-Watch </ref>. The Apple Watch is equipped with software and hardware capabilities that gives it the ability to interact with the user in a variety of ways. <br />
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There are different Apple Watch models that users can choose from, which each offering a unique array of features to the users. In terms of the public perception of the Apple Watch, it has gained both negative and positive feedback from the public, but has overall been a popular product for Apple. In 2020 alone, Apple sold 33.9 million Apple Watches to users, making it a leader in the smartwatch industry over comparable brands like Huawei, Samsung, BBK, and fitbit. <ref>https://www.statista.com/chart/15035/worldwide-smartwatch-shipments/#:~:text=According%20to%20estimates%20from%20Counterpoint,leader%20in%20the%20smartwatch%20market.</ref>. <br />
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In looking at the ethical considerations of the product, the Apple Watch collects sensitive user data, including health related data. As a device that connects with other Apple products, there are security concerns related to the data that it collects in relation to being a internet of Things device. Further, there are rising concerns that the Apple Watch may intendedly lead to the perpetuation of inequalities in society <ref>https://www.frontiersin.org/articles/10.3389/fcvm.2020.615927/full</ref>.
  
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== Brief History ==
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The Apple Watch was first released in 2015, with three distinct models; however, the genesis of the Apple Watch reportedly started with Jony Ive, Apple's chief designer, in 2011. <ref>https://appleinsider.com/inside/apple-watch</ref>. Ive reportedly began "dreaming about an Apple watch just after CEO Steve Jobs' death in October 2011" and brought to idea to Alan Dye, the chief of Apple's human interface group to put the idea to life <ref> https://www.wired.com/2015/04/the-apple-watch/ </ref>. The intention of the watch was to reportedly free people from their phones <ref> https://www.wired.com/2015/04/the-apple-watch/ </ref>. The designers of the Apple Watch uncovered early in the process that they needed to break away from Apple's status quo of only offering a few product options as the watch industry values personalization and beauty. That realization spurred the development the watch which offered a variety of options in order to appeal to a way consumer base. <ref>https://www.wired.com/2015/04/the-apple-watch/</ref>
  
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=== Computation ===
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== Hardware Capabilities ==
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Another cornerstone for algorithms comes from [https://en.wikipedia.org/wiki/Alan_Turing Alan Turing] and his contributions to cognitive and [https://en.wikipedia.org/wiki/Computer_science computer science]. Turing conceptualized the concept of cognition and designed ways to emulate human cognition with machines. This process turned the human thought process into mathematical algorithms and it led to the development of Turing Machines. It capitalized on these theoretical algorithms to perform unique functions and the development of computers. As their name suggests, computers utilized specific rules or algorithms to compute and it is these machines (or sometimes people)<ref>[crgis.ndc.nasa.gov/historic/Human_Computers "Human Computers"]. ''NASA Cultural Resources''. Retrieved April 28, 2019.</ref> that most often relate to the concept of algorithms that is used today. With the advent of mechanical computers, the computer science field paved the way for algorithms to run the world as they do now by calculating and controlling an immense quantity of facets of daily life. To this day, Turing machines are a main area of study in the theory of computation.
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[[File:HowitWorksApple.jpeg|300px|thumb|left|Image of the Apple Watch Hardware Capabilities <ref >https://www.dailymail.co.uk/sciencetech/article-3030492/Take-peek-inside-Apple-Watch-Infographic-reveals-firm-packs-components-needed-power-wearable.html</ref>]]
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The hardware installed on the Apple Watch includes a digital crown, friends button, touchscreen, taptic engine, battery, s1 chip, loudspeaker, custom-heart rate sensor, induction charger, and a customizable appearance. The "digital crown," is a small button on the side of the watch that users can rotate and press in order to scroll through and engage with content on their watch <ref>https://www.dailymail.co.uk/sciencetech/article-3030492/Take-peek-inside-Apple-Watch-Infographic-reveals-firm-packs-components-needed-power-wearable.html</ref>. The friends button, located next to the crown on the side of the watch, brings up a shortlist of chosen contacts to the user. The touchscreen is a retina display that allows users to interact with the watches face, through tapping on the small touchscreen interface with their finger. The touchscreen is polished with crystal in order to prevent users from scratching the screen. The taptic engine delivers tactile sensations to the user, and essentially taps the user's wrist to deliver tactile feedback when interfacing with the digital crown or force touch. It also helps notify users of alerts and notifications <ref>https://www.iphonefaq.org/archives/974320</ref>. The battery allows the watch to run throughout the day without running out of battery. The Apple Watch comes equipped with a charger, which is used to recharge the battery after use. Similar to the iPhone, the Apple Watch typically lasts about a full day's use before needing to recharge, depending on usage. The s1 chip contains the entire computer system needed for the Apple Watch to run. The loudspeaker which has a multidirectional microphone that allows it to listen and process multiple conversations around and involving the user <ref>https://www.commonsense.org/education/articles/privacy-and-security-evaluation-of-the-apple-watch</ref>. It allows for the watch to output noise, or speak, to the user as well. The custom heart-rate sensor uses infrared lights and photosensors to read the user's heart rate <ref>https://www.dailymail.co.uk/sciencetech/article-3030492/Take-peek-inside-Apple-Watch-Infographic-reveals-firm-packs-components-needed-power-wearable.html</ref>. This provides users with information on their daily steps, heart rate capabilities, and hours of sleep.  
  
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=== Advancements In Algorithms ===
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== Software Capabilities ==
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In the years following Alan Turing’s contributions, computer algorithms increased in magnitude and complexity. Advanced algorithms, such as artificial intelligence is defined as utilizing machine learning capabilities.<ref>Anyoha, Rockwell (August 28, 2017). [http://sitn.hms.harvard.edu/flash/2017/history-artificial-intelligence/ "The History of Artificial Intelligence"]. ''Science in the News''. Harvard. Retrieved April 28, 2019.</ref> This level of algorithmic improvement provided the foundation for more technological advancement.
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Apple watchOS is the software that Apple Watches run on, and is based on iOS. watchOS was released along with the launch of the first Apple Watch in 2015 <ref>https://www.lifewire.com/what-is-watchos-4690550</ref>. Since 2015, Apple has launched watchOS 1, watchOS 2, watchOS 3, watchOS 4, watchOS5, watchOS 6, watchOS 7, and watchOS 8. Each watchOS update offered a variety of new features and capabilities to the watch user. For example, the latest Apple Watch software update, watchOS 8, includes new features such as an updated interface of its wellness app, and updated fall detection algorithms <ref>https://www.apple.com/watchos/watchos-8/</ref>.
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[[File:machineLearning.png|500px]]
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=== Communication features  ===
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Through the evolution of the watchOS versions, Apple Watch is able to offer an increasing variety of features to its users, allowing them to engage with the watch in their everyday lives. The Apple Watch allows users to see who is calling or texting them at any given time, given that the watch is properly connected to wifi, their nearby iPhone, or is equipped with GPS capabilities. If the Apple Watch model is equipped with GPS capabilities, and the watch has been connected to a wireless plan with a phone provider, the Apple Watch does not need to be near ones phone in order to send and receive calls and text messages. If an Apple Watch does not have cellular connectivity, it will use bluetooth technology to connect to the users nearby iPhone (up to 12 feet away) or through wifi. The "walkie-talkie" feature allows users to connect with other Apple Watch users; however, both Apple Watch users need to have a watchOS 5 operating system or later. <ref>https://learning-oreilly-com.proxy.lib.umich.edu/library/view/apple-watch-for/9781119558637/c05.xhtml#h2-3</ref> In order to use the Walkie-Talkie features, users must send and accept friend invitations through the "Walkie-Talkie app," and then use the touch and hold button to speak directly to other users like a traditional "Walkie-Talkie."
  
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The machine learning process shown above describes how machine learning algorithms can provide more features and functionality to artificial intelligence.
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=== Fitness Tracking ===
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[[File:appleWatchRings.jpeg|200px|thumb|right|Apple Watch Rings, as Screen shotted from the Author's Own Watch]]
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The Apple watch offers fitness tracking in the form of Activity Rings which is broken down into three colored rings. The first is the red Move ring which allows users to track how many calories they have burned by moving throughout a 24 hour period. The second is the green Exercise ring that allows users to track the minutes of brisk activity they have completed that day. Finally, the blue Stand ring allows users to track how many hours they have stood in a 24 hour period. <ref>https://learning-oreilly-com.proxy.lib.umich.edu/library/view/apple-watch-for/9781119658665/c08.xhtml#h2-6</ref> The user can define their activity goals for each type of ring, and the ring visually closes once the user achieves that goal.
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The visualization shown to the right demonstrates the different types of rings, as well as the users progress in completing the goals associated with each ring.  
  
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== Classifications ==
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== Apple Watch Models ==
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There are many different classifications of algorithms, some are more well-suited for particular families of computational problems than others. In many cases, the algorithm one chooses to make for a given problem will have tradeoffs dealing with time complexity and memory usage.
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Since its release in 2015, Apple has released multiple other models which provide different features to the user including Apple Watch Series 1, Apple Watch Series 2, Apple Watch Series 3, Apple Watch Series 4, Apple Watch Series 5, Apple Watch SE, Apple Watch Series 6, and Apple Watch Series 7. The price of an Apple Watch can vary greatly, depending on the model and upgrades that the user would like to purchase. An Apple Watch can be bought through the Apple website starting at $199 for the Series 3 model, and $399 for the Series 7 model <ref>https://www.apple.com/shop/buy-watch/apple-watch</ref>. The larger displays (45mm) cost users more compared to the smaller screen displays (41mm). There are also other offered upgrades that will affect the price of the watch.
  
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==== Apple Watch Series 1 ====
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==== Apple Watch Series 7 ====
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The Apple Watch has evolved over time, offering more high-tech features to its users. The newest Apple Watch, Apple Watch Series 7, offers the most high-technology features to its users through its newest watch software, watchOS8 <ref> https://support.apple.com/guide/watch/whats-new-apdb93ea3872/8.0/watchos/8.0 </ref>. In comparing to the first model of the Apple Watch, released in 2015, the Series 1 Apple Watch only had a 8GB capacity, the Series 7 Apple Watch has a 32 GB capacity. Other notable upgrades that the Series 7 Apple Watch offers over the Series 1 Watch includes: 50 percent more screen area, GPS and GPS cellular models, water resistance (up to 50 meters).<ref>https://www.apple.com/watch/compare/</ref> The Apple Watch Series 7 is marketed as having the largest and most advanced display, being the most durable and fast charging <ref> https://support.apple.com/guide/watch/whats-new-apdb93ea3872/8.0/watchos/8.0 </ref>. The newest software offers a few new features to users such as the ability to add vaccination records in user's health app as well as allowing users to better control their home smart devices through the home app <ref>https://support.apple.com/guide/watch/whats-new-apdb93ea3872/8.0/watchos/8.0</ref>.
  
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A [https://en.wikipedia.org/wiki/Recursion_(computer_science) recursive algorithm] is an algorithm that calls itself with decreasing values in order to reach a pre-defined base case solution. The base case solution determines the values that are sent back up the recursive stack to determine the final outcome of the algorithm. It follows the principle of solving subproblems to solve the larger problem since once the base case solution is reached, the algorithm works upwards to fit the solution into the larger subproblem. The base case must be present, otherwise the recursive function will never stop calling itself, creating an infinite loop. Since recursion involves numerous function calls, it is one of the main sources of stack overflow. With recursive calls, programs have to save more stacks despite a lack of available space. Further, some recursive functions require additional computations even after the recursive call, adding to the consumption speed and memory. 'Tail Recursive' functions are an efficient solution to this, wherein recursive calls happen at the very end of the function, allowing only one stack to be saved throughout the function calls.
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==== Additional Collaborations ====
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Apple partnered with Nike+ to produce an apple watch for those who are passionate about fitness and the Nike brand. This watch was designed to synchronize with the "Nike Run Club" and "Nike Training Club" apps <ref>https://www.apple.com/newsroom/2016/09/apple-nike-launch-apple-watch-nike/</ref>. Apple partnered with Hermès as well, to create an Apple Watch that emphasized bold, colorful leather bands and exclusive new watch faces for users. <ref>https://learning-oreilly-com.proxy.lib.umich.edu/library/view/apple-watch-for/9781119558637/c01.xhtml#h2-2</ref>
  
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Due to the recurring memory stack frames that are created with each call, recursive algorithms generally require more memory and computation power. However, they are still viewed as simplistic and succinct ways to write elaborate algorithms.
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== Public Perception ==
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The Apple Watch was first introduced to the public in 2015. The International Data corporation (IDC) highlights that the total sales amount of Apple increased in 2015 <ref>https://repositorio.iscte-iul.pt/handle/10071/14630</ref>. One of the main critiques for the Apple watch upon its debut included its inability to differentiate from similar products, such as the Fitbit. <br />
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==== Apple Watch Series 2 ====
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Fitbit is a brand that offers a lineup of smartwatches that allow users to track personal health data including daily activity, sleep metrics, and other personalized health metrics such as heart rate variability. <ref>https://www.fitbit.com/global/us/products/smartwatches</ref> This smartwatch was released in 2009 <ref>https://www.fitbit.com/global/us/about-us</ref>, and Fitbit's early entry to this untapped market has caused it to be popular to millions of Fitbit devices to be sold worldwide. Yet, while Fitbit was one of the first companies to create a smartwatch with the intent of collecting personalized health data, the Apple Watch has become a more popular smartwatch among users. Specifically, Apple sold 33.9 million watches in 2020, compared to 5.9 million Fitbit watches sold in the same year <ref>https://www.statista.com/chart/15035/worldwide-smartwatch-shipments/#:~:text=According%20to%20estimates%20from%20Counterpoint,leader%20in%20the%20smartwatch%20market</ref>.  
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A [https://www.apple.com/watch/compare/] Words
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==== Apple Watch Series 3 ====
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[https://www.apple.com/apple-watch-series-3/] However, they vary in that they are capable of using a multiple-valued function whose values are the positive integers less than or equal to itself. In addition, all points of termination are labeled as successes or failures. The terminology "non-deterministic" does not imply randomness, of rather a kind of free will<ref>"
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Floyd, Robert W. (November 1996) ''Non-Deterministic Algorithms''. Carnegie Institute of Technology. pp. 1–17."</ref>
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==== Apple Watch Series 4 ====
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==== Apple Watch Series 5 ====
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A brute force algorithm is the most "naive" approach one can take in attempting to solve a particular problem. A solution is reached by searching through every single possible outcome before arriving at an answer. In terms of complexity or Big-O notation, brute force algorithms typically represent the highest order complexity compared to other potential solutions for a given problem. While brute force algorithms may not be considered the most efficient option for solving computational problems, they do offer reliability as well as a guarantee that a solution to a given problem will eventually be found.
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An example of a brute force algorithm would be trying all combinations of a 4-digit passcode, in order to crack into a target's smartphone.
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==== Apple Watch SE ====
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A divide and conquer algorithm divides a problem into smaller sub-problems then conquer each smaller problems before merging them together to solve the original problem. In terms of efficiency and the Big-O notation, the Divide and Conquer fares better than Brute Force but is still relatively inefficient compared to other more complex algorithms. An example of divide and conquer is merge sort wherein a list is split into smaller sorted lists and then merged together to sort the original list.
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Examples of Divide and Conquer algorithms would be the sorting algorithm [https://en.wikipedia.org/wiki/Merge_sort Merge Sort], and the searching algorithm [https://en.wikipedia.org/wiki/Binary_search_algorithm Binary Search]<ref>[https://www.geeksforgeeks.org/divide-and-conquer-algorithm-introduction "Divide and Conquer Algorithms"]. ''Geeks for Geeks''. Retrieved April 28, 2019.</ref>.
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==== Apple Watch 6 ====
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[https://.wikipedia.org/wiki/Dynamic_programming Dynamic programming] takes advantage of overlapping subproblems to more efficiently solve a larger computational problem. The algorithm first solves less complex subproblems and stores their solutions in memory. Then more complex problems will find these solutions using some method of lookup to find the solution and implement it in the more complex problem to find its own solution. The method of lookup enables solutions to be computed once and used multiple times. This method reduces the time complexity from exponential to polynomial.
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An example of a common problem that can be solved by Dynamic Programming is the [https://en.wikipedia.org/wiki/Knapsack_problem#Dynamic_programming_in-advance_algorithm 0-1 Knapsack Problem].
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==== Apple Watch 7 ====
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A backtracking algorithm is similar to brute force with the exception that as soon as it reaches a node where a solution could never be reached from said node on, it prunes all the subsequent node and backtracks to the closest node that has the possibility to be right. Pruning in this context means neglecting the failed branch as a potential solution branch in all further searches, reducing the scope of the possible solution set and eventually guiding the program to the right outcome.
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An example of some problems that can be solved by algorithms that take advantage of backtracking is solving [[Wikipedia:Sudoku|Sudoko]], or the [[Wikipedia:Eight_queens_puzzle|N-Queens Problem]].
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== Ethical Considerations ==
 
== Ethical Considerations ==
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[[File:Big-O.jpeg|300px|thumbnail|right|Complexity Graph]]
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The data collection abilities of the Apple Watch have lead to concerns about the privacy and security of user's data. Further, concerns about the potential reliance on health related data as a replacement for professional medical care has been of rising concern. Additionally, there has been rising concern over the lack of security related to Internet of Things devices, such as the Apple Watch. There are concerns that the Apple Watch furthers systems of oppression towards underprivileged populations.
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Measuring the efficiency of an algorithm is standardized by checking how well it grows with more inputs. Computer scientists calculate how much computational time increases with an increasing number of inputs. Since this form of measurement merely intends to see how well an algorithm grows, the constants are left out since with high inputs these constants are negligible anyways. Big-O notation specifically describes the worst-case scenario and measures the time or space the algorithm uses<ref name = 'Big-O'>[ Bell, Rob. [https://rob-bell.net/2009/06/a-beginners-guide-to-big-o-notation/ "A Beginner's Guide to Big O Notation"]. Retrieved April 28, 2019.</ref>. Big-O notation can be broken down into order of growth algorithms such as O(1), O(N), and O(log N), with each notation representing different orders of growth. The later, log algorithms -commonly referred to as logarithms, are  bit more complex than the rest, log algorithms take a median from a data set and compare it to a target value, the algorithm continues to halve the data as long as the median is higher or lower than the target value<ref name='Big-O'></ref>. An algorithm with a higher Big-O is less efficient at large scales, for example in general a O(N) algorithm will run slower than a O(1) algorithm, and this difference will be more and more apparent, the larger the number of inputs.
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== Artificial Intelligence Algorithms ==
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=== Clustering ===
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Clustering is a [https://en.wikipedia.org/wiki/Machine_learning Machine Learning] technique in which, data is segregated into groups called clusters through an algorithm, given a set of data points. These clustering algorithms classify the data based on various criteria, but the fundamental premise is that data points with similarities will belong in the same group, that must be dissimilar to other groups. There are numerous clustering algorithms including K-Means clustering, Mean-Shift Clustering, DBSCAN (Density-Based Spatial Clustering of Applications with Noise) Clustering, EM (Expectation Maximization) Clustering, Agglomerative Hierarchical Clustering. <ref name="Clustering">Seif, George (February 5, 2018). [https://towardsdatascience.com/the-5-clustering-algorithms-data-scientists-need-to-know-a36d136ef68 "The 5 Clustering Algorithms Data Scientists Need To Know"]. Retrieved April 28, 2019.</ref>
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==== K-Means Clustering ====
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K-means clustering is the most widely known and used algorithm out of all the clustering algorithms. It involves pre-determining a target number - ''k'', which represents the number of centroids needed in the dataset. A centroid refers to the predicted center of the cluster. It then identifies the data points nearest to the center to form each cluster, while keeping ''k'' as small as possible. <ref>Garbade, Michael J. (September 12, 2018). [https://towardsdatascience.com/understanding-k-means-clustering-in-machine-learning-6a6e67336aa1 "Understanding K-Means Clustering in Machine Learning"]. ''Towards Data Science''. Retrieved April 28, 2019.</ref> K-Means clustering is considered to be a fast algorithm, due to the minimal computations it requires. It has a Big-O complexity of O(''n''). <ref name="Clustering"></ref>
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==== Mean-Shift Clustering ====
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Mean-Shift Clustering, also known as Mode-Seeking, is an algorithm where datapoints are grouped into clusters, through the process of iteratively shifting all the points towards their mode. The mode of a dataset is defined as the most occurring value in that particular dataset, or in graphical terms, the point where the density of data points is the highest. Therefore, the algorithm moves, or "shifts" each point closer to its closest centroid, the direction of which is determined by the density of the nearby points. Therefore, each iteration of the program moves each point closer to where all the other points are, eventually forming a cluster center. The key difference between Mean-Shift and K-Means clustering is that K-Means requires the number ''k'' to be set beforehand, whereas the Mean-Shift algorithm creates clusters on the go without necessarily determining how many will be formed. <ref>[http://www.chioka.in/meanshift-algorithm-for-the-rest-of-us-python/ "Meanshift Algorithm for the Rest of Us (Python)"], May 14, 2016. Retrieved April 28, 2019.</ref> Usually, the Big-O complexity of such an algorithm is O(''Tn^2''), where ''T'' refers to the number of iterations in the algorithm. <ref>Thirumuruganathan, Saravanan (April 1, 2010). [https://saravananthirumuruganathan.wordpress.com/2010/04/01/introduction-to-mean-shift-algorithm/ "Introduction To Mean Shift Algorithm"]. Retrieved April 28, 2019.</ref>
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==== DBSCAN Clustering ====
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DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is an algorithm that groups together nearby datapoints based on a measure of distance, often [https://en.wikipedia.org/wiki/Euclidean_distance Euclidean distance] and a minimal number of points. The algorithm also differentiates outliers if they are in low density areas. The algorithm requires two parameters - ''eps'' and ''minPoints''. The decision on what to set these parameters as depends from dataset to dataset, and requires a fundamental understanding of the context of the dataset being used. The ''eps'' should be picked based on the dataset distance and while generally small ''eps'' values are desirable, if the set value is too small there is a danger that a portion of the data will go unclustered. Conversely, if the value set is too large, too many of the points might get grouped into the same cluster. The minPoints parameter is usually derived from the parameter ''D'', following that minPoints ≥ ''D + 1'' where ''D'' measures the number of dimensions in the data. <ref>Salton do Prado, Kelvin (April 1, 2017). [https://towardsdatascience.com/how-dbscan-works-and-why-should-i-use-it-443b4a191c80 "How DBSCAN works and why should we use it?"]. ''Towards Data Science''. Retrieved April 28, 2019.</ref> The average run-time complexity of a DBSCAN algorithm is O(''n log n'') whereas it's worst-case complexity can be O(''n^2'').
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==== EM Clustering ====
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Expectation Maximization or EM Clustering is similar to the K-Means clustering technique. The EM Clustering method takes forward the basic principles of K-Means Clustering in two primary ways:
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1) The EM Clustering method aims to calculate what datapoints belong in what cluster through one or more probability distributions, instead of trying to simply calculate and maximize the difference in mean points.
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2) The overall purpose of the algorithm is to maximize the chance or the likelihood of belonging to a cluster in the data.
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Essentially, the EM clustering method approximates the distribution of each point based on different probability distributions and at the end of it, each observation has a certain level of probability of belonging to a particular cluster. Ultimately, the resulting clusters are analyzed based on which clusters have the highest classification probabilities. <ref>[https://docs.rapidminer.com/latest/studio/operators/modeling/segmentation/expectation_maximization_clustering.html "Expectation Maximization Clustering"] ''RapidMiner Documentation''. Retrieved April 28, 2019.</ref>
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==== Agglomerative Hierarchical Clustering ====
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Also known as AGNES (Agglomerative Nesting), the Agglomerative Hierarchical Clustering Technique also creates clusters based on similarity. To start, this method takes each object and treats it as if it were a cluster. It then merges each of these clusters in pairs, until one huge cluster consisting of all the individual clusters has been formed. The result is represented in the form of a tree, which is called a ''dendrogram''. The manner in which the algorithm works is called the "bottom-up" technique. Each data entry is considered an individual element or a leaf node. At each following stage, the element is joined with its closest or most-similar element to form a bigger element or parent node. The process is repeated over and over until the root node is formed, with all of the subsequent nodes under it.
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The opposite of this technique is through the "top-down" method, which is implemented in an algorithm called "Divisive Clustering". This method starts at the root node, and at each iteration nodes are split or "divided" into two separate nodes, based off the ranking of dissimilarity within the clusters. This is done until every node has been divided, leaving individual clusters or leaf nodes. <ref>[https://www.datanovia.com/en/lessons/agglomerative-hierarchical-clustering/ "HIERARCHICAL CLUSTERING IN R: THE ESSENTIALS/Agglomerative Hierarchical Clustering"]. ''DataNovia''. Retrieved April 28, 2019.</ref>
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==== Deep Learning and Neural Networks ====
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Neural networks are a collection of algorithms that utilize many of the concepts mentioned above while taking their capabilities a step further through deep learning. On a high level, the purpose of a neural network is to interpret raw input data through a machine perception and return patterns in the data, through techniques above such as K-means clustering or Random Forests. To do so, a neural network requires datasets to train on and thus models its interpretations on the training sets in a machine learning process. Where neural networks differ is its ability to be “stacked” to engage in deep learning. Each process is held in nodes that can be likened to neurons in a human brain. When data is encountered, many separate computations occur that can be weighted to produce the desired output. How many “layers”,  or the depth, of a neural network increases its capabilities and complexity multiplicatively. <ref> A Beginner's Guide to Neural Networks and Deep Learning. (n.d.). Retrieved April 27, 2019, from https://skymind.ai/wiki/neural-network </ref>
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== Ethical Dilemmas ==
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With the relevance of algorithms as well as their sheer magnitude, ethical dilemmas were bound to arise. Potential ethical issues related to algorithms and computer science include issues of privacy, data gathering, and bias.
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=== Bias ===
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Given that people are the creators of algorithms, code can inherit bias from its coder or its initial source data. 
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====Joy Buolamwini and Facial Recognition====
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Joy Buolamwini, a graduate computer science student at MIT, experienced a case of this. The facial recognition software she was working on failed to detect her face, as she had a skin tone that had not been accounted for in the facial recognition algorithm. This is because the software had used machine learning with a dataset that was not diverse enough, and as a result, the algorithm failed to recognize her.<ref>Buolamwini, Joy. [www.media.mit.edu/posts/how-i-m-fighting-bias-in-algorithms/ "How I'm Fighting Bias in Algorithms"]. ''MIT Media Lab''. Retrieved April 28, 2019.</ref> Safiya Noble discusses instances of algorithmic search engines reinforcing racism in her book, "Algorithms of Oppression".<ref>Noble, Safiya. ''Algorithms of Oppression''.</ref> Bias like this occurs in countless algorithms, be it through insufficient machine learning data sets, or the algorithm developers own fault, among other reasons, and it has the potential to cause legitimate problems even outside the realm of ethics.
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====Bias in Criminalization====
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[https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing COMPAS] is algorithm written to determine whether a criminal is likely to re-offend using information like age, gender, and previously committed crimes. Tests have found it to be more likely to incorrectly evaluate black people than white people because it has learned on historical criminal data, which has been influenced by our biased policing practices.<ref>Larson, Mattu; Kirchner, Angwin (May 23, 2016). [https://www.propublica.org/article/how-we-analyzed-the-compas-recidivism-algorithm "How We Analyzed the COMPAS Recidivism Algorithm"]. ''Propublica''. Retrieved April 28, 2019.</ref>
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Jerry Kaplan is a research affiliate at Stanford University’s Center on Democracy, Development and the Rule of Law at the Freeman Spogli Institute for International Studies, where he teaches “Social and Economic Impact of Artificial Intelligence.” According to Kaplan, algorithmic bias can even influence whether or not a person is sent to jail. A 2016 study conducted by ProPublica indicated that software designed to predict the likelihood an arrestee will re-offend incorrectly flagged black defendants twice as frequently as white defendants in a decision-support system widely used by judges. Ideally, predictive systems should be wholly impartial and therefore be agnostic to skin color. However, surprisingly, the program cannot give black and white defendants who are otherwise identical the same risk score, and simultaneously match the actual recidivism rates for these two groups. This is because black defendants are re-arrested at higher rates that their white counterparts (52% versus 39%), at least in part due to racial profiling, inequities in enforcement, and harsher treatment of black people within the justice system. <ref>Kaplan, J. (December 17, 2018). [https://www.washingtonpost.com/opinions/2018/12/17/why-your-ai-might-be-racist/ "Why your AI might be racist"]. ''Washington Post''. Retrieved April 28, 2019.</ref>
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==== Job Applicants ====
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Many companies employ complex algorithms in order to review and sift the thousands of resumes they will receive each year. Sometimes these algorithms display a bias which can result in people with a specific racial background or gender being recommended over others. An example of this was an Amazon AI algorithm that preferred men over women in recommending people for an interview. The algorithm employed machine learning techniques and over time taught itself to prefer men over women due to a variety of factors <ref>Dastin, Jeffrey. “Amazon Scraps Secret AI Recruiting Tool That Showed Bias against Women.” Reuters, Thomson Reuters, 9 Oct. 2018, af.reuters.com/.</ref>. A major problem facing machine learning algorithms is the unpredictability in their models and what they will begin to teach themselves. It was apparent Amazon engineers did not intend for their algorithm to be bias towards men but an error resulted in this happening. Additionally, this was not a quick fix as the algorithm had been in place for years and began to pick up this bias and after analysis after a period time, it was discovered.
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=== Privacy And Data Gathering ===
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The ethical issue of privacy is also highly relevant to the concept of algorithms. Information transparency <ref>Turilli, Matteo, and Luciano Floridi (2009). "The Ethics of Information Transparency." ''Ethics and Information Technology''. '''11'''(2): 105-112. doi:10.1007/s10676-009-9187-9.</ref> is an import point regarding these issues. In popular social media algorithms, user information is often probed without the knowledge of the individual, and this can lead to problems. It is often not transparent enough how these algorithms receive user data, resulting in often incorrect information which can affect both how a person is treated within social media, as well as how outside agents could view these individuals given false data. Algorithms can also often infringe on a user’s feelings of privacy, as data can be collected that a person would prefer to be private. Data brokers are in the business of collecting peoples in formation and selling it to anyone for a profit, like data brokers companies often have their own collection of data. In 2013, Yahoo was hacked, leading to the leak of data pertaining to approximately three billion users.<ref>Griffin, Andrew (October 4, 2017) [http://www.independent.co.uk/life-style/gadgets-and-tech/news/yahoo-hack-details-personal-information-was-i-compromised-affected-leak-a7981671.html "Yahoo Admits It Accidentally Leaked the Personal Details of Half the People on Earth."] ''The Independent''. Retrieved April 28, 2019.</ref> The information leaked contained data relating to usernames, passwords, as well as dates of birth. Privacy and data gathering are common ethical dilemmas relating to algorithms and are often not considered thoroughly enough by algorithm’s users.
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===The Filter Bubble===
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[[File:Filterbubble.png|400px|thumbnail|Personalized, Online Filter Bubbles]]
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Algorithms can be used to filter results in order to prioritize items that the user might be interested in. On some platforms, like Amazon, people can find this filtering useful because of the useful shopping recommendations the algorithm can provide. However, in other scenarios, this algorithmic filtering can become a problem. For example, Facebook has an algorithm that re-orders the user's news feed. For a period of time, the technology company prioritized sponsored posts in their algorithm. This often prioritized news articles, but there was no certainty on whether these articles came from a reliable source, simply the fact that they were sponsored. Facebook also uses its technology to gather information about its users, like which political party they belong to. This combined with prioritizing news can create a Facebook feed filled with only one party's perspective. This phenomenon is called the filter bubble, which essentially creates a platform centered completely around its user's interests.
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Many, like Eli Pariser, have questioned the ethical implications of the filter bubble. Pariser believes that filter bubbles are a problem because they prevent users from seeing perspectives that might challenge their own. Even worse, Pariser emphasizes that this filter bubble is invisible, meaning that the people in it do not realize that they are in it. <ref>Pariser, Eli. (2012). ''The Filter Bubble''. Penguin Books.</ref> This creates a huge lack of awareness in the world, allowing people to stand by often uninformed opinions and creating separation, instead of collaboration, with users who have different beliefs. Because of the issues Pariser outlined, Facebook decided to change their algorithm in order to prioritize posts from friends and family, in hopes of eliminating the effects of the potential filter bubble.
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====Filter Bubble in Politics====
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Another issue that these Filter Bubbles create are echo chambers; Facebook, in particular, filters out [political] content that one might disagree with, or simply not enjoy <ref>Knight, Megan (November 30, 2018). [http://theconversation.com/explainer-how-facebook-has-become-the-worlds-largest-echo-chamber-91024 "Explainer: How Facebook Has Become the World's Largest Echo Chamber"]. ''The Conversation''. Retrieved April 28, 2019.</ref>. The more a user "likes" a particular type of content, similar content will continue to appear, and perhaps content that is even more extreme. This was clearly seen in the 2016 election when without realizing it, voters developed tunnel vision. Rarely did their Facebook comfort zones expose them to opposing views, and as a result they eventually became victims to their own biases and the biases embedded within the algorithms.<ref>El-Bermawy, Mostafa M. (June 3, 2017). [https://www.wired.com/2016/11/filter-bubble-destroying-democracy/ "Your Filter Bubble Is Destroying Democracy"]. ''Wired''. Retrieved April 28, 2019.</ref> Later studies produced visualizations to show how insular the country was at the time of the election on social media and the large divide between the two echo chambers with almost no ties to each other.<ref>MIS2: Misinformation and Misbehavior Mining on the Web - Scientific Figure on ResearchGate. Available from: https://www.researchgate.net/figure/Social-media-platforms-can-produce-echo-chambers-which-lead-to-polarization-and-can_fig4_322971747 [accessed 23 Apr, 2019]</ref> 
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[[File:Socal-med-polarizing.png|thumbnail|right|From [https://www.researchgate.net/figure/Social-media-platforms-can-produce-echo-chambers-which-lead-to-polarization-and-can_fig4_322971747 Research Gate]: An example of algorithmic echo chambers that contribute to the polarization of political beliefs]]
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===Corrupt Personalization===
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Algorithms have the potential to become dangerous, with their most serious repercussions being the threat to democracy that is extensive personalization. Algorithms such as Facebook's are corrupt in the practice of "like recycling" that they partake in. In Christian Sandvig's article title ''Corrupt Personalization,'' he notes that Facebook has defined a "like" in two ways that the users do not realize. The first is that "anyone who clicks on a "like" button is considered to have "liked" all future content from that source," and the second is that "anyone who "likes" a comment on a shared link is considered to "like" wherever that link points to" <ref>Sandvig, Christian. “Corrupt Personalization.” Social Media Collective, 27 June 2014, socialmediacollective.org/2014/06/26/corrupt-personalization/</ref>. As a result, posts that you "like" wind up becoming ads on your friends' pages claiming that you like a certain item or thing. You are not able to see these posts and, because they do not appear on your news feed, you do not have the power to delete them. This becomes a threat to one's autonomy, for even if they wanted to delete this post, they can not. Furthermore, everyone is entitled to the ability to manage the public presentation of their own self-identity, and in this corrupt personalization, Facebook is giving users new aspects of their identity that may or may not be accurate.
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=== Agency And Accountability ===
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Algorithms make "decisions" based on the steps they were designed to follow and the input they received. This can often lead to algorithms as autonomous agents<ref>“Autonomous Agent.” Autonomous Agent - an Overview | ScienceDirect Topics, www.sciencedirect.com/topics/computer-science/autonomous-agent.</ref>, taking decision making responsibilities out of the hands of real people. Useful in terms of efficiency, these autonomous agents are capable of making decisions in a greater frequency than humans. Efficiency is what materializes the baseline for algorithm use in the first place.
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From an ethical standpoint, this type of agency raises many complications, specifically regarding accountability. It is no secret that many aspects of life are run by algorithms. Even events like applying to jobs are often drastically effected by these processes. Information like age, race, status, along with other qualifications, are all fed to algorithms, which then take agency and decide who moves further along in the hiring process and who is left behind. Disregarding inherent biases in this specific scenario, this process still serves to reduce the input of real humans and decrease the number of decisions that they have to make, and what is left over is the fact that autonomous agents are making systematic decisions that have extraordinary impact on people's lives. While the results of the previous example may only culminate to the occasional disregard of a qualified applicant or resentful feelings, this same principle can be much more influential.
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====The Trolley Problem in Practice====
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=== Data Privacy ===
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Consider autonomous vehicles, or self-driving cars, for instance. These are highly advanced algorithms that are programmed to make split second decisions with the greatest possible accuracy. In the case of the well-known "Trolley Problem"<ref>Roff, Heather M. “The Folly of Trolleys: Ethical Challenges and Autonomous Vehicles.” Brookings, Brookings, 17 Dec. 2018, www.brookings.edu/research/the-folly-of-trolleys-ethical-challenges-and-autonomous-vehicles/.</ref>, these agents are forced to make a decision jeopardizing one party or another. This decision can easily conclude in the injury or even death of individuals, all at the discretion of a mere program.  
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Big data has emerged in recent years, and is of increasing interest to researchers and organizations anticipating an overwhelming flow of data. The individual privacy surrounding this data is of increasing concern. Data privacy is defined as one's control over access to their own personal information, specifically the right to know what personal information is being dispersed to which people <ref>https://timreview.ca/article/1067</ref>. It is therefore important that users understand the security risks of big data, and understand how companies are protecting and handling user data. Given that the Apple Watch collects multiple types of personal data about the user, the discussion of big data privacy concerns have been relevant to this device. <br />
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Privacy legislations are important for prioritizing individual privacy protections, and valuing personal information control, despite business and commercial enterprises' desire to collect data. The access to personal data about users is valuable for companies as they are able to profit from users by customizing their marketing and advertising to the user's preferences. The United States specifically passed the first legislation on information privacy more than 40 years ago through the 1974 US Privacy Act, which developed values related to individual data privacy <ref>https://timreview.ca/article/1067</ref>. This legislation is important for monitoring how companies, like Apple, utilize the big data that is collected from devices like the Apple Watch. Given that the Apple Watch is a device that is intended to connect with other devices, the evolution of the internet of Things has complicated the subject of data privacy. Namely, it is of increasing concern that while data privacy legislation exists, the internet of Things complicates the application of the current legislation. Researchers stress that there are risks of privacy loss and exploitation from internet of Things devices, penalizing owners for accessing the advantages of an increasingly connected world <ref>https://timreview.ca/article/1067</ref>. Given that the Apple Watch is a part of the internet of Things, this is an increasing ethical concern with the device.
  
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The issue of accountability is then raised, in a situation such as this, because in the eyes of the law, society, and ethical observers, there must be someone held responsible. Attempting to prosecute a program would not be feasible in a legal situation, due to not being able to have a physical representation of the program, like you would a person. However, there are those such as Frances Grodzinsky and Kirsten Martin <ref>Martin, Kirsten. “Ethical Implications and Accountability of Algorithms.” SpringerLink, Springer Netherlands, 7 June 2018, link.springer.com/article/10.1007/s10551-018-3921-3.</ref>, who believe that the designers of an artificial agent, should be responsible for the actions of the program. <ref name="Grodzinsky"> "The ethics of designing artificial agents" by Frances S. Grodzinsky et al, Springer, 2008.</ref> Others contend this point by saying that the blame should be attributed to the users or persons directly involved in the situation.  
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=== Health Related Data ===
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Smartwatches, such as the Apple Watch are equipped with features that allow the user to track health related data. In the medical field, the principle of non maleficence requires that medical actions are weighed against risks, benefits, and consequences <ref>https://pubmed.ncbi.nlm.nih.gov/30916041/#:~:text=The%20principle%20of%20nonmaleficence%20requires,level%20of%20competence%20and%20training</ref>. Researchers have argued that the accessibility of a user's own health data has been problematic as it violates the principle of non-maleficence.
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For example, ECG monitoring on Apple watches which allows users to screen for atrial fibrillation, with the intent of early detection. Early detection of this arrhythmia can prevent adverse events such as stroke, by treating with anticoagulants. Although the Apple Watch has an algorithm with a very high specificity, many people have still been notified of arrhythmia without having atrial fibrillation, leading to a false positive result <ref>https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7843431/</ref>. If the patient is not aware of the potential benefits, the risks and the possible consequences of atrial fibrillation detection, the diagnostic can be done at the user's risk. The Apple Watch does not provide any information comparable to traditional medical education, leading the patient to not be medically well-informed, according to researchers. Further, researchers argue that patients could overestimate the accuracy of the diagnostic capabilities of the watch and the over reliance on home monitoring of atrial fibrillation could lead to fewer face-to-face doctor visits. Researchers conclude that the over-reliance on smartwatches and the lower number of face-to-face doctor visits lead to a violation of the principle of non-maleficence. <ref>https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7843431/</ref>.<br />
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The American Journal of Medicine highlights the limitations of the health benefits that the Apple Watch can provide through its personalized health data. While wearable devices are designed to promote healthy behaviors and decrease risk for chronic diseases, current literature indicates that wearable devices provide little benefit on chronic disease health outcomes. While the Apple Watch uses marketing techniques to boost a product that will lead to a healthier lifestyle, research has found that while wearable devices can motivate and accelerate physical activity, data does not suggest consistent health benefits <ref>https://www.amjmed.com/article/S0002-9343(19)30553-4/pdf</ref>. Ethically, scholars fear that the advertising of the product can be misleading to customers with chronic diseases, as they overestimate the value that the product can provide by offering personalized health data.  
  
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These complications will continue to arise, especially as algorithms continue to make autonomous decisions at grander scales and rates. Determining responsibility for the decisions these agents make will continue to be a vexing process, and will no doubt shape in some form many of the advanced algorithms that will be developed in the coming years.
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=== Internet of Things ===
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The internet of Things (IoT) covers objects and devices used by a user that are connected to the internet. Connected Devices have the ability to collect sensitive information on users and their daily activity <ref>https://www.commonsense.org/education/articles/privacy-and-security-evaluation-of-the-apple-watch</ref>. While the Internet of Things enhances human comfort and convenience, it has raised questions amongst academics about security and privacy.
  
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=== Intentions and Consequences ===
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==== Internet of Things & Security ====
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The ethical consequences that are common in algorithm implementations can be either deliberate or unintentional. Instances where an algorithm's intent and outcome differs is noted below.  
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Researchers have uncovered that since Internet of Things technology was not designed with security in mind, as it was designed to provide novel features while minimizing device cost and size, the devices have limited hardware resources. This means that security tools cannot be installed in internet of Things devices, which makes it an easy target for cyber crimes. There are 3 typical ways that an agent can threaten IoT systems: 1. stealing information (ie.user credentials) 2. tracking user information (ie. location) and 3. taking control of a system (ie. through malware) <ref>https://web-s-ebscohost-com.proxy.lib.umich.edu/ehost/detail/detail?vid=0&sid=cbf01992-81a8-48e6-af22-021502434a67%40redis&bdata=JnNpdGU9ZWhvc3QtbGl2ZSZzY29wZT1zaXRl#AN=153038918&db=a9h</ref>. A single Internet of Things device can compromise other connected devices, leading the collection of compromised devices to be used to attack computing assets and services. This means that criminals can leverage the power of Internet of Things technology can be used to hack connected devices, such as pacemakers, and cause potential physical harm to others <ref>https://link-springer-com.proxy.lib.umich.edu/content/pdf/10.1007%2F978-3-319-99277-8.pdf</ref>. Considering that the Apple Watch is an internet of Things device, data security, is a topic of rising concern with the product as compromised devices can affect other devices within the internet of Things.
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At the same time, Internet of Things devices can be useful in criminal activities, by providing law enforcement with rich information. The interconnected nature of the devices means that experts are able to collect more data about criminals, if they are using a connected device <ref>https://link-springer-com.proxy.lib.umich.edu/content/pdf/10.1007%2F978-3-319-99277-8.pdf</ref>. While the data collected on users through the Apple Watch raises data privacy and security concerns, scholars point out that the product can be valuable in crime related investigations.  
  
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==== YouTube Radicalization ====
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=== Increasing Disparities  ===
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Scholar and technosociologist Zeynep Tufekci has claimed that "[[YouTube]] may be one of the most powerful radicalizing instruments of the 21st century."<ref name="YouTube Radicalization">[https://www.nytimes.com/2018/03/10/opinion/sunday/youtube-politics-radical.html] Tufekci, Zeynep. “YouTube, the Great Radicalizer.” The New York Times, The New York Times, 10 Mar. 2018, www.nytimes.com/2018/03/10/opinion/sunday/youtube-politics-radical.html.</ref> As YouTube algorithms aim maximize the amount of time that viewers spend watching, it inevitably discovered that the best way to do this was to show videos that slowly "up the stakes" of the subject being watched - from jogging to ultramarathons, from vegetarianism to veganism, from Trump speeches to white supremacist rants.<ref name="YouTube Radicalization" /> Thus, while the intention of Youtube is to keep viewers watching (and bring in advertisement money), they have unintentionally created a site that shows viewers more and more extreme content - contributing to radicalization. Such activity circles back to and produce filters and echo chambers.
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The Apple Watch may indirectly contribute to the perpetuation inequality amongst minority populations. Since smartwatches are notably expensive products, the purchase of this technology is not accessible to every socioeconomic and demographic population. The majority of smartwatches are bought by young people with higher socioeconomic status <ref>https://www.frontiersin.org/articles/10.3389/fcvm.2020.615927/full</ref>. Access to healthcare is lower for low-income populations, leading the inaccessibility of lower populations to gain access to health data through an Apple Watch to lead to further discrimination against people who cannot afford the watches <ref>https://www.nejm.org/doi/full/10.1056/NEJMoa1901183</ref>. Further, scholars have uncovered that the accuracy of algorithms for detecting certain health related conditions through the Apple Watch is not representative. Namely, the participants of the Apple heart study were on average 41 years old and 68% were white <ref>https://www.nejm.org/doi/full/10.1056/NEJMoa1901183</ref>. Therefore, the algorithms were not evaluated for a diverse group of users, causing there to be a lack of algorithmic accuracy for these populations.<br />
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Given the rising concerns about the disparities that Apple indirectly widens through its products, Apple has increasingly made commitments to diversity, equity, and inclusion. Specifically, Apple highlights that the number of employees from underrepresented communities (URCs) has increased by 64%, or over 18,000 people, and makes up nearly 50% of Apple’s U.S. workforce <ref>https://www.apple.com/diversity/</ref>.  
  
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==== Facebook Advertising ====
 
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By taking a closer look at Facebook's algorithm that serves up ads to its users, gender and racial bias is obviously prominent. Using demographic and racial background as factors, Facebook's decides which ads are served up to its users. A team from Northeastern tested to see the algorithm bias in action and by running identical ads with slight tweaks to budget, images, and headings, the ads reached vastly different audiences. Results such as minorities receiving a higher percentage of low-cost housing ads, and women receiving more ads for secretary and nursing jobs <ref>Hao, Karen. “Facebook's Ad-Serving Algorithm Discriminate by Gender and Race.” MIT Technology Review, 5 Apr. 2019, www.technologyreview.com/.</ref>. Although the intent of Facebook may be to reach people that these ads are intended for, the companies that are signing up to advertise with Facebook have stated they did not anticipate this type of filtering when paying for their services. Additionally, although Facebook may believe it is win-win for everyone since advertisers will be getting more interactions with certain audiences targeted, and people will be happy to see ads more relatable to them shown, it is incredibly discriminatory to target people based on factors that are uncontrollable. Facebook needs to adjust its targeted advertising practices by removing racial and gender factors in their algorithms in order to prevent perpetuating stereotypes and placing people in certain boxes by race and gender. This type of algorithm goes against many ethical principles and is important that powerful technology companies are not setting poor examples for others.
 
  
 
==See also==
 
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Latest revision as of 19:04, 11 February 2022

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Image of the Apple Watch Series 7, from AT&T website. [1]

Apple Watch is a line of smartwatches, or computers that users can wear around their wrists, from the technology company Apple[1] and allows users to perform a variety of tasks [2]. In order to function, the watch needs to be paired with other Apple products, specifically an iPhone5 model or later. [3]. The Apple Watch is equipped with software and hardware capabilities that gives it the ability to interact with the user in a variety of ways.
There are different Apple Watch models that users can choose from, which each offering a unique array of features to the users. In terms of the public perception of the Apple Watch, it has gained both negative and positive feedback from the public, but has overall been a popular product for Apple. In 2020 alone, Apple sold 33.9 million Apple Watches to users, making it a leader in the smartwatch industry over comparable brands like Huawei, Samsung, BBK, and fitbit. [4].
In looking at the ethical considerations of the product, the Apple Watch collects sensitive user data, including health related data. As a device that connects with other Apple products, there are security concerns related to the data that it collects in relation to being a internet of Things device. Further, there are rising concerns that the Apple Watch may intendedly lead to the perpetuation of inequalities in society [5].

Brief History

The Apple Watch was first released in 2015, with three distinct models; however, the genesis of the Apple Watch reportedly started with Jony Ive, Apple's chief designer, in 2011. [6]. Ive reportedly began "dreaming about an Apple watch just after CEO Steve Jobs' death in October 2011" and brought to idea to Alan Dye, the chief of Apple's human interface group to put the idea to life [7]. The intention of the watch was to reportedly free people from their phones [8]. The designers of the Apple Watch uncovered early in the process that they needed to break away from Apple's status quo of only offering a few product options as the watch industry values personalization and beauty. That realization spurred the development the watch which offered a variety of options in order to appeal to a way consumer base. [9]

Hardware Capabilities

Image of the Apple Watch Hardware Capabilities [10]

The hardware installed on the Apple Watch includes a digital crown, friends button, touchscreen, taptic engine, battery, s1 chip, loudspeaker, custom-heart rate sensor, induction charger, and a customizable appearance. The "digital crown," is a small button on the side of the watch that users can rotate and press in order to scroll through and engage with content on their watch [11]. The friends button, located next to the crown on the side of the watch, brings up a shortlist of chosen contacts to the user. The touchscreen is a retina display that allows users to interact with the watches face, through tapping on the small touchscreen interface with their finger. The touchscreen is polished with crystal in order to prevent users from scratching the screen. The taptic engine delivers tactile sensations to the user, and essentially taps the user's wrist to deliver tactile feedback when interfacing with the digital crown or force touch. It also helps notify users of alerts and notifications [12]. The battery allows the watch to run throughout the day without running out of battery. The Apple Watch comes equipped with a charger, which is used to recharge the battery after use. Similar to the iPhone, the Apple Watch typically lasts about a full day's use before needing to recharge, depending on usage. The s1 chip contains the entire computer system needed for the Apple Watch to run. The loudspeaker which has a multidirectional microphone that allows it to listen and process multiple conversations around and involving the user [13]. It allows for the watch to output noise, or speak, to the user as well. The custom heart-rate sensor uses infrared lights and photosensors to read the user's heart rate [14]. This provides users with information on their daily steps, heart rate capabilities, and hours of sleep.

Software Capabilities

Apple watchOS is the software that Apple Watches run on, and is based on iOS. watchOS was released along with the launch of the first Apple Watch in 2015 [15]. Since 2015, Apple has launched watchOS 1, watchOS 2, watchOS 3, watchOS 4, watchOS5, watchOS 6, watchOS 7, and watchOS 8. Each watchOS update offered a variety of new features and capabilities to the watch user. For example, the latest Apple Watch software update, watchOS 8, includes new features such as an updated interface of its wellness app, and updated fall detection algorithms [16].

Communication features

Through the evolution of the watchOS versions, Apple Watch is able to offer an increasing variety of features to its users, allowing them to engage with the watch in their everyday lives. The Apple Watch allows users to see who is calling or texting them at any given time, given that the watch is properly connected to wifi, their nearby iPhone, or is equipped with GPS capabilities. If the Apple Watch model is equipped with GPS capabilities, and the watch has been connected to a wireless plan with a phone provider, the Apple Watch does not need to be near ones phone in order to send and receive calls and text messages. If an Apple Watch does not have cellular connectivity, it will use bluetooth technology to connect to the users nearby iPhone (up to 12 feet away) or through wifi. The "walkie-talkie" feature allows users to connect with other Apple Watch users; however, both Apple Watch users need to have a watchOS 5 operating system or later. [17] In order to use the Walkie-Talkie features, users must send and accept friend invitations through the "Walkie-Talkie app," and then use the touch and hold button to speak directly to other users like a traditional "Walkie-Talkie."

Fitness Tracking

Apple Watch Rings, as Screen shotted from the Author's Own Watch

The Apple watch offers fitness tracking in the form of Activity Rings which is broken down into three colored rings. The first is the red Move ring which allows users to track how many calories they have burned by moving throughout a 24 hour period. The second is the green Exercise ring that allows users to track the minutes of brisk activity they have completed that day. Finally, the blue Stand ring allows users to track how many hours they have stood in a 24 hour period. [18] The user can define their activity goals for each type of ring, and the ring visually closes once the user achieves that goal. The visualization shown to the right demonstrates the different types of rings, as well as the users progress in completing the goals associated with each ring.

Apple Watch Models

Since its release in 2015, Apple has released multiple other models which provide different features to the user including Apple Watch Series 1, Apple Watch Series 2, Apple Watch Series 3, Apple Watch Series 4, Apple Watch Series 5, Apple Watch SE, Apple Watch Series 6, and Apple Watch Series 7. The price of an Apple Watch can vary greatly, depending on the model and upgrades that the user would like to purchase. An Apple Watch can be bought through the Apple website starting at $199 for the Series 3 model, and $399 for the Series 7 model [19]. The larger displays (45mm) cost users more compared to the smaller screen displays (41mm). There are also other offered upgrades that will affect the price of the watch.

Apple Watch Series 7

The Apple Watch has evolved over time, offering more high-tech features to its users. The newest Apple Watch, Apple Watch Series 7, offers the most high-technology features to its users through its newest watch software, watchOS8 [20]. In comparing to the first model of the Apple Watch, released in 2015, the Series 1 Apple Watch only had a 8GB capacity, the Series 7 Apple Watch has a 32 GB capacity. Other notable upgrades that the Series 7 Apple Watch offers over the Series 1 Watch includes: 50 percent more screen area, GPS and GPS cellular models, water resistance (up to 50 meters).[21] The Apple Watch Series 7 is marketed as having the largest and most advanced display, being the most durable and fast charging [22]. The newest software offers a few new features to users such as the ability to add vaccination records in user's health app as well as allowing users to better control their home smart devices through the home app [23].

Additional Collaborations

Apple partnered with Nike+ to produce an apple watch for those who are passionate about fitness and the Nike brand. This watch was designed to synchronize with the "Nike Run Club" and "Nike Training Club" apps [24]. Apple partnered with Hermès as well, to create an Apple Watch that emphasized bold, colorful leather bands and exclusive new watch faces for users. [25]

Public Perception

The Apple Watch was first introduced to the public in 2015. The International Data corporation (IDC) highlights that the total sales amount of Apple increased in 2015 [26]. One of the main critiques for the Apple watch upon its debut included its inability to differentiate from similar products, such as the Fitbit.
Fitbit is a brand that offers a lineup of smartwatches that allow users to track personal health data including daily activity, sleep metrics, and other personalized health metrics such as heart rate variability. [27] This smartwatch was released in 2009 [28], and Fitbit's early entry to this untapped market has caused it to be popular to millions of Fitbit devices to be sold worldwide. Yet, while Fitbit was one of the first companies to create a smartwatch with the intent of collecting personalized health data, the Apple Watch has become a more popular smartwatch among users. Specifically, Apple sold 33.9 million watches in 2020, compared to 5.9 million Fitbit watches sold in the same year [29].

Ethical Considerations

The data collection abilities of the Apple Watch have lead to concerns about the privacy and security of user's data. Further, concerns about the potential reliance on health related data as a replacement for professional medical care has been of rising concern. Additionally, there has been rising concern over the lack of security related to Internet of Things devices, such as the Apple Watch. There are concerns that the Apple Watch furthers systems of oppression towards underprivileged populations.

Data Privacy

Big data has emerged in recent years, and is of increasing interest to researchers and organizations anticipating an overwhelming flow of data. The individual privacy surrounding this data is of increasing concern. Data privacy is defined as one's control over access to their own personal information, specifically the right to know what personal information is being dispersed to which people [30]. It is therefore important that users understand the security risks of big data, and understand how companies are protecting and handling user data. Given that the Apple Watch collects multiple types of personal data about the user, the discussion of big data privacy concerns have been relevant to this device.
Privacy legislations are important for prioritizing individual privacy protections, and valuing personal information control, despite business and commercial enterprises' desire to collect data. The access to personal data about users is valuable for companies as they are able to profit from users by customizing their marketing and advertising to the user's preferences. The United States specifically passed the first legislation on information privacy more than 40 years ago through the 1974 US Privacy Act, which developed values related to individual data privacy [31]. This legislation is important for monitoring how companies, like Apple, utilize the big data that is collected from devices like the Apple Watch. Given that the Apple Watch is a device that is intended to connect with other devices, the evolution of the internet of Things has complicated the subject of data privacy. Namely, it is of increasing concern that while data privacy legislation exists, the internet of Things complicates the application of the current legislation. Researchers stress that there are risks of privacy loss and exploitation from internet of Things devices, penalizing owners for accessing the advantages of an increasingly connected world [32]. Given that the Apple Watch is a part of the internet of Things, this is an increasing ethical concern with the device.

Health Related Data

Smartwatches, such as the Apple Watch are equipped with features that allow the user to track health related data. In the medical field, the principle of non maleficence requires that medical actions are weighed against risks, benefits, and consequences [33]. Researchers have argued that the accessibility of a user's own health data has been problematic as it violates the principle of non-maleficence.
For example, ECG monitoring on Apple watches which allows users to screen for atrial fibrillation, with the intent of early detection. Early detection of this arrhythmia can prevent adverse events such as stroke, by treating with anticoagulants. Although the Apple Watch has an algorithm with a very high specificity, many people have still been notified of arrhythmia without having atrial fibrillation, leading to a false positive result [34]. If the patient is not aware of the potential benefits, the risks and the possible consequences of atrial fibrillation detection, the diagnostic can be done at the user's risk. The Apple Watch does not provide any information comparable to traditional medical education, leading the patient to not be medically well-informed, according to researchers. Further, researchers argue that patients could overestimate the accuracy of the diagnostic capabilities of the watch and the over reliance on home monitoring of atrial fibrillation could lead to fewer face-to-face doctor visits. Researchers conclude that the over-reliance on smartwatches and the lower number of face-to-face doctor visits lead to a violation of the principle of non-maleficence. [35].
The American Journal of Medicine highlights the limitations of the health benefits that the Apple Watch can provide through its personalized health data. While wearable devices are designed to promote healthy behaviors and decrease risk for chronic diseases, current literature indicates that wearable devices provide little benefit on chronic disease health outcomes. While the Apple Watch uses marketing techniques to boost a product that will lead to a healthier lifestyle, research has found that while wearable devices can motivate and accelerate physical activity, data does not suggest consistent health benefits [36]. Ethically, scholars fear that the advertising of the product can be misleading to customers with chronic diseases, as they overestimate the value that the product can provide by offering personalized health data.

Internet of Things

The internet of Things (IoT) covers objects and devices used by a user that are connected to the internet. Connected Devices have the ability to collect sensitive information on users and their daily activity [37]. While the Internet of Things enhances human comfort and convenience, it has raised questions amongst academics about security and privacy.

Internet of Things & Security

Researchers have uncovered that since Internet of Things technology was not designed with security in mind, as it was designed to provide novel features while minimizing device cost and size, the devices have limited hardware resources. This means that security tools cannot be installed in internet of Things devices, which makes it an easy target for cyber crimes. There are 3 typical ways that an agent can threaten IoT systems: 1. stealing information (ie.user credentials) 2. tracking user information (ie. location) and 3. taking control of a system (ie. through malware) [38]. A single Internet of Things device can compromise other connected devices, leading the collection of compromised devices to be used to attack computing assets and services. This means that criminals can leverage the power of Internet of Things technology can be used to hack connected devices, such as pacemakers, and cause potential physical harm to others [39]. Considering that the Apple Watch is an internet of Things device, data security, is a topic of rising concern with the product as compromised devices can affect other devices within the internet of Things.
At the same time, Internet of Things devices can be useful in criminal activities, by providing law enforcement with rich information. The interconnected nature of the devices means that experts are able to collect more data about criminals, if they are using a connected device [40]. While the data collected on users through the Apple Watch raises data privacy and security concerns, scholars point out that the product can be valuable in crime related investigations.

Increasing Disparities

The Apple Watch may indirectly contribute to the perpetuation inequality amongst minority populations. Since smartwatches are notably expensive products, the purchase of this technology is not accessible to every socioeconomic and demographic population. The majority of smartwatches are bought by young people with higher socioeconomic status [41]. Access to healthcare is lower for low-income populations, leading the inaccessibility of lower populations to gain access to health data through an Apple Watch to lead to further discrimination against people who cannot afford the watches [42]. Further, scholars have uncovered that the accuracy of algorithms for detecting certain health related conditions through the Apple Watch is not representative. Namely, the participants of the Apple heart study were on average 41 years old and 68% were white [43]. Therefore, the algorithms were not evaluated for a diverse group of users, causing there to be a lack of algorithmic accuracy for these populations.
Given the rising concerns about the disparities that Apple indirectly widens through its products, Apple has increasingly made commitments to diversity, equity, and inclusion. Specifically, Apple highlights that the number of employees from underrepresented communities (URCs) has increased by 64%, or over 18,000 people, and makes up nearly 50% of Apple’s U.S. workforce [44].


See also

References

  1. ↑ "Apple Watch Documentation." <https://www.att.com/buy/wearables/apple-watch-series-7-41mm-starlight-aluminum-starlight-sport.html?source=EC1NSpDES0000000P&tfn=wireless&WT.srch=1&wtExtndSource=PRODUCT_GROUP&gclsrc=aw.ds&&gclid=Cj0KCQiAjJOQBhCkARIsAEKMtO04UBmqiHJg_8MSWh-dvlXea5u8BGIgdlFT4PSreL3DnDeL_tfK11AaAs5REALw_wcB&gclsrc=aw.ds>.
  2. ↑ https://www.apple.com/
  3. ↑ https://searchmobilecomputing.techtarget.com/definition/Apple-Watch
  4. ↑ https://www.statista.com/chart/15035/worldwide-smartwatch-shipments/#:~:text=According%20to%20estimates%20from%20Counterpoint,leader%20in%20the%20smartwatch%20market.
  5. ↑ https://www.frontiersin.org/articles/10.3389/fcvm.2020.615927/full
  6. ↑ https://appleinsider.com/inside/apple-watch
  7. ↑ https://www.wired.com/2015/04/the-apple-watch/
  8. ↑ https://www.wired.com/2015/04/the-apple-watch/
  9. ↑ https://www.wired.com/2015/04/the-apple-watch/
  10. ↑ https://www.dailymail.co.uk/sciencetech/article-3030492/Take-peek-inside-Apple-Watch-Infographic-reveals-firm-packs-components-needed-power-wearable.html
  11. ↑ https://www.dailymail.co.uk/sciencetech/article-3030492/Take-peek-inside-Apple-Watch-Infographic-reveals-firm-packs-components-needed-power-wearable.html
  12. ↑ https://www.iphonefaq.org/archives/974320
  13. ↑ https://www.commonsense.org/education/articles/privacy-and-security-evaluation-of-the-apple-watch
  14. ↑ https://www.dailymail.co.uk/sciencetech/article-3030492/Take-peek-inside-Apple-Watch-Infographic-reveals-firm-packs-components-needed-power-wearable.html
  15. ↑ https://www.lifewire.com/what-is-watchos-4690550
  16. ↑ https://www.apple.com/watchos/watchos-8/
  17. ↑ https://learning-oreilly-com.proxy.lib.umich.edu/library/view/apple-watch-for/9781119558637/c05.xhtml#h2-3
  18. ↑ https://learning-oreilly-com.proxy.lib.umich.edu/library/view/apple-watch-for/9781119658665/c08.xhtml#h2-6
  19. ↑ https://www.apple.com/shop/buy-watch/apple-watch
  20. ↑ https://support.apple.com/guide/watch/whats-new-apdb93ea3872/8.0/watchos/8.0
  21. ↑ https://www.apple.com/watch/compare/
  22. ↑ https://support.apple.com/guide/watch/whats-new-apdb93ea3872/8.0/watchos/8.0
  23. ↑ https://support.apple.com/guide/watch/whats-new-apdb93ea3872/8.0/watchos/8.0
  24. ↑ https://www.apple.com/newsroom/2016/09/apple-nike-launch-apple-watch-nike/
  25. ↑ https://learning-oreilly-com.proxy.lib.umich.edu/library/view/apple-watch-for/9781119558637/c01.xhtml#h2-2
  26. ↑ https://repositorio.iscte-iul.pt/handle/10071/14630
  27. ↑ https://www.fitbit.com/global/us/products/smartwatches
  28. ↑ https://www.fitbit.com/global/us/about-us
  29. ↑ https://www.statista.com/chart/15035/worldwide-smartwatch-shipments/#:~:text=According%20to%20estimates%20from%20Counterpoint,leader%20in%20the%20smartwatch%20market
  30. ↑ https://timreview.ca/article/1067
  31. ↑ https://timreview.ca/article/1067
  32. ↑ https://timreview.ca/article/1067
  33. ↑ https://pubmed.ncbi.nlm.nih.gov/30916041/#:~:text=The%20principle%20of%20nonmaleficence%20requires,level%20of%20competence%20and%20training
  34. ↑ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7843431/
  35. ↑ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7843431/
  36. ↑ https://www.amjmed.com/article/S0002-9343(19)30553-4/pdf
  37. ↑ https://www.commonsense.org/education/articles/privacy-and-security-evaluation-of-the-apple-watch
  38. ↑ https://web-s-ebscohost-com.proxy.lib.umich.edu/ehost/detail/detail?vid=0&sid=cbf01992-81a8-48e6-af22-021502434a67%40redis&bdata=JnNpdGU9ZWhvc3QtbGl2ZSZzY29wZT1zaXRl#AN=153038918&db=a9h
  39. ↑ https://link-springer-com.proxy.lib.umich.edu/content/pdf/10.1007%2F978-3-319-99277-8.pdf
  40. ↑ https://link-springer-com.proxy.lib.umich.edu/content/pdf/10.1007%2F978-3-319-99277-8.pdf
  41. ↑ https://www.frontiersin.org/articles/10.3389/fcvm.2020.615927/full
  42. ↑ https://www.nejm.org/doi/full/10.1056/NEJMoa1901183
  43. ↑ https://www.nejm.org/doi/full/10.1056/NEJMoa1901183
  44. ↑ https://www.apple.com/diversity/

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