Difference between revisions of "Touch ID"

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−	[[File:TouchID.png|200px|thumb|right]]	+	[[File:Touch.jpeg|400px|thumb|right|Touch ID Usage <ref>“How to Set up Touch ID on Your IPhone or IPad - Apple Support.” YouTube, YouTube, 5 Mar. 2018, www.youtube.com/watch?v=xTZ2LALWZlg.</ref>]]
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−	An '''Algorithm''' is defined as a set of precise steps and distinct states used to express the detailed structure of a program or the order of events that occurred in a system <ref>Cormen, Thomas H. et al. (2009). ''Introduction to Algorithms;'. MIT Press.</ref>.Algorithms are involved in many aspects of daily life and in complex computer science concepts. They often use repetition of operations to allow people and machines to execute tasks more efficiently by executing tasks faster and using fewer resources such as memory.  On a basic level, an algorithm is a system working through different iterations of a process<ref>Lim, Brian (December 7, 2016). [e27.co/brief-history-algorithms-important-automation-machine-learning-everyday-life-20161207/ "A Brief History of Algorithms (and Why It's so Important in Automation, Machine Learning, and Everyday Life)"] ''e27''.</ref>. They can help turn systematic and tedious tasks into fast, automated processes. Large companies particularly value robust algorithms because their infrastructure depends on efficiency to remain profitable on a massive scale<ref>Rastogi, Rajeev, and Kyuseok Shim (1999). "Scalable algorithms for mining large databases." ''Tutorial notes of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining''. ACM.</ref>.
−	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.	+	'''Touch ID''' was first made by [https://www.apple.com/sitemap/. Apple Inc.] as a fingerprint recognition security feature on iPhones. It was used to unlock iPhones, make purchases on the Apple Store, and use Apple Pay. Touch ID was first introduced in 2013 with the iPhone 5S, and over time, Touch ID was improved and added to even more Apple products. Although the Touch ID is still used and incorporated in many Apple products today, Face ID, a facial recognition security feature, is used a lot in the new Apple phones. Face ID was introduced in 2017 with the iPhone X and created quite the commotion among many Apple product users.
		+	Touch ID works by using a sensor to pass a small current to the user's finger and create a 'fingerprint map'. This map is then stored in a chip in your phone. This way hackers cannot externally access that information.
	== History ==		== History ==
−	The earliest known algorithms stem back to 1700 BC when the Egyptians created them for quick multiplication and division<ref>Stepanov, A. and Rose, D. (2014).
−	''From Mathematics to Generic Programming: The First Algorithm''. [http://www.informit.com/articles/article.aspx?p=2264460 "Chapter 2"]. Retrieved April 28, 2019.</ref>. Since then, ancient Babylonians in 300 BC created algorithms to track farming and livestock using square roots. Following this, steady advancement gave birth to fundamental mathematical algorithm families like algebra, shaping the field of mathematics with its all-purpose formulas. The man often accredited as “The Father of Algebra,” Muhammad ibn Mūsa al-Khwarizmī, was also the one who gave English the word “algorithm” around 850 AD, as he wrote a book ''Al-Khwarizmi on the Hindu Art of Reckoning'', which in Latin translates to ''Algoritmi de Numero Indorum''. The English word [https://www.digit.in/features/science-and-technology/the-origin-of-algorithms-30045.html "algorithm"] was adopted from this title.
−	A myriad of fundamental algorithms have been developed throughout history, ranging from pure mathematics to important computer science stalwarts, extending from ancient times up through the modern day. The computer revolution led to algorithms that can filter and personalize results based on the user.	+	=== History Behind Fingerprints ===
−	The [https://en.wikipedia.org/wiki/Timeline_of_algorithms Algorithm Timeline] outlines the advancements in the development of the algorithm as well as a number of the well-known algorithms developed from the Medieval Period until modern day.	+	[[File:TouchID2nd.png|300px|thumb|right|Touch ID Setup on Mac<ref>"Use Touch ID on Mac" Apple Support. Web. 17 Apr. 2021. https://support.apple.com/guide/mac-help/touch-id-mchl16fbf90a/mac</ref>]] Fingerprints are tiny patterns on the tip of our fingers that are completely unique to each person. No two people have ever been found to have the same fingerprints, so they are seen as one of the most secure ways to verify people. Another reason fingerprints are considered a highly secure feature is that they don't change with age and are easily collected from people. Fingerprints form from a result of DNA and environmental factors starting from a baby’s development in the womb. Environmental factors that can affect a person’s fingerprint are as subtle as the nutrition received, umbilical cord length, position in the womb, and blood pressure. Because of this, even identical twins with the same DNA have never been found to have identical fingerprints. <ref> “Why Twins Don’t Have Identical Fingerprints.” Parenthood, Healthline, 4 Jan. 2019, www.healthline.com/health/do-identical-twins-have-the-same-fingerprints. </ref> The use of fingerprints as a security feature has been most commonly utilized by state prisons, police stations, and even the FBI<ref>Watson, Stephanie (2021). [https://science.howstuffworks.com/fingerprinting.htm "How Fingerprinting Works"] ''howstuffworks''.</ref>. The FBI and police use fingerprints to identify suspects and solve different crimes where fingerprints can be found at the scene. Often they use fingerprint identification to decide sentences, probation, and paroles. The way they find fingerprints is often through chemical techniques and then find matches through online programs<ref>The Scientific Research Honor Society (2021). [https://www.americanscientist.org/article/crime-scene-chemistry-fingerprint-analysis#:~:text=Law%20enforcement%20has%20relied%20on,%2C%20probation%2C%20and%20parole%20decisions "Crime Scene Chemistry: Fingerprint Analysis"] ''American Scientist''.</ref>.
−	=== Computation ===	+	=== History Behind TouchID ===
−	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.	+
−	=== Advancements In Algorithms ===	+	In 2012, Apple bought [https://www.reuters.com/article/us-authentec-acquisition-apple/apple-buys-mobile-security-firm-authentec-for-356-million-idUSBRE86Q0KD20120727 AuthenTec] for $356 million and used their technology to build the Touch ID sensors on the iPhone 5S. Once the Touch ID feature was finished and perfected, it wasn't long before companies like [https://www.motorola.com/us/?ds_rl=1242193&ds_rl=1242196&gclid=CjwKCAjwjuqDBhAGEiwAdX2cj0qTp-UoPkRhZ-8y5qAIgVRZKaE0hsW-lAIckw1vQ9-2GFLGXA9o4RoCn3cQAvD_BwE&gclsrc=aw.ds Motorola] and [https://www.fujitsu.com/us/ Fujitsu] tried to potentially buy out Apple, but Apple eventually won. In 2013, the iPhone 5S came out with a Touch ID protocol for their iPhones which was used only to unlock the phone. Simply resting your finger on the sensor area will automatically read the fingerprint. In certain scenarios, like rebooting the phone, Touch ID is disabled and the user's numerical passcode is required<ref>Apple Inc. (October, 2014). [https://web.archive.org/web/20150319073804/https://www.apple.com/business/docs/iOS_Security_Guide.pdf "IOS Security"] ''IOS Security''.</ref>. A year later, when the iPhone 6 and 6 Plus were released, Touch ID was able to not only unlock the phone, but could be used to make purchases in the App Store, iTunes, and Apple Pay. The Touch ID technology is now on 6S, 6S Plus, 7, 7 Plus, 8, 8 Plus, SE (2nd generation), MacBook Pro, MacBook Air, iPad Pro, and iPad Air. With the more recent Touch ID, you can choose to show details about your notifications only after your fingerprint is read. This way other people who look at your phone can't read your notifications if your iPhone is locked. Now with Facial Recognition rising, it seems that Touch ID's time might be slowly coming to an end. However, its impact on our technological advancements has been revolutionary in the field of technology<ref>Dormehl, Luke. (July 28, 2020). [https://www.cultofmac.com/440033/today-in-apple-history-apple-acquires-the-company-behind-id/ "Today in Apple history: Apple acquires the company behind Touch ID"] ''Cult of Mac''.</ref>.
−	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.	+
−	[[File:machineLearning.png|500px]]	+
−	The machine learning process shown above describes how machine learning algorithms can provide more features and functionality to artificial intelligence.	+	==== The Chip ====
−	== Classifications ==	+	The Fingerprint data was first stored in the Apple A7 chip in the iPhone 5S, but with new phones came new chips. They are now stored inside the Apple A8, A8X, A9, A9X, A10, A10X, A11, A12, A13, and A14 processors in the iPhones and the T1 and T2 in the MacBook Pro and MacBook Air. Contrary to popular belief, the fingerprints are not stored in iCloud or any place outside the physical iPhone itself. [[File:FindPhone.png|230px|thumbnail|right|Image of Find my iPhone<ref>"Locate a Device in Find My on IPhone." Apple Support. Web. 08 Apr. 2021. https://support.apple.com/guide/iphone/locate-a-device-iph09b087eda/ios</ref>]]
−	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.	+
−	==== Recursive Algorithms ====	+	==== Apple and Anti-theft technology ====
−	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.	+	As one of the leading technology companies, Apple has invested heavily in anti-theft technology. If an Apple device is lost or stolen, touch ID and [https://www.apple.com/icloud/find-my/ Find My iPhone] work together to offer additional protection against theft<ref>Apple Inc. (2021). [https://support.apple.com/en-us/HT204587 "About Touch ID advanced security technology"] ''Apple Inc''.</ref>. If the iPhone owner realizes their device is missing, there are numerous ways to lock and locate it. Without the iCloud account info, passcode, or touch ID, it is impossible to get into the device unless you have the original box and the iPhone's serial number. If the owner can't locate an iPhone, you can remotely erase your devices to protect their information.
−	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.	+	Essentially, Apple has created a general culture that their products, especially their most popular product, iPhones, can't be stolen effectively. This process has coined the term [https://mpdc.dc.gov/page/stolen-smart-phone-brick-it "brick"] for iPhones that have been stolen and locked. Unless the original owner deactivates the security protocols and unlocks the devices, it has no viable use other than spare parts.<ref>Srinivasan, Avinash, and Wu, Jie. (2012). [https://link.springer.com/chapter/10.1007/978-3-642-35362-8_2 "SafeCode–safeguarding security and privacy of user data on stolen iOS devices"] ''International Symposium on Cyberspace Safety and Security''.</ref>
−	==== Serial, Parallel or Distributed ====	+	However, hackers are becoming more advanced by the day, and while it is implausible that a stolen phone would end up in a hacker's hands, it is possible.
−	A [https://en.wikipedia.org/wiki/Sequential_algorithm serial algorithm] is an algorithm in which calculation is done in sequential manners on one core, it follows a defined order in solving a problem. Parallel algorithms utilizes the fact that modern computers have more than one cores, so that computations that are not interdependent could be calculated on separate cores. This is referred to as multi-threaded algorithm where each thread is a series of executing commands and they are inter-weaved to ensure correct output without deadlock. A deadlock occurs when there are interdependence between more than one thread so that none of the threads can continue until one of the other threads continues. Parallel algorithm is important that it allows more than one program to run at the same time by leveraging a computer's available resources otherwise would not have been possible with serial algorithm. Finally, distributed algorithm is similar to parallel algorithm in that it allows multiple programs to run at once with the exception that instead of leveraging multiple cores in a single computer it leverages multiple computers that communicates through a computer network. Similar to how parallel algorithm builds on serial algorithm with the added complexity of synchronizing threads to prevent deadlock and ensure correct outputs, distributed algorithm builds on parallel algorithm with the added complexity of managing communication latency and defining order since it is impossible to synchronize every computer's clock in a distributed system without significant compromises. A distributed algorithm provides extra reliability in that data are stored in more than one location so that one failure would not result in loss of data and by doing computation in multiple computer it can potentially have even faster computational speed.	+
−	==== Deterministic vs Non-Deterministic ====	+	In this case, Apple has set another safeguard called the [https://support.apple.com/en-us/HT204587 Secure Enclave], which Apple developed to protect your passcode and fingerprint data. Meaning touch ID doesn't store any fingerprint images and instead relies only on a mathematical representation. It isn't possible for someone to reverse engineer your actual fingerprint image from this stored data therefore your biometrics are protected.
−	Deterministic algorithms are those that solve problems with exact precision and ordering so a given input would produce the same output every time. Non-deterministic algorithms either have data races or utilize certain randomization, so the same input could have a myriad of outputs. Non-deterministic algorithms can be represented by flowcharts, programming languages, and machine language programs. 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>"	+
−	Floyd, Robert W. (November 1996) ''Non-Deterministic Algorithms''. Carnegie Institute of Technology. pp. 1–17."</ref>	+
−	==== Exact vs Approximation ====	+	===Why Do People Use Touch ID===
−	Some algorithms are implemented to solve for the exact solutions of a problem, whereas some problems are implemented for an approximation or heuristic. Approximation is important in which heuristics could provide an answer that is good enough such that the excessive computational time necessary to find the actual solution is not warranted since one would get minimal gains while expanding a great deal of resources. An example where an approximation is warranted is the traveling salesman's algorithm in which has computational complexity of O(n!), so an heuristic is necessary since some high values of n are not even possible to calculate.	+
−	==== Brute Force or Exhaustive Search ====	+	People started using Touch ID because it allows users to quickly unlock their phones.  Before Touch ID, to unlock a locked phone, users would need to enter a passcode.  This passcode was either a combination of numbers (a PIN) or a combination of characters selected from the alphabet and symbols (periods, question marks, etc.)<ref>Cherapau, Ivan, and Muslukhov, Ildar, and Asanka, Nalin, and Beznosov, Konstantin. (2015).[https://www.usenix.org/conference/soups2015/proceedings/presentation/cherapau "On the Impact of Touch ID on iPhone Passcodes"] ''Symposium on Usable Privacy and Security''.</ref>.  It was found that users spend a significant amount of their overall device time entering in their passcode<ref>De Luca, Alexander, and Hang, Alina, and Von Zezschwitz, Emanuel, and Hussmann, Heinrich. (2015). [https://dl.acm.org/doi/10.1145/2702123.2702141 "I Feel Like I'm Taking Selfies All Day!"] ''Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems''.</ref>.  So, by utilizing Touch ID, iPhones could be more efficient to users and all they need is their fingerprint.  And Touch ID can unlock a user's phone in seconds.  Additionally, multiple fingerprints can be stored with Touch ID<ref>Bud, Andrew. (2018). [https://www.sciencedirect.com/science/article/abs/pii/S0969476518300109 "Facing the Future: The Impact of Apple FaceID"] ''Biometric Technology Today''.</ref>.  This makes Touch ID easier to use in that if a user can’t use the original finger that Touch ID was set up with (maybe they broke a finger or it’s dirty) they can use another one of their fingers/fingerprints they set up<ref name="Ahmad">Ahmad, Diana Al, and Al, Hadeel, and Hamad, Nada. (2015). [https://thesai.org/Publications/ViewPaper?Volume=6&Issue=1&Code=IJACSA&SerialNo=22 "Effectiveness of Iphone’s Touch ID: KSA Case Study"] ''International Journal of Advanced Computer Science and Applications''.</ref>.  In addition, when a user purchases something from the App Store, instead of having to re-enter their passcode as was previously required, users can simply place their finger on the scanner and use Touch ID<ref name="Ahmad"></ref>. And, finally, using Touch ID gives users peace of mind that hackers will have a more difficult time getting into their phones and accessing their information<ref name="Ahmad"></ref>.  Because a person’s fingerprint is unique to them, it will be hard, if not impossible, for someone to recreate it.  So, essentially, Touch ID is providing users with a stronger passcode unique to them, that doesn’t change over time.
−	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.	+
−	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.	+	===Various Usage of Touch ID===
−	==== Divide and Conquer ====	+	The most commons usage is to unlock your iPhone, iPad, or MacBook devices. However, this function is banned when the user hasn't been using his or her device for 8 hours. In this situation, the user must self-enter the password. Apart from that, Touch ID can also be applied to various scenes. According to Apple, Touch ID can be used to make purchases in App Store and set for Apple Pay. Additionally, if the individual app supports Touch ID, it can also be used to unlock or make purchases on that app. <ref> Apple [https://support.apple.com/en-us/HT201371 Use Touch ID on iPhone and iPad] </ref> In Apple's Autofill function, which auto-fills the account and password for users, the user needs to first verify their identity using Touch ID. [[File:Applepay.jpg|350px|thumbnail|right|Apple Pay]] The idea of fingerprint biometric locks has been expanded upon into many different products. Many shackle locks are now designed to program the owner’s fingerprint and unlock with it. This design feature eliminates the need for a key and core system and likewise eliminates the possibility for the lock to be picked. Other locks designed with a deadbolt for home door security combine the idea of an electronic lock with fingerprint technology. The internal hardware allows the entering of a passcode as well as a fingerprint to unlock, just like a smartphone. <ref> “Biometric - Keyless Door Locks - Door Locks.” The Home Depot, www.homedepot.com/b/Hardware-Door-Hardware-Door-Locks-Keyless-Door-Locks/Biometric/N-5yc1vZc2bdZ1z1pm1g. </ref> Other examples are lockboxes. These lockboxes can be intended for a variety of applications like gun locks and security safes. Fingerprint gun lockboxes allow users to access their firearms quicker than entering a password. Fingerprint trigger locks provide the same utility. <ref> “10 Best Biometric Gun Safes In 2021.” Gun Reviews and Buying Guides, 28 Jan. 2021, robarguns.com/biometric-gun-safe. </ref>
−	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.	+
−	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>.	+	====Electronic Payment====
−	====Dynamic Programming====	+	Countries like China have been using their smartphones as vessels for payment. In Shanghai, almost everywhere you go people pay with their phones. It is so common that even all the street vendors have QR codes that take smartphones can scan for payment. Some places do not even take cash payments. Now a shopper can leave home with just their phone. Touch ID, Face ID, or password allows a person to unlock a phone and use any credit cards associated with the phone. The development of new facial technology is also starting to appear in China. Users with the newest iPhone can simply scan their faces and confirm their payment. <ref> World Leaders in Research-Based User Experience. “Case Study of Facial-Recognition Payment in China.” Nielsen Norman Group, 10 May 2020, www.nngroup.com/articles/face-recognition-pay/. </ref> In the US the development of Apple Pay and Samsung Pay is also gaining in popularity.
−	[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.	+
−	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].	+	===Touch ID or Face ID===
−	==== Backtracking ====	+	When Apple launched iPhone X, Apple first introduces Face ID. In the following generations of iPhone or iPad Pro, Apple gradually replaces Touch ID with Face ID. However, Touch ID is still widely used in Apple's product line. Currently, the newest generation of iPad Air, MacBook Pro, and MacBook Air are still using Touch ID. According to Apple, the probability that a random person in the population could look at your iPhone or iPad Pro and unlock it using Face ID is approximately 1 in 1,000,000. <ref> Apple [https://support.apple.com/en-us/HT208108 "About Face ID Advanced Technology"] </ref> In comparison, the probability for Touch ID is 1 in 50,000. Apple says Face ID is a huge improvement in security. However, the actual experience differs in different situations. For example, when a person is wearing gloves, Touch ID would not work because no distortion is generated in the button's electrostatic field. <ref> William Judd [https://www.mobilefun.co.uk/blog/2016/09/psa-the-iphone-home-button-wont-work-with-gloves/ "The iPhone home button won’t work with gloves"] </ref> However, under the COVID-19 global pandemic where everyone has to wear masks, Touch ID is easier because one does not need to put his mask off. However, according to BBC, Apple will make Face ID work with masks in the newest update iOS 14.5. <ref> [https://www.bbc.com/news/technology-55904562 "Apple Face ID to work for mask wearers] BBC News</ref>
−	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.	+
−		+
−	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]].	+
−		+
−	====Greedy Algorithm====	+
−	An intuitive approach to design algorithms that don’t always yield the optimal solution. This approach required a collection of candidates, or options, in which the algorithm selects in order to satisfy a given predicate. Greedy algorithms can either favor the least element in the collection or the greatest in order to satisfy the predicate<ref>[https://brilliant.org/wiki/greedy-algorithm/ "Greedy Algorithms"]. ''Brilliant Math & Science Wiki''. Retrieved April 28, 2019.</ref>.	+
−		+
−	An example of a greedy algorithm may take the form of selecting coins to make change in a transaction. The collection includes the official coins of the U.S. currency (25 cents, 10 cents, 5 cents, and 1 cent) and the predicate would be to make change of 11 cents. The greedy algorithm will select the greatest of our collection to approach 11, but not past it. The algorithm will first select a dime, then a penny, then end when 11 cents has been made.	+
−		+
−	== Complexity and Big-O Notation ==	+
−	[[File:Big-O.jpeg|300px|thumbnail|right|Complexity Graph]]	+
−	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.	+
−		+
−	== Artificial Intelligence Algorithms ==	+
−	=== Clustering ===	+
−	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>	+
−		+
−	==== K-Means Clustering ====	+
−	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>	+
−		+
−	==== Mean-Shift Clustering ====	+
−	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>	+
−		+
−	==== DBSCAN Clustering ====	+
−	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'').	+
−		+
−	==== EM Clustering ====	+
−	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:	+
−	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.	+
−	2) The overall purpose of the algorithm is to maximize the chance or the likelihood of belonging to a cluster in the data.	+
−		+
−	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>	+
−		+
−	==== Agglomerative Hierarchical Clustering ====	+
−	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.	+
−		+
−	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>	+
−		+
−	==== Deep Learning and Neural Networks ====	+
−	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>	+
	== Ethical Dilemmas ==		== Ethical Dilemmas ==
−	With the relevance of algorithms as well as their sheer magnitude, ethical dilemmas were bound to arise. There is a vast list of potential ethical issues relating to algorithms and computer science, including issues of privacy, data gathering, and bias.
−
−	=== Bias ===
−	Given that people are the creators of algorithms, code can inherit bias from its coder or its initial source data.
−
−	====Joy Buolamwini and Facial Recognition====
−	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.
−
−	====Bias in Criminalization====
−	[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>
−
−	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>
−
−	==== Job Applicants ====
−	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.
−
−	=== Privacy And Data Gathering ===
−	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.
−
−	===The Filter Bubble===
−	[[File:Filterbubble.png|400px|thumbnail|Personalized, Online Filter Bubbles]]
−	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.
−	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.	+	=== Access To Our Information ===
−	====Filter Bubble in Politics====	+
−	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>	+
−	[[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]]	+	One of the ethical issues that arise with biometric fingerprinting is the right to privacy. For instance, “Biometric data are personally identifying information. Thus biometric systems have the potential to collect not only pattern recognition information captured by sensors, but also other information that can be associated with the biometric data themselves or with data records already contained within the system.”<ref>National Research Council. (2010). [www.nap.edu/read/12720/chapter/6#111 “Read ‘Biometric Recognition: Challenges and Opportunities’ at NAP.edu.”] ''National Academies Press: OpenBook''.</ref> As a result, the problem arises with what data should be stored and for what purposes. This dilemma gets even more complicated when private companies are allowed to collect your information. For instance, “The GPS Act permits service providers to collect geolocation information in the normal course of business”<ref>U.S. Senator Ron Wyden of Oregon. [www.wyden.senate.gov/priorities/gps-act “GPS Act”] ''Ron Wyden United States Senator for Oregon''.</ref>. Hence, it has become ever more important to know what information is being sent and collected about the user while accessing laptops, phones, smartwatches, etc.
−	===Corrupt Personalization===	+	As a follow-up to the concerns mentioned above, the issue arises here regarding informed consent of the user in terms of what they are signing up for because understanding what you are signing up for before giving away sensitive information is an important aspect of a person’s privacy right. “In general, adults are considered to have sufficient ability to understand information. The problem is mainly the child’s informed consent when using biometrics  (24).  Similar informed consent issues also come from vulnerable populations such as the elderly, mentally ill, and poorly understood people”<ref>Cooper, Isaac. (September 2019). [www.researchgate.net/publication/335768067_Ethical_Issues_in_Biometrics “(PDF) Ethical Issues in Biometrics”] ''ResearchGate''.</ref>. Therefore, understanding the terms and conditions before signing up has become important in today’s world.
−	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.	+
−	=== Agency And Accountability ===	+	One of the steps that Apple has taken recently to address some of the issues regarding the storage and retrieval of sensitive information is the use of tokens. For instance, “With Apple Pay, your exact credit card information is never sent over the internet. Instead, a token or random string of numbers representing the card is sent. Biometric tokenization is similar, but it's your biometric data, rather than credit card info, that is obfuscated and transmitted.”<ref>Simic, Bojan. (25 Sept. 2017). [www.forbes.com/sites/forbestechcouncil/2016/12/22/the-promise-and-challenges-of-biometrics/?sh=73359d7c7e6b “Council Post: The Promise And Challenges Of Biometrics”] ''Forbes Magazine''.</ref>
−	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.	+
−	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.	+	=== How Secure Is Touch ID? ===
−	====The Trolley Problem in Practice====	+	Touch ID is more secure than Face ID but less secure than password protection. The question is how much sacrificed security is this convenient option than typing in your password every time you need to unlock your phone or sign in to an account? Apple claims the uniqueness of an individual's fingerprint makes this system extremely secure but researchers have recently discovered a new way to break this security barrier. Researchers at Michigan State University and New York University have discovered that because the fingerprint scanners on phones are so small, they only read partial fingerprints--making the images easier to duplicate. Touch ID works by taking about ten different images creating ten partial images of your fingerprint from different angles, never creating one full unique fingerprint image.  Therefore, a hacker only needs to match one of these ten partial fingerprint images to unlock the phone. Through the study, using computer simulations, researchers "were able to develop a set of artificial “MasterPrints” that could match real prints similar to those used by phones as much as 65 percent of the time.” <ref>Goel, Vindu (April 10, 2017). [http://www.nytimes.com/2017/04/10/technology/fingerprint-security-smartphones-apple-google-samsung.html%20www.nytimes.com/2017/04/10/technology/fingerprint-security-smartphones-apple-google-samsung.html "That Fingerprint Sensor on Your Phone Is Not as Safe as You Think"] ''The New York Times''. </ref> Although this is a concerningly high number, these tests were not done with phones. Instead, they were tested on Touch ID systems not connected to phones, making the realistic percentage of replicated prints much smaller. There is concern about the idea that hackers only need to match one of the ten partial prints to unlock your phone but realistically, this is still a very unlikely and difficult thing to do. Although Touch ID is a very secure system, this study highlights some minor weaknesses it presents that could easily be fixed. Most of the researchers were quoted saying they still use Touch ID to secure their phones, but might suggest users use personal password insertion for more confidential log-ins like bank accounts. As technology develops and the importance of smartphones increases with linkage to bank accounts and credit cards, touch ID security becomes a greater issue.
−	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.	+
−	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.	+	=== User Accessibility Concerns ===
−	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.	+	Apple has made massive efforts in improving accessibility in recent years. This includes adding categories such as “Vision,” Interaction,” “Hearing,” “Media,” and “Learning” to their “Accessibility” section in their phone settings. However, the release of the iPhone X prompted some concern among the blind/low-vision community <ref> Thompson, Terrill. (2018). [www.washington.edu/accesscomputing/resources/accesscomputing-news-february-2018/iphone-x-and-accessibility “iPhone X and Accessibility | AccessComputing”] ''AccessComputing''. </ref>. The removal of the home button with the additional Touch ID feature posed some complications with those who relied on the physical feeling of the home button to navigate unlocking their phone.
−	=== Intentions and Consequences ===	+	Luckily, the new Face ID feature has proven to outdo Touch ID at every level in regards to accessibility. First, the set-up process of Face ID is much faster and requires less precision which can be a hindrance to many with limited fine-motor skills. Beyond set-up, Face ID has allowed Apple to make a virtually hands-free experience. The VoiceOver feature has been a vital tool to the blind/low-vision community and this coupled with the Face ID unlocking feature make iPhone products all the more accessible. You still have to occasionally swipe up from the bottom of the screen to the top, but this requires less precision than the former home button/Touch ID as mentioned previously <ref> Aquino, Steven. (21 Nov. 2017). [www.stevensblog.co/blogs/what-face-id-means-for-accessibility “What Face ID Means for Accessibility”] ''Steven’s Blog''.</ref>.
−	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.	+
−	==== YouTube Radicalization ====	+	Although Face ID is generally more accessible, there was previous concern with the “look-to-unlock” feature that requires an active gaze would create a barrier. People with visual impairments or blindness find this task difficult or impossible to do. They also don’t naturally hold the phone up to their face when using them. However, Apple thought this through and allows users to disable this feature by going to Settings > Face ID and Passcodes > Required Attention for Face ID <ref> RightPoint. (6 Dec. 2017). [www.rightpoint.com/thought/articles/2017/12/06/what-does-x-face-mean “The iPhone X: What Does Face ID Mean for Accessibility?”] ''Rightpoint''.</ref>. Apple does say, “Requiring Attention makes Face ID more secure,” but unlike Touch ID which allows multiple fingerprints to be added, only one face can be entered for Face ID <ref> Ingber, Janet. (Feb. 2018). [www.afb.org/aw/19/2/15124 “The iPhone X for People with Visual Impairments: Face ID, New Gestures, and Useful Commands | AccessWorld | American Foundation for the Blind”] ''American Foundation for the Blind''.</ref>. Touch ID is not made with accessibility in mind. Apple went a more inclusive route by creating and implementing Face ID instead.
−	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.	+
−	==== Facebook Advertising ====	+	Touch ID may also not be of use if a person's fingerprint is not identifiable. There are many reasons this could be. For example, wet, wrinkly fingers may not register correctly on Touch ID devices. Medical reasons also may explain the loss of a fingerprint. A 62-year-old man from Singapore was treated for cancer with chemotherapy. He was given the drug capecitabine which led to the peeling of his palms and feet, leaving him temporarily without a fingerprint. US Customs detained him on his travel to visit family in the states and released him after a couple of hours. <ref> Harmon, Katherine. “Can You Lose Your Fingerprints?” Scientific American, Scientific American, 29 May 2009, www.scientificamerican.com/article/lose-your-fingerprints/. </ref>
−	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==		==See also==
	{{resource|		{{resource|
−	*[[Bias in Information]]	+	*[[Face recognition]]
−	*[[Artificial Agents]]	+	*[[Face recognition in law enforcement]]
−	*[[Value Sensitive Design]]	+
−	*[[Artificial Intelligence and Technology]]	+
	}}		}}
	== References ==		== References ==
	<references/>		<references/>
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Latest revision as of 16:10, 20 April 2021

Touch ID was first made by Apple Inc. as a fingerprint recognition security feature on iPhones. It was used to unlock iPhones, make purchases on the Apple Store, and use Apple Pay. Touch ID was first introduced in 2013 with the iPhone 5S, and over time, Touch ID was improved and added to even more Apple products. Although the Touch ID is still used and incorporated in many Apple products today, Face ID, a facial recognition security feature, is used a lot in the new Apple phones. Face ID was introduced in 2017 with the iPhone X and created quite the commotion among many Apple product users.

Touch ID works by using a sensor to pass a small current to the user's finger and create a 'fingerprint map'. This map is then stored in a chip in your phone. This way hackers cannot externally access that information.

History

History Behind Fingerprints

Fingerprints are tiny patterns on the tip of our fingers that are completely unique to each person. No two people have ever been found to have the same fingerprints, so they are seen as one of the most secure ways to verify people. Another reason fingerprints are considered a highly secure feature is that they don't change with age and are easily collected from people. Fingerprints form from a result of DNA and environmental factors starting from a baby’s development in the womb. Environmental factors that can affect a person’s fingerprint are as subtle as the nutrition received, umbilical cord length, position in the womb, and blood pressure. Because of this, even identical twins with the same DNA have never been found to have identical fingerprints. ^[3] The use of fingerprints as a security feature has been most commonly utilized by state prisons, police stations, and even the FBI^[4]. The FBI and police use fingerprints to identify suspects and solve different crimes where fingerprints can be found at the scene. Often they use fingerprint identification to decide sentences, probation, and paroles. The way they find fingerprints is often through chemical techniques and then find matches through online programs^[5].

History Behind TouchID

In 2012, Apple bought AuthenTec for $356 million and used their technology to build the Touch ID sensors on the iPhone 5S. Once the Touch ID feature was finished and perfected, it wasn't long before companies like Motorola and Fujitsu tried to potentially buy out Apple, but Apple eventually won. In 2013, the iPhone 5S came out with a Touch ID protocol for their iPhones which was used only to unlock the phone. Simply resting your finger on the sensor area will automatically read the fingerprint. In certain scenarios, like rebooting the phone, Touch ID is disabled and the user's numerical passcode is required^[6]. A year later, when the iPhone 6 and 6 Plus were released, Touch ID was able to not only unlock the phone, but could be used to make purchases in the App Store, iTunes, and Apple Pay. The Touch ID technology is now on 6S, 6S Plus, 7, 7 Plus, 8, 8 Plus, SE (2nd generation), MacBook Pro, MacBook Air, iPad Pro, and iPad Air. With the more recent Touch ID, you can choose to show details about your notifications only after your fingerprint is read. This way other people who look at your phone can't read your notifications if your iPhone is locked. Now with Facial Recognition rising, it seems that Touch ID's time might be slowly coming to an end. However, its impact on our technological advancements has been revolutionary in the field of technology^[7].

The Chip

The Fingerprint data was first stored in the Apple A7 chip in the iPhone 5S, but with new phones came new chips. They are now stored inside the Apple A8, A8X, A9, A9X, A10, A10X, A11, A12, A13, and A14 processors in the iPhones and the T1 and T2 in the MacBook Pro and MacBook Air. Contrary to popular belief, the fingerprints are not stored in iCloud or any place outside the physical iPhone itself.

Apple and Anti-theft technology

As one of the leading technology companies, Apple has invested heavily in anti-theft technology. If an Apple device is lost or stolen, touch ID and Find My iPhone work together to offer additional protection against theft^[9]. If the iPhone owner realizes their device is missing, there are numerous ways to lock and locate it. Without the iCloud account info, passcode, or touch ID, it is impossible to get into the device unless you have the original box and the iPhone's serial number. If the owner can't locate an iPhone, you can remotely erase your devices to protect their information.

Essentially, Apple has created a general culture that their products, especially their most popular product, iPhones, can't be stolen effectively. This process has coined the term "brick" for iPhones that have been stolen and locked. Unless the original owner deactivates the security protocols and unlocks the devices, it has no viable use other than spare parts.^[10]

However, hackers are becoming more advanced by the day, and while it is implausible that a stolen phone would end up in a hacker's hands, it is possible.

In this case, Apple has set another safeguard called the Secure Enclave, which Apple developed to protect your passcode and fingerprint data. Meaning touch ID doesn't store any fingerprint images and instead relies only on a mathematical representation. It isn't possible for someone to reverse engineer your actual fingerprint image from this stored data therefore your biometrics are protected.

Why Do People Use Touch ID

People started using Touch ID because it allows users to quickly unlock their phones. Before Touch ID, to unlock a locked phone, users would need to enter a passcode. This passcode was either a combination of numbers (a PIN) or a combination of characters selected from the alphabet and symbols (periods, question marks, etc.)^[11]. It was found that users spend a significant amount of their overall device time entering in their passcode^[12]. So, by utilizing Touch ID, iPhones could be more efficient to users and all they need is their fingerprint. And Touch ID can unlock a user's phone in seconds. Additionally, multiple fingerprints can be stored with Touch ID^[13]. This makes Touch ID easier to use in that if a user can’t use the original finger that Touch ID was set up with (maybe they broke a finger or it’s dirty) they can use another one of their fingers/fingerprints they set up^[14]. In addition, when a user purchases something from the App Store, instead of having to re-enter their passcode as was previously required, users can simply place their finger on the scanner and use Touch ID^[14]. And, finally, using Touch ID gives users peace of mind that hackers will have a more difficult time getting into their phones and accessing their information^[14]. Because a person’s fingerprint is unique to them, it will be hard, if not impossible, for someone to recreate it. So, essentially, Touch ID is providing users with a stronger passcode unique to them, that doesn’t change over time.

Various Usage of Touch ID

The most commons usage is to unlock your iPhone, iPad, or MacBook devices. However, this function is banned when the user hasn't been using his or her device for 8 hours. In this situation, the user must self-enter the password. Apart from that, Touch ID can also be applied to various scenes. According to Apple, Touch ID can be used to make purchases in App Store and set for Apple Pay. Additionally, if the individual app supports Touch ID, it can also be used to unlock or make purchases on that app. ^[15] In Apple's Autofill function, which auto-fills the account and password for users, the user needs to first verify their identity using Touch ID. The idea of fingerprint biometric locks has been expanded upon into many different products. Many shackle locks are now designed to program the owner’s fingerprint and unlock with it. This design feature eliminates the need for a key and core system and likewise eliminates the possibility for the lock to be picked. Other locks designed with a deadbolt for home door security combine the idea of an electronic lock with fingerprint technology. The internal hardware allows the entering of a passcode as well as a fingerprint to unlock, just like a smartphone. ^[16] Other examples are lockboxes. These lockboxes can be intended for a variety of applications like gun locks and security safes. Fingerprint gun lockboxes allow users to access their firearms quicker than entering a password. Fingerprint trigger locks provide the same utility. ^[17]

Electronic Payment

Countries like China have been using their smartphones as vessels for payment. In Shanghai, almost everywhere you go people pay with their phones. It is so common that even all the street vendors have QR codes that take smartphones can scan for payment. Some places do not even take cash payments. Now a shopper can leave home with just their phone. Touch ID, Face ID, or password allows a person to unlock a phone and use any credit cards associated with the phone. The development of new facial technology is also starting to appear in China. Users with the newest iPhone can simply scan their faces and confirm their payment. ^[18] In the US the development of Apple Pay and Samsung Pay is also gaining in popularity.

Touch ID or Face ID

When Apple launched iPhone X, Apple first introduces Face ID. In the following generations of iPhone or iPad Pro, Apple gradually replaces Touch ID with Face ID. However, Touch ID is still widely used in Apple's product line. Currently, the newest generation of iPad Air, MacBook Pro, and MacBook Air are still using Touch ID. According to Apple, the probability that a random person in the population could look at your iPhone or iPad Pro and unlock it using Face ID is approximately 1 in 1,000,000. ^[19] In comparison, the probability for Touch ID is 1 in 50,000. Apple says Face ID is a huge improvement in security. However, the actual experience differs in different situations. For example, when a person is wearing gloves, Touch ID would not work because no distortion is generated in the button's electrostatic field. ^[20] However, under the COVID-19 global pandemic where everyone has to wear masks, Touch ID is easier because one does not need to put his mask off. However, according to BBC, Apple will make Face ID work with masks in the newest update iOS 14.5. ^[21]

Ethical Dilemmas

Access To Our Information

One of the ethical issues that arise with biometric fingerprinting is the right to privacy. For instance, “Biometric data are personally identifying information. Thus biometric systems have the potential to collect not only pattern recognition information captured by sensors, but also other information that can be associated with the biometric data themselves or with data records already contained within the system.”^[22] As a result, the problem arises with what data should be stored and for what purposes. This dilemma gets even more complicated when private companies are allowed to collect your information. For instance, “The GPS Act permits service providers to collect geolocation information in the normal course of business”^[23]. Hence, it has become ever more important to know what information is being sent and collected about the user while accessing laptops, phones, smartwatches, etc.

As a follow-up to the concerns mentioned above, the issue arises here regarding informed consent of the user in terms of what they are signing up for because understanding what you are signing up for before giving away sensitive information is an important aspect of a person’s privacy right. “In general, adults are considered to have sufficient ability to understand information. The problem is mainly the child’s informed consent when using biometrics (24). Similar informed consent issues also come from vulnerable populations such as the elderly, mentally ill, and poorly understood people”^[24]. Therefore, understanding the terms and conditions before signing up has become important in today’s world.

One of the steps that Apple has taken recently to address some of the issues regarding the storage and retrieval of sensitive information is the use of tokens. For instance, “With Apple Pay, your exact credit card information is never sent over the internet. Instead, a token or random string of numbers representing the card is sent. Biometric tokenization is similar, but it's your biometric data, rather than credit card info, that is obfuscated and transmitted.”^[25]

How Secure Is Touch ID?

Touch ID is more secure than Face ID but less secure than password protection. The question is how much sacrificed security is this convenient option than typing in your password every time you need to unlock your phone or sign in to an account? Apple claims the uniqueness of an individual's fingerprint makes this system extremely secure but researchers have recently discovered a new way to break this security barrier. Researchers at Michigan State University and New York University have discovered that because the fingerprint scanners on phones are so small, they only read partial fingerprints--making the images easier to duplicate. Touch ID works by taking about ten different images creating ten partial images of your fingerprint from different angles, never creating one full unique fingerprint image. Therefore, a hacker only needs to match one of these ten partial fingerprint images to unlock the phone. Through the study, using computer simulations, researchers "were able to develop a set of artificial “MasterPrints” that could match real prints similar to those used by phones as much as 65 percent of the time.” ^[26] Although this is a concerningly high number, these tests were not done with phones. Instead, they were tested on Touch ID systems not connected to phones, making the realistic percentage of replicated prints much smaller. There is concern about the idea that hackers only need to match one of the ten partial prints to unlock your phone but realistically, this is still a very unlikely and difficult thing to do. Although Touch ID is a very secure system, this study highlights some minor weaknesses it presents that could easily be fixed. Most of the researchers were quoted saying they still use Touch ID to secure their phones, but might suggest users use personal password insertion for more confidential log-ins like bank accounts. As technology develops and the importance of smartphones increases with linkage to bank accounts and credit cards, touch ID security becomes a greater issue.

User Accessibility Concerns

Apple has made massive efforts in improving accessibility in recent years. This includes adding categories such as “Vision,” Interaction,” “Hearing,” “Media,” and “Learning” to their “Accessibility” section in their phone settings. However, the release of the iPhone X prompted some concern among the blind/low-vision community ^[27]. The removal of the home button with the additional Touch ID feature posed some complications with those who relied on the physical feeling of the home button to navigate unlocking their phone.

Luckily, the new Face ID feature has proven to outdo Touch ID at every level in regards to accessibility. First, the set-up process of Face ID is much faster and requires less precision which can be a hindrance to many with limited fine-motor skills. Beyond set-up, Face ID has allowed Apple to make a virtually hands-free experience. The VoiceOver feature has been a vital tool to the blind/low-vision community and this coupled with the Face ID unlocking feature make iPhone products all the more accessible. You still have to occasionally swipe up from the bottom of the screen to the top, but this requires less precision than the former home button/Touch ID as mentioned previously ^[28].

Although Face ID is generally more accessible, there was previous concern with the “look-to-unlock” feature that requires an active gaze would create a barrier. People with visual impairments or blindness find this task difficult or impossible to do. They also don’t naturally hold the phone up to their face when using them. However, Apple thought this through and allows users to disable this feature by going to Settings > Face ID and Passcodes > Required Attention for Face ID ^[29]. Apple does say, “Requiring Attention makes Face ID more secure,” but unlike Touch ID which allows multiple fingerprints to be added, only one face can be entered for Face ID ^[30]. Touch ID is not made with accessibility in mind. Apple went a more inclusive route by creating and implementing Face ID instead.

Touch ID may also not be of use if a person's fingerprint is not identifiable. There are many reasons this could be. For example, wet, wrinkly fingers may not register correctly on Touch ID devices. Medical reasons also may explain the loss of a fingerprint. A 62-year-old man from Singapore was treated for cancer with chemotherapy. He was given the drug capecitabine which led to the peeling of his palms and feet, leaving him temporarily without a fingerprint. US Customs detained him on his travel to visit family in the states and released him after a couple of hours. ^[31]

See also

References

1. ↑ “How to Set up Touch ID on Your IPhone or IPad - Apple Support.” YouTube, YouTube, 5 Mar. 2018, www.youtube.com/watch?v=xTZ2LALWZlg.
2. ↑ "Use Touch ID on Mac" Apple Support. Web. 17 Apr. 2021. https://support.apple.com/guide/mac-help/touch-id-mchl16fbf90a/mac
3. ↑ “Why Twins Don’t Have Identical Fingerprints.” Parenthood, Healthline, 4 Jan. 2019, www.healthline.com/health/do-identical-twins-have-the-same-fingerprints.
4. ↑ Watson, Stephanie (2021). "How Fingerprinting Works" howstuffworks.
5. ↑ The Scientific Research Honor Society (2021). "Crime Scene Chemistry: Fingerprint Analysis" American Scientist.
6. ↑ Apple Inc. (October, 2014). "IOS Security" IOS Security.
7. ↑ Dormehl, Luke. (July 28, 2020). "Today in Apple history: Apple acquires the company behind Touch ID" Cult of Mac.
8. ↑ "Locate a Device in Find My on IPhone." Apple Support. Web. 08 Apr. 2021. https://support.apple.com/guide/iphone/locate-a-device-iph09b087eda/ios
9. ↑ Apple Inc. (2021). "About Touch ID advanced security technology" Apple Inc.
10. ↑ Srinivasan, Avinash, and Wu, Jie. (2012). "SafeCode–safeguarding security and privacy of user data on stolen iOS devices" International Symposium on Cyberspace Safety and Security.
11. ↑ Cherapau, Ivan, and Muslukhov, Ildar, and Asanka, Nalin, and Beznosov, Konstantin. (2015)."On the Impact of Touch ID on iPhone Passcodes" Symposium on Usable Privacy and Security.
12. ↑ De Luca, Alexander, and Hang, Alina, and Von Zezschwitz, Emanuel, and Hussmann, Heinrich. (2015). "I Feel Like I'm Taking Selfies All Day!" Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems.
13. ↑ Bud, Andrew. (2018). "Facing the Future: The Impact of Apple FaceID" Biometric Technology Today.
14. ↑ ^{14.0 14.1 14.2} Ahmad, Diana Al, and Al, Hadeel, and Hamad, Nada. (2015). "Effectiveness of Iphone’s Touch ID: KSA Case Study" International Journal of Advanced Computer Science and Applications.
15. ↑ Apple Use Touch ID on iPhone and iPad
16. ↑ “Biometric - Keyless Door Locks - Door Locks.” The Home Depot, www.homedepot.com/b/Hardware-Door-Hardware-Door-Locks-Keyless-Door-Locks/Biometric/N-5yc1vZc2bdZ1z1pm1g.
17. ↑ “10 Best Biometric Gun Safes In 2021.” Gun Reviews and Buying Guides, 28 Jan. 2021, robarguns.com/biometric-gun-safe.
18. ↑ World Leaders in Research-Based User Experience. “Case Study of Facial-Recognition Payment in China.” Nielsen Norman Group, 10 May 2020, www.nngroup.com/articles/face-recognition-pay/.
19. ↑ Apple "About Face ID Advanced Technology"
20. ↑ William Judd "The iPhone home button won’t work with gloves"
21. ↑ "Apple Face ID to work for mask wearers BBC News
22. ↑ National Research Council. (2010). [www.nap.edu/read/12720/chapter/6#111 “Read ‘Biometric Recognition: Challenges and Opportunities’ at NAP.edu.”] National Academies Press: OpenBook.
23. ↑ U.S. Senator Ron Wyden of Oregon. [www.wyden.senate.gov/priorities/gps-act “GPS Act”] Ron Wyden United States Senator for Oregon.
24. ↑ Cooper, Isaac. (September 2019). [www.researchgate.net/publication/335768067_Ethical_Issues_in_Biometrics “(PDF) Ethical Issues in Biometrics”] ResearchGate.
25. ↑ Simic, Bojan. (25 Sept. 2017). [www.forbes.com/sites/forbestechcouncil/2016/12/22/the-promise-and-challenges-of-biometrics/?sh=73359d7c7e6b “Council Post: The Promise And Challenges Of Biometrics”] Forbes Magazine.
26. ↑ Goel, Vindu (April 10, 2017). "That Fingerprint Sensor on Your Phone Is Not as Safe as You Think" The New York Times.
27. ↑ Thompson, Terrill. (2018). [www.washington.edu/accesscomputing/resources/accesscomputing-news-february-2018/iphone-x-and-accessibility “iPhone X and Accessibility | AccessComputing”] AccessComputing.
28. ↑ Aquino, Steven. (21 Nov. 2017). [www.stevensblog.co/blogs/what-face-id-means-for-accessibility “What Face ID Means for Accessibility”] Steven’s Blog.
29. ↑ RightPoint. (6 Dec. 2017). [www.rightpoint.com/thought/articles/2017/12/06/what-does-x-face-mean “The iPhone X: What Does Face ID Mean for Accessibility?”] Rightpoint.
30. ↑ Ingber, Janet. (Feb. 2018). [www.afb.org/aw/19/2/15124 “The iPhone X for People with Visual Impairments: Face ID, New Gestures, and Useful Commands | AccessWorld | American Foundation for the Blind”] American Foundation for the Blind.
31. ↑ Harmon, Katherine. “Can You Lose Your Fingerprints?” Scientific American, Scientific American, 29 May 2009, www.scientificamerican.com/article/lose-your-fingerprints/.