Machine Learning Underlying Technology and Ethical Issues

Machine Learning is a subfield of Computer Science that has its roots in the 1950s but gained recognition in academia starting during the 1970s. The field of machine learning has grown significantly since the 1970s due to the rapid increase in computational power as well as the decreased costs. At a basic level, machine learning algorithms process and learn from a set of data given to them called training data, the model they build can then be used to predict new data that they have not seen before. Machine learning allows for the analysis, pattern detection, and prediction of massive amounts of data far better and more efficient than a human or team of humans ever could. This has brought upon many great benefits through the use of machine learning. For example, image analysis for medical purposes, data analysis in business, and object recognition for autonomous systems just to name a few. Despite the practicality of machine learning, unintended negative effects in the form of various biases and unfairness have emerged from models. Harmful societal stereotypes have been unintentionally perpetuated through the use of machine learning which has negatively impacted various groups of people. One incident of bias was Apple Credit Card offering smaller lines of credit to women when using machine learning to determine creditworthiness. Conversations about the ethics of machine learning are becoming more widespread and there has been an increase in research into mitigation strategies. Some common ways of reducing model bias are more frequent model and feature analysis, adversarial de-biasing, and regularization.

History

One of the first strides in the realm of Machine Learning was taken by Frank Rosenblatt from Cornell in 1957. Rosenblatt was a psychologist who developed the first machine learning algorithm the Perceptron. As a psychologist, his efforts into creating the perceptron stemmed from the human abilities of perception, recognition, concept formulation, and generalization of experiences. Through his cognitive studies, he developed the perceptron with these conditions in mind, an algorithm that could “learn” and then predict with probabilistic methods. A basic perceptron algorithm operates on two assumptions the data can be binary classified and the data is linearly separable. Binary classification is where a data point can only be classified into one group or another, for example, positive or negative. Data is called linearly separable if it can be graphed in two dimensions and a straight line, called a decision boundary, can be drawn to separate it. The perception algorithm is a very simple but effective one. It looks at one data point at a time and classifies it, if correct it does nothing, if incorrect it updates the corresponding decision boundary or separating line. It continues to do this for all the data points until they are all correct and then it stops. If the model is given a test point to predict, depending on which side of the decision boundary it is on determines the prediction. The Perceptron can be viewed as a single-layer neural network taking in a single data point and predicting it, laying the groundwork for algorithms to come.

AI Winter

The advent of the Perceptron led to a large amount of hype surrounding Artificial Intelligence in the 1960s. Machine Learning is viewed as a subset of the larger field of artificial intelligence, where machines display intelligence. This was displayed by the 1961 television episode “The Thinking Machine” which was a documentary on artificial intelligence aired on CBS and produced with MIT. The public started to believe that robots would soon be able to teach classes, operate rockets, and think faster than the human brain. The first ethical questions surrounding artificial intelligence also started to arise at this time. Could artificial intelligence outsmart humans and replace them? It was also during the height of the cold war and people were wondering, what if the Soviets won the war of AI? Despite all the hype, none of these questions came true during the 1960s, and by the 1970s the hype surrounding AI had crashed, funding was cut, and researchers lost the passion for it . The promises of the perceptron’s ability to be used to complete more complex tasks were not coming to fruition. This came to be known as an AI winter, a time where interest in AI is low and there is not much funding or research being put into the field. Despite the lack of widespread research during AI winters, few dedicated mathematicians and computer scientists kept the field alive with significant contributions. In 1985 with the introduction of new methods such as gradient descent, multi-layer neural networks, and backpropagation AI and machine learning became prominent once again but still wasn’t in widespread use. Data is the driver of machine learning, in order to solve complex problems a significant amount of data is needed. Collecting, processing, and doing computations on large datasets is a challenge technically as well as it requires a lot of computation. Thus as the price of computation decreased significantly in the 90s and the advent of the internet allowed for more access to data, machine learning exploded in use.

Machine Learning Algorithms and Technologies

Features

Machine learning algorithms are only as good as the training data they are given. Similar to a human, if given bad information the algorithm will make bad predictions when shown new information. Data given to machine learning algorithms is known as features. And a singular data point would be known as a feature vector, with each dimension of the vector being a different feature. Feature vectors can represent many different types of data such as text data, image data, stock market data, and a lot more. Two and three-dimensional feature vectors can be visualized by humans on a plot and can give some idea about how the algorithm is making its decisions. However, feature vectors can contain many more dimensions which would be extremely difficult for a human to try and find patterns. That's where machine learning algorithms are useful. The feature itself doesn’t mean much to the computer, however, the values and how these values compare to other feature values help the algorithm construct models. With this ability to recognize patterns with extremely large and higher dimensional datasets comes benefits it also leads to bias as it becomes hard to discern what is influencing the model.

Supervised learning

Supervised learning is a subset of machine learning. It contains algorithms that utilize training data that is labeled in order to classify data it has not seen before. For example, if you were trying to predict dog breeds from an image, you would train the algorithm with images of dogs that have been labeled with the type of dog displayed in the picture. In many cases, the data has to be labeled by humans which can easily make mistakes. This can unintentionally introduce bias and error into models that are used for important tasks. Commonly used supervised learning algorithms include Support Vector Machines, Linear and Logistic Regression, K-nearest neighbors, and Neural Networks.

Unsupervised Learning

Unsupervised learning is a subset of machine learning. It contains algorithms that utilize unlabeled training data to find patterns, clusters, and structures within data. New data points can then be predicted depending on where they would fit in on the model. An example of this can be seen with product recommendations. If two people buy the same product and they went on to purchase various other items, it might recommend similar products. Some popular unsupervised learning algorithms include K-means Clustering, Spectral Clustering, Hierarchical Clustering, Gaussian Mixture Models, and Expectation-Maximization.

Other Categories of Algorithms

There are other types of machine learning subsets, however, supervised and unsupervised learning are the most popular. Some of the other categories include semi-supervised learning as well as reinforcement learning. Semi-supervised has some training data with labels and some training data without labels. Reinforcement learning is where the model will use information that it learns as well as actions it takes in order to maximize reward. A popular reinforcement learning algorithm type is Hidden-Markov Models.