Difference between revisions of "Machine Learning Underlying Technology and Ethical Issues"

Revision as of 05:36, 27 January 2022

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^[1]. 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^[2]. 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^[3].

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^[4]. 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 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^[5].

References

1. ↑ P. Louridas and C. Ebert, "Machine Learning," in IEEE Software, vol. 33, no. 5, pp. 110-115, Sept.-Oct. 2016, doi: 10.1109/MS.2016.114.
2. ↑ P. Louridas and C. Ebert, "Machine Learning," in IEEE Software, vol. 33, no. 5, pp. 110-115, Sept.-Oct. 2016, doi: 10.1109/MS.2016.114.
3. ↑ Kumar, M., Roy, R., & Oden, K. D. (2020, September). Identifying Bias in Machine Learning Algorithms: CLASSIFICATION WITHOUT DISCRIMINATION. The RMA Journal, 103(1), 42. https://link.gale.com/apps/doc/A639368819/ITOF?u=umuser&sid=bookmark-ITOF&xid=277baa63
4. ↑ Rosenblatt, Frank (January 1957). "The Perceptron: A Perceiving and Recognizing Automaton (Project PARA)" (PDF). Report (85–460–1).
5. ↑ Daume, Hal (January 2017). “A Course in Machine Learning”. http://ciml.info/