Difference between revisions of "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 the industry 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<ref>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.</ref>. 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<ref>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.</ref>. 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<ref>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</ref>.  

'''Machine Learning''' is a subfield of Computer Science that has its roots in the 1950s but gained recognition in the industry 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<ref>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.</ref>. 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<ref>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.</ref>. 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<ref>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</ref>.  

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