Date of Award
Bachelor of Science (B.S.) in Political Science and University Honors
Law -- Methodology -- Data processing, Information storage and retrieval systems -- Law, Artificial intelligence, Machine learning, Social policy -- Decision making
Given growing investment capital in research and development, accompanied by extensive literature on the subject by researchers in nearly every domain from civil engineering to legal studies, automated decision-support systems (ADM) are likely to see a place in the foreseeable future. Artificial intelligence (AI), as an automated system, can be defined as broad range of computerized tasks designed to replicate human neural networks, store and organize large quantities of information, detect patterns, and make predictions with increasing accuracy and reliability. By itself, artificial intelligence is not quite science-fiction tropes (i.e. an uncontrollable existential threat to humanity) yet not without real-world implications. The fears that come from machines operating autonomously are justified in many ways given their ability to worsen existing inequalities, collapse financial markets (the 2010 “flash crash”), erode trust in societal institutions, and pose threats to physical safety. Still, even when applied in complex social environments, the political and legal mechanisms for dealing with the risks and harms that are likely to arise from artificial intelligence are not obsolete. As this paper seeks to demonstrate, other Information Age technologies have introduced comparable issues. However, the dominant market-based approach to regulation is insufficient in dealing with issues related to artificial intelligence because of the unique risks they pose to civil liberties and human rights. Assuming the government has a role in protecting values and ensuring societal well-being, in this paper, I work toward an alternative regulatory approach that focuses on regulating the commercial side of automated decision-making and machine learning techniques.
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Heminger, Alyssa, "Automated Decision Making and Machine Learning: Regulatory Alternatives for Autonomous Settings" (2021). University Honors Theses. Paper 1115.
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