Evidence-based AI Ethics
Name
Boag-wboag-PhD-EECS-2022-thesis.pdf
Description
Thesis PDF
Size
6.72 MB
Format
Adobe PDF
Checksum (MD5)
bfa94e19ecdf8e8a9cd6c587bbdb7477
Author(s)
Boag, William
Advisor(s)
Szolovits, Peter
Date Issued
May 2022
Publisher
Massachusetts Institute of Technology
Abstract
With the rise in prominence of algorithmic-decision making, and numerous high-profile failures, many people have called for the integration of ethics into the development and use of these technologies. In the past five years, the field of “AI Ethics” has risen to prominence to explore questions such as 'how can ML algorithms be more fair' and 'are are tradeoffs when incorporating values such as fairness or privacy into models.' One common trend, particularly by corporations and governments, has been a top-down, principles-based approach for setting the agenda. However, such efforts are usually too abstract to engage with; everyone agrees models should be fair, but there is often disagreement on what "fair" means. In this work, I propose a bottom-up alternative: Evidence-based AI Ethics. Learning from other influential movements, such as Evidence-based Medicine, we can consider specific projects and examine them for "evidence." We draw from complementary critical lenses, one based on utilitarian ethics and on from intersectional feminism to analyze five case studies I have worked on, ranging from automatically-generated radiology reports to tech worker organizing.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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