Thesis Defense: Evidence-based AI Ethics

Speaker: William Boag , CSAIL

Date: Thursday, April 28, 2022

Time: 12:30 PM to 1:30 PM Note: all times are in the Eastern Time Zone

Public: Yes

Location: Hewlett room 32-G882 and

Event Type: Thesis Defense

Room Description:

Host: Peter Szolovits, CSAIL

Contact: Fern D Keniston,

Relevant URL:

Speaker URL: None

Speaker Photo:

Reminders to:,

Reminder Subject: TALK: Thesis Defense: Evidence-based AI Ethics

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.

Thesis Committee: Peter Szolovits, Marzyeh Ghassemi, Danny Weitzner, and Catherine D’Ignazio

Research Areas:

Impact Areas:

This event is not part of a series.

Created by Fern D Keniston Email at Tuesday, April 19, 2022 at 10:39 AM.