Tackling Bias in Health AI Systems from a Human Rights Lens

The University of Toronto Joint Centre for Bioethics (JCB) has been a global leader in bioethics for over 20 years. We are an Ethics Observatory dedicated to addressing complex ethical issues of contemporary health systems through bioethics research, education and practice in partnership with other academic and health sector institutions. At the JCB, we put theory into practice.

University of Toronto Joint Centre for Bioethics

In this session, we explore the bias in health AI systems. I hope to discuss how AI algorithms can become biased and how it enter the system

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Full Title: Tackling bias in health AI systems from a human rights lensS﻿peakers: Jake Okechukwu Effoduh (He/Him)
Vanier Scholar, Osgoode Hall Law School
Y﻿ork UniversityAbstractThe healthcare industry is witnessing an era of innovation explosion, part of which is as a result of the increasing use of artificial intelligence (AI) within healthcare contexts. Although the technology is still relatively new, there are already some promising examples of AI systems improving diagnostics, treatment, and the speed of healthcare delivery. For example, some AI systems are remarkably predicting disease outbreaks, cancers, and heart diseases long before any signs or symptoms show. The use of AI is also advancing the practice of telemedicine, medical informatics, and is improving clinical operations (such as interpreting staining images and aiding the performance of high-risk surgeries). Many of these innovations are unprecedented. 

However, one of biggest challenges in the use of AI for healthcare is the issue of bias: instances when the application of an AI algorithm compounds existing inequities in socioeconomic status, race, ethnic background, religion, gender, disability, sexual orientation, or other criteria in ways that could amplify discrimination or adversely impact inequities in health systems. One of the many ways that this phenomenon of bias from AI health systems could occur is when an AI algorithm produces results that are systemically prejudiced due to erroneous assumptions in the machine learning process. 

In this session, I plan to speak on bias in health AI systems. I hope to discuss how AI algorithms can become biased, how they enter systems, and the harmful effects of algorithmic bias in AI for health care. Taking a critical human rights approach, I will also explore the legal and regulatory responses to bias in AI for health purposes.
Additional Details:The event is free and is open to the general public. The direct link to the seminar will be sent out to registered participants 2 hours before the event. The JCB Bioethics Seminars are now being live-streamed to our <a href="https://youtube.com/channel/UCV1FanK51llV351jZq5K-gA" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">YouTube channel</a>. If you don't receive the link 2 hours before the event, please head over there and click on the live stream. Subscribe to our channel to receive notices of upcoming events.Questions? Please email Terry Yuen, jcb.ea@utoronto.ca.

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Tackling Bias in Health AI Systems from a Human Rights Lens

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Tackling Bias in Health AI Systems from a Human Rights Lens

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