Open Access10.1145/3465416.3483305
A Framework for Understanding Sources of Harm throughout the Machine Learning Life Cycle
Harini Suresh,John V. Guttag +1 more
- 05 Oct 2021
TL;DR: In this paper, the authors identify seven potential sources of downstream harm in machine learning, spanning data collection, development, and deployment, and propose a framework to facilitate more productive and precise communication around these issues.
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Abstract: As machine learning (ML) increasingly affects people and society, awareness of its potential unwanted consequences has also grown. To anticipate, prevent, and mitigate undesirable downstream consequences, it is critical that we understand when and how harm might be introduced throughout the ML life cycle. In this paper, we provide a framework that identifies seven distinct potential sources of downstream harm in machine learning, spanning data collection, development, and deployment. In doing so, we aim to facilitate more productive and precise communication around these issues, as well as more direct, application-grounded ways to mitigate them.
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Development and preliminary testing of Health Equity Across the AI Lifecycle (HEAAL): A framework for healthcare delivery organizations to mitigate the risk of AI solutions worsening health inequities
Jee Young Kim,Alifia Hasan,Kate Kellogg,William Ratliff,Sara Murray,Harini Suresh,Alexandra Valladares,Keo Shaw,Danny Tobey,David E Vidal,Mark A. Lifson,Manesh R. Patel,Inioluwa Deborah Raji,William Boag,Linda Tang,Shems Saleh,Suresh Balu,Mark Sendak +17 more
TL;DR: The Health Equity Across the AI Lifecycle (HEAAL) guides how healthcare delivery organizations may mitigate the potential risk of AI solutions worsening health inequities and informs how much resources and support are required to assess the potential impact ofAI solutions on health inequity.
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The Ethics of Computational Social Science
Haofei YU
- 01 Jan 2023
TL;DR: In this article , a taxonomy of the ethical challenges faced by researchers in the field of Computational Social Science (CSS) is presented, focusing on the role that contextual considerations, anticipatory reflection, impact assessment, public engagement, and justifiable and well-documented action should play across the research lifecycle.
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TL;DR: This review focusses on the latest advancements in capsule endoscopy, analyzing the possible benefits and ethical challenges that artificial intelligence may bring to the field of minimally invasive capsule panendoscopy, while also offering insights into future directions.
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Positioning responsible learning analytics in the context of STEM identities of under-served students
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