104 citations · 104 across the 5 of their papers we have counts for
5 papers
Concrete Safety for ML Problems: System Safety for ML Development and Assessment
Edgar W. Jatho, Logan O. Mailloux, Eugene D. Williams +2
Many stakeholders struggle to make reliances on ML-driven systems due to the risk of harm these systems may cause. Concerns of trustworthiness, unintended social harms, and unaccep…
System Safety Engineering for Social and Ethical ML Risks: A Case Study
Edgar W. Jatho, Logan O. Mailloux, Shalaleh Rismani +2
Governments, industry, and academia have undertaken efforts to identify and mitigate harms in ML-driven systems, with a particular focus on social and ethical risks of ML component…
From plane crashes to algorithmic harm: applicability of safety engineering frameworks for responsible ML
Shalaleh Rismani, Renee Shelby, Andrew Smart +4
Inappropriate design and deployment of machine learning (ML) systems leads to negative downstream social and ethical impact -- described here as social and ethical risks -- for use…
Outlining Traceability: A Principle for Operationalizing Accountability in Computing Systems
Joshua A. Kroll
Accountability is widely understood as a goal for well governed computer systems, and is a sought-after value in many governance contexts. But how can it be achieved? Recent work o…
This Thing Called Fairness: Disciplinary Confusion Realizing a Value in Technology
Deirdre K. Mulligan, Joshua A. Kroll, Nitin Kohli +1
The explosion in the use of software in important sociotechnical systems has renewed focus on the study of the way technical constructs reflect policies, norms, and human values. T…