Moss, Emanuel

Participation Is not a Design Fix for Machine Learning

Sloane, M., Moss, E., Awomolo, O., & Forlano, L. (2022) · Equity and Access in Algorithms, Mechanisms, and Optimization

Ethics and Social Impacts of AIInnovative Human-Technology Interaction

Cited by 267

Algorithmic Impact Assessments and Accountability: The Co-construction of Impacts

Metcalf, J., Moss, E., Watkins, E. A., Singh, R., & Elish, M. C. (2021) · Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency

Ethics and Social Impacts of AIExplainable Artificial Intelligence (XAI)

Cited by 195

Taking Algorithms to Courts: A Relational Approach to Algorithmic Accountability

Metcalf, J., Singh, R., Moss, E., Tafesse, E., & Watkins, E. A. (2023) · 2023 ACM Conference on Fairness, Accountability, and Transparency

Ethics and Social Impacts of AIDigitalization, Law, and Regulation

Cited by 21

Tackling AI Hyping

Sloane, M., Danks, D., & Moss, E. (2024) · AI and Ethics

Ethics and Social Impacts of AIDigital Education and Society

Cited by 15

Introducing v0.5 of the AI Safety Benchmark from MLCommons

Vidgen, B., Agrawal, A., Ahmed, A. M., Akinwande, V., Al-Nuaimi, N., Alfaraj, N., Alhajjar, E., Aroyo, L., Bavalatti, T., Bartolo, M., et al. (2024) · arXiv

Ethics and Social Impacts of AIAdversarial Robustness in Machine Learning

Cited by 5