Ai alignment: A comprehensive survey

J Ji, T Qiu, B Chen, B Zhang, H Lou, K Wang… - ar** a taxonomy for harm reduction
R Shelby, S Rismani, K Henne, AJ Moon… - Proceedings of the …, 2023 - dl.acm.org
Understanding the landscape of potential harms from algorithmic systems enables
practitioners to better anticipate consequences of the systems they build. It also supports the …

Beyond surveillance: privacy, ethics, and regulations in face recognition technology

X Wang, YC Wu, M Zhou, H Fu - Frontiers in big data, 2024 - frontiersin.org
Facial recognition technology (FRT) has emerged as a powerful tool for public governance
and security, but its rapid adoption has also raised significant concerns about privacy, civil …

Trauma-informed computing: Towards safer technology experiences for all

JX Chen, A McDonald, Y Zou, E Tseng… - Proceedings of the …, 2022 - dl.acm.org
Trauma is the physical, emotional, or psychological harm caused by deeply distressing
experiences. Research with communities that may experience high rates of trauma has …

Image representations learned with unsupervised pre-training contain human-like biases

R Steed, A Caliskan - Proceedings of the 2021 ACM conference on …, 2021 - dl.acm.org
Recent advances in machine learning leverage massive datasets of unlabeled images from
the web to learn general-purpose image representations for tasks from image classification …

Outsider oversight: Designing a third party audit ecosystem for ai governance

ID Raji, P Xu, C Honigsberg, D Ho - Proceedings of the 2022 AAAI/ACM …, 2022 - dl.acm.org
Much attention has focused on algorithmic audits and impact assessments to hold
developers and users of algorithmic systems accountable. But existing algorithmic …

Black-box access is insufficient for rigorous ai audits

S Casper, C Ezell, C Siegmann, N Kolt… - The 2024 ACM …, 2024 - dl.acm.org
External audits of AI systems are increasingly recognized as a key mechanism for AI
governance. The effectiveness of an audit, however, depends on the degree of access …