A review on fairness in machine learning

D Pessach, E Shmueli - ACM Computing Surveys (CSUR), 2022 - dl.acm.org
An increasing number of decisions regarding the daily lives of human beings are being
controlled by artificial intelligence and machine learning (ML) algorithms in spheres ranging …

Clinical artificial intelligence quality improvement: towards continual monitoring and updating of AI algorithms in healthcare

J Feng, RV Phillips, I Malenica, A Bishara… - NPJ digital …, 2022 - nature.com
Abstract Machine learning (ML) and artificial intelligence (AI) algorithms have the potential to
derive insights from clinical data and improve patient outcomes. However, these highly …

On the opportunities and risks of foundation models

R Bommasani, DA Hudson, E Adeli, R Altman… - arXiv preprint arXiv …, 2021 - arxiv.org
AI is undergoing a paradigm shift with the rise of models (eg, BERT, DALL-E, GPT-3) that are
trained on broad data at scale and are adaptable to a wide range of downstream tasks. We …

Explainable ai: A review of machine learning interpretability methods

P Linardatos, V Papastefanopoulos, S Kotsiantis - Entropy, 2020 - mdpi.com
Recent advances in artificial intelligence (AI) have led to its widespread industrial adoption,
with machine learning systems demonstrating superhuman performance in a significant …

Algorithmic bias: review, synthesis, and future research directions

N Kordzadeh, M Ghasemaghaei - European Journal of Information …, 2022 - Taylor & Francis
As firms are moving towards data-driven decision making, they are facing an emerging
problem, namely, algorithmic bias. Accordingly, algorithmic systems can yield socially …

Fairness in machine learning: A survey

S Caton, C Haas - ACM Computing Surveys, 2024 - dl.acm.org
When Machine Learning technologies are used in contexts that affect citizens, companies as
well as researchers need to be confident that there will not be any unexpected social …

Closing the AI accountability gap: Defining an end-to-end framework for internal algorithmic auditing

ID Raji, A Smart, RN White, M Mitchell… - Proceedings of the …, 2020 - dl.acm.org
Rising concern for the societal implications of artificial intelligence systems has inspired a
wave of academic and journalistic literature in which deployed systems are audited for harm …

Uncertainty as a form of transparency: Measuring, communicating, and using uncertainty

U Bhatt, J Antorán, Y Zhang, QV Liao… - Proceedings of the …, 2021 - dl.acm.org
Algorithmic transparency entails exposing system properties to various stakeholders for
purposes that include understanding, improving, and contesting predictions. Until now, most …

A snapshot of the frontiers of fairness in machine learning

A Chouldechova, A Roth - Communications of the ACM, 2020 - dl.acm.org
A snapshot of the frontiers of fairness in machine learning Page 1 82 COMMUNICATIONS OF
THE ACM | MAY 2020 | VOL. 63 | NO. 5 review articles ILL US TRA TION B Y JUS TIN METZ …

Machine behaviour

I Rahwan, M Cebrian, N Obradovich, J Bongard… - Nature, 2019 - nature.com
Abstract Machines powered by artificial intelligence increasingly mediate our social, cultural,
economic and political interactions. Understanding the behaviour of artificial intelligence …