Model multiplicity: Opportunities, concerns, and solutions

E Black, M Raghavan, S Barocas - … of the 2022 ACM Conference on …, 2022 - dl.acm.org
Recent scholarship has brought attention to the fact that there often exist multiple models for
a given prediction task with equal accuracy that differ in their individual-level predictions or …

Artificial intelligence and public human resource management: questions for research and practice

BAM Johnson, JD Coggburn… - Public Personnel …, 2022 - journals.sagepub.com
Advances in big data and artificial intelligence (AI), including machine learning (ML) and
other cognitive computing technologies (CCT), have facilitated the development of human …

Algorithmic auditing and social justice: Lessons from the history of audit studies

B Vecchione, K Levy, S Barocas - … of the 1st ACM Conference on Equity …, 2021 - dl.acm.org
“Algorithmic audits” have been embraced as tools to investigate the functioning and
consequences of sociotechnical systems. Though the term is used somewhat loosely in the …

Toward a theory of justice for artificial intelligence

I Gabriel - Daedalus, 2022 - direct.mit.edu
This essay explores the relationship between artificial intelligence and principles of
distributive justice. Drawing upon the political philosophy of John Rawls, it holds that the …

Disparate impact of artificial intelligence bias in ridehailing economy's price discrimination algorithms

A Pandey, A Caliskan - Proceedings of the 2021 AAAI/ACM Conference …, 2021 - dl.acm.org
Ridehailing applications that collect mobility data from individuals to inform smart city
planning predict each trip's fare pricing with automated algorithms that rely on artificial …

Taking algorithms to courts: A relational approach to algorithmic accountability

J Metcalf, R Singh, E Moss, E Tafesse… - Proceedings of the 2023 …, 2023 - dl.acm.org
In widely used sociological descriptions of how accountability is structured through
institutions, an “actor”(eg, the developer) is accountable to a “forum”(eg, regulatory …

Survey on fair reinforcement learning: Theory and practice

P Gajane, A Saxena, M Tavakol, G Fletcher… - arXiv preprint arXiv …, 2022 - arxiv.org
Fairness-aware learning aims at satisfying various fairness constraints in addition to the
usual performance criteria via data-driven machine learning techniques. Most of the …

Limitations of the" Four-Fifths Rule" and Statistical Parity Tests for Measuring Fairness

M Raghavan, PT Kim - Geo. L. Tech. Rev., 2024 - HeinOnline
Algorithmic tools have become increasingly common in a variety of social domains like
consumer finance, housing, employment, and criminal law enforcement. For example, in the …

The impact of nondiagnostic information on selection decision making: A cautionary note and mitigation strategies

DK Dalal, L Sassaman, XS Zhu - Personnel Assessment …, 2020 - scholarworks.bgsu.edu
Selection decision makers are inundated with information from which to make decisions
about the suitability of a job candidate for a position. Although some of this information is …

Rethinking Artificial Intelligence: Algorithmic Bias and Ethical Issues| How Process Experts Enable and Constrain Fairness in AI-Driven Hiring

IF Cruz - International Journal of Communication, 2023 - ijoc.org
Organizations risk losing their competitive edge as they struggle to find and hire qualified
talent. Hiring personnel turn to artificial intelligence (AI) tools to help acquire talent, increase …