We review and reflect on fairness notions proposed in machine learning literature and make
an attempt to draw connections to arguments in moral and political philosophy, especially …

This review paper examines the ethical considerations and proposes solutions for ensuring
fairness in AI-driven financial services. Artificial intelligence (AI) technologies are …

Much attention has focused on algorithmic audits and impact assessments to hold
developers and users of algorithmic systems accountable. But existing algorithmic …

Privacy is a key principle for develo** ethical AI technologies, but how does including AI
technologies in products and services change privacy risks? We constructed a taxonomy of …

Machine learning systems require representations of the real world for training and testing-
they require data, and lots of it. Collecting data at scale has logistical and ethical challenges …

Predictive analytics methods in education are seeing widespread use and are producing
increasingly accurate predictions of students' outcomes. With the increased use of predictive …

The workplace has experienced extensive digital transformation, in part due to artificial
intelligence's commercial availability. Though still an emerging technology, emotional …

Inherent Limitations of AI Fairness Page 1 key insights ˽ The field of AI fairness aims to
measure and mitigate algorithmic discrimination, but the technical formalism this requires has …

Detecting, measuring and mitigating various measures of unfairness are core aims of
algorithmic fairness research. However, the most prominent approaches require access to …

The Less Discriminatory Alternative is a key provision of the disparate impact doctrine in the
United States. In fair lending, this provision mandates that lenders must adopt models that …