Trustworthy machine learning (ML) encompasses multiple fields of research, including (but not limited to) robustness, algorithmic fairness, interpretability and privacy. Recently, relationships between techniques and metrics used across different fields of trustworthy ML have emerged, leading to interesting ...
Non-empirical problems in fair machine learning Article Open access 05 August 2021 Is it still fair? A comparative evaluation of fairness algorithms through the lens of covariate drift Article Open access 14 January 2025 Explore related subjects Discover the latest articles, news and stories from...
In this Perspective, we outline fairness in machine learning through the lens of healthcare, and discuss how algorithmic biases (in data acquisition, genetic variation and intra-observer labelling variability, in particular) arise in clinical workflows and the resulting healthcare disparities. We also...
Relatedly, algorithmic decision-making entails exercising power as well. Decisions must be made throughout the entire model building and deployment process, from collecting and processing data to developing the algorithmic model to hyperparameter tuning and choosing the fairness metric. At each step, va...
However, online portals have been associated with controversial issues such as fairness in news editing and manipulated comments on the news. Thus, user attitudes toward NAVER may be influenced by their daily use of its various services, and perceptions of positive or negative issues related to ...
its fairness. As Kiviat (2019, p. 1151) writes consistently in reference to creditscoring: “Algorithmic prediction is imbued with normative viewpoints—theyare viewpoints that suit the goals of corporations”—and the goals of securityagencies, one might add.Before this backdrop, the study of ...
1.1Algorithmic Fairness and Its Discontents Machine learning algorithms have become central components in many efforts to promote equitable public policy. In the face of widespread concerns about discriminatory institutions and decision-making processes, many policymakers, policy advocates, and scholars praise...
Recently, scholars have begun investigating not only the harms and discrimination created by ADM but also how individuals perceive decisions that rely on algorithms. This work has paralleled existing theory on the perceptions of fairness, trust, and legitimacy of firms and decisions. ...
Racial and ethnic minorities have borne a particularly acute burden of the COVID-19 pandemic in the United States. There is a growing awareness from both researchers and public health leaders of the critical need to ensure fairness in forecast results. W