Aequitas: A bias and fairness audit toolkit
P Saleiro, B Kuester, L Hinkson, J London… - arXiv preprint arXiv …, 2018 - arxiv.org
Recent work has raised concerns on the risk of unintended bias in AI systems being used
nowadays that can affect individuals unfairly based on race, gender or religion, among other …
nowadays that can affect individuals unfairly based on race, gender or religion, among other …
A survey on bias and fairness in machine learning
With the widespread use of artificial intelligence (AI) systems and applications in our
everyday lives, accounting for fairness has gained significant importance in designing and …
everyday lives, accounting for fairness has gained significant importance in designing and …
A framework for fairness: A systematic review of existing fair ai solutions
B Richardson, JE Gilbert - arXiv preprint arXiv:2112.05700, 2021 - arxiv.org
In a world of daily emerging scientific inquisition and discovery, the prolific launch of
machine learning across industries comes to little surprise for those familiar with the …
machine learning across industries comes to little surprise for those familiar with the …
Effect of information presentation on fairness perceptions of machine learning predictors
The uptake of artificial intelligence-based applications raises concerns about the fairness
and transparency of AI behaviour. Consequently, the Computer Science community calls for …
and transparency of AI behaviour. Consequently, the Computer Science community calls for …
Fairlearn: Assessing and improving fairness of ai systems
Fairlearn is an open source project to help practitioners assess and improve fairness of
artificial intelligence (AI) systems. The associated Python library, also named fairlearn …
artificial intelligence (AI) systems. The associated Python library, also named fairlearn …
Fairness and bias in artificial intelligence: A brief survey of sources, impacts, and mitigation strategies
E Ferrara - Sci, 2023 - mdpi.com
The significant advancements in applying artificial intelligence (AI) to healthcare decision-
making, medical diagnosis, and other domains have simultaneously raised concerns about …
making, medical diagnosis, and other domains have simultaneously raised concerns about …
Towards intersectionality in machine learning: Including more identities, handling underrepresentation, and performing evaluation
Research in machine learning fairness has historically considered a single binary
demographic attribute; however, the reality is of course far more complicated. In this work …
demographic attribute; however, the reality is of course far more complicated. In this work …
Fairness in machine learning
Abstract Machine learning based systems are reaching society at large and in many aspects
of everyday life. This phenomenon has been accompanied by concerns about the ethical …
of everyday life. This phenomenon has been accompanied by concerns about the ethical …
Active fairness in algorithmic decision making
A Noriega-Campero, MA Bakker… - Proceedings of the …, 2019 - dl.acm.org
Society increasingly relies on machine learning models for automated decision making. Yet,
efficiency gains from automation have come paired with concern for algorithmic …
efficiency gains from automation have come paired with concern for algorithmic …
A translational perspective towards clinical AI fairness
Artificial intelligence (AI) has demonstrated the ability to extract insights from data, but the
fairness of such data-driven insights remains a concern in high-stakes fields. Despite …
fairness of such data-driven insights remains a concern in high-stakes fields. Despite …