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Found 4 papers, 1 papers with code
Addressing multiple metrics of group fairness in data-driven decision making
The Fairness, Accountability, and Transparency in Machine Learning (FAT-ML) literature proposes a varied set of group fairness metrics to measure discrimination against socio-demographic groups that are characterized by a protected feature, such as gender or race. Such a system can be deemed as either fair or unfair depending on the choice of the metric.
Fair and Unbiased Algorithmic Decision Making: Current State and Future Challenges
Machine learning algorithms are now frequently used in sensitive contexts that substantially affect the course of human lives, such as credit lending or criminal justice.
A multidisciplinary task-based perspective for evaluating the impact of AI autonomy and generality on the future of work
This paper presents a multidisciplinary task approach for assessing the impact of artificial intelligence on the future of work.
Assessing the impact of machine intelligence on human behaviour: an interdisciplinary endeavour
The workshop gathered an interdisciplinary group of experts to establish the state of the art research in the field and a list of future research challenges to be addressed on the topic of human and machine intelligence, algorithm's potential impact on human cognitive capabilities and decision making, and evaluation and regulation needs.
