In this article, we describe the influence of violations of community standards of fairness (Kahneman, Knetsch, and Thaler, 1986a) on subsequent ethical decision-making and emotions. Across two studies, we manipulated explanations for a common action, and we find that explanations...
Conversely, when we act unfairly, we can experience guilt, anxiety, and shame, leading to emotional distress and poor mental health.Promoting a sense of fairness in society requires a multifaceted approach that encompasses education, legislation, and social norms. Schools should teach children about ...
This study highlights the need for enhanced information and education on algorithmic fairness to support informed decision-making in using these systems. Furthermore, the results suggest that a one-size-fits-all approach to fairness may be insufficient, pointing to the importance of context-sensitive...
From a comprehensive perspective, English and Chinese literature concur that “algorithmic fairness” is a simplified approach to achieving “fairness” through technical means, by extracting core elements and quantifying them. To attain “fairness in medical AI” in specific scenarios, we begin with ...
The other is to evaluate whether our approach can be generalized to other diseases that are of worldwide concern to alleviate the unfairness brought about by AI. Methods Ethical approval for the study was obtained from the Ethics Committee of Eye & ENT Hospital, Fudan University, Shanghai, ...
For example, if the mechanism is an AI that is used to make recommendations to the vendor, it does not account for the vendor injecting additional bias into a subsequent decision. If appropriate for a particular application, human decision making can also be factored into the “mechanism,” ...
et al. A reductions approach to fair classification. In Int. Conf. Machine Learning 60–69 (PMLR, 2018). Corbett-Davies, S., Pierson, E., Feller, A., Goel, S. & Huq, A. Algorithmic decision making and the cost of fairness. In Proc. 23rd ACM SIGKDD Int. Conf. Knowledge ...
posit that a transparent model allows users to see its inner workings and gauge how it arrived at a decision, but others hold that while one may be able to look inside a model, the tool may be so complicated that a human would not be able to gain insights into its decisi...
a result, clinicians may find that the algorithm tends to underdiagnose specific sub-populations, and the corresponding treatment for each group might also differ. This phenomenon will make the clinician confused about clinical decision-making and wonder why the AI algorithms perform unfairly like ...
Loosely speaking, a small distance in feature space (i.e. similar individuals) must correspond to a small distance in decision space (i.e. similar outcomes). They also propose an approach to enforce such kind of fairness by introducing a constrained optimization at training time, once a ...