Open AccessPosted Content
Algorithmic Fairness
Dana Pessach,Erez Shmueli +1 more
TL;DR: An overview of the main concepts of identifying, measuring and improving algorithmic fairness when using AI algorithms is presented and the most commonly used fairness-related datasets in this field are described.
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Abstract: An increasing number of decisions regarding the daily lives of human beings are being controlled by artificial intelligence (AI) algorithms in spheres ranging from healthcare, transportation, and education to college admissions, recruitment, provision of loans and many more realms. Since they now touch on many aspects of our lives, it is crucial to develop AI algorithms that are not only accurate but also objective and fair. Recent studies have shown that algorithmic decision-making may be inherently prone to unfairness, even when there is no intention for it. This paper presents an overview of the main concepts of identifying, measuring and improving algorithmic fairness when using AI algorithms. The paper begins by discussing the causes of algorithmic bias and unfairness and the common definitions and measures for fairness. Fairness-enhancing mechanisms are then reviewed and divided into pre-process, in-process and post-process mechanisms. A comprehensive comparison of the mechanisms is then conducted, towards a better understanding of which mechanisms should be used in different scenarios. The paper then describes the most commonly used fairness-related datasets in this field. Finally, the paper ends by reviewing several emerging research sub-fields of algorithmic fairness.
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Figures
Table 1. Measures and Definitions for Algorithmic Fairness 
Table 4. Additional Measures and Definitions for Algorithmic Fairness 
Fig. 1. If the SAT scores were used for hiring, then unprivileged candidates with high potential would be excluded, whereas lower potential candidates from the privileged group would be hired instead 
Table 3. Common Benchmark Datasets for Algorithmic Fairness 
Table 2. Pre-Process, In-Process and Post-Process Mechanisms for Algorithmic Fairness
Citations
Fairness in Contextual Resource Allocation Systems: Metrics and Incompatibility Results
Nathanael Jo,Bill Tang,Kathryn Dullerud,Sina Aghaei,Eric R. Rice,Phebe Vayanos +5 more
- 04 Dec 2022
TL;DR: In this article , the authors propose a framework for evaluating fairness in contextual resource allocation systems that is inspired by fairness metrics in machine learning, which can be applied to evaluate the fairness properties of a historical policy, as well as to impose constraints in the design of new allocation policies.
Mitigating Algorithmic Bias with Limited Annotations
TL;DR: According to the evaluation on benchmark datasets, APOD outperforms the state-of-the-arts baseline methods under the limited annotation budget, and shows comparable performance to fully annotated bias mitigation, which demonstrates that APOD could benefit real-world applications when sensitive information is limited.
Fairness and Bias in Robot Learning
TL;DR: This work presents the first survey on fairness in robot learning from an interdisciplinary perspective spanning technical, ethical, and legal challenges, and proposes a taxonomy for sources of bias and the resulting types of discrimination due to them.
•Posted Content
A Possibility in Algorithmic Fairness: Calibrated Scores for Fair Classifications
TL;DR: This paper derives necessary and sufficient conditions for the existence of calibrated scores that yield classifications achieving equal error rates, and presents an algorithm that searches for the most informative score subject to both calibration and minimal error rate disparity.
4
Uncovering the Source of Machine Bias
TL;DR: A structural econometric model to capture the decision dynamics of human evaluators on an online micro-lending platform and it is found that machine learning algorithms can mitigate both the preferencebased bias and the belief-based bias.
4
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