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Algorithmic decision-making systems are increasingly used throughout the public and private sectors to make important decisions or assist humans in making these decisions with real social consequences. While there has been substantial research in recent years to build fair decision-making algorithms, there has been less research seeking to understand the factors that affect people's perceptions of fairness in these systems, which we argue is also important for their broader acceptance. In this research, we conduct an online experiment to better understand perceptions of fairness, focusing on three sets of factors: algorithm outcomes, algorithm development and deployment procedures, and individual differences. We find that people rate the algorithm as more fair when the algorithm predicts in their favor, even surpassing the negative effects of describing algorithms that are very biased against particular demographic groups. We find that this effect is moderated by several variables, including participants' education level, gender, and several aspects of the development procedure. Our findings suggest that systems that evaluate algorithmic fairness through users' feedback must consider the possibility of "outcome favorability" bias.
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Analyzing the impact of missing values and selection bias on fairness
Abstract Algorithmic decision making is becoming more prevalent, increasingly impacting people’s daily lives. Recently, discussions have been emerging about the fairness of decisions made by machines. Researchers have proposed different approaches for improving the fairness of these algorithms. While these approaches can help machines make fairer decisions, they have been developed and validated on fairly clean data sets. Unfortunately, most real-world data have complexities that make them more dirty . This work considers two of these complexities by analyzing the impact of two real-world data issues on fairness—missing values and selection bias—for categorical data. After formulating this problem and showing its existence, we propose fixing algorithms for data sets containing missing values and/or selection bias that use different forms of reweighting and resampling based upon the missing value generation process. We conduct an extensive empirical evaluation on both real-world and synthetic data using various fairness metrics, and demonstrate how different missing values generated from different mechanisms and selection bias impact prediction fairness, even when prediction accuracy remains fairly constant.
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- PAR ID:
- 10280383
- Date Published:
- Journal Name:
- International Journal of Data Science and Analytics
- ISSN:
- 2364-415X
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1007/s41060-021-00259-z
