Human Perceptions of Fairness in Algorithmic Decision Making: A Case Study of Criminal Risk Prediction

By Nina Grgic-Hlaca, Elissa M. Redmiles, Krishna P. Gummadi, Adrian Weller. 2018

250 citations recorded by the source corpus.

As algorithms are increasingly used to make important decisions that affect human lives, ranging from social benefit assignment to predicting risk of criminal recidivism, concerns have been raised about the fairness of algorithmic decision making. Most prior works on algorithmic fairness normatively prescribe how fair decisions ought to be made. In contrast, here, we descriptively survey users for how they perceive and reason about fairness in algorithmic decision making. A key contribution of this work is the framework we propose to understand why people perceive certain features as fair or unfair to be used in algorithms. Our framework identifies eight properties of features, such as relevance, volitionality and reliability, as latent considerations that inform people»s moral judgments about the fairness of feature use in decision-making algorithms. We validate our framework through a series of scenario-based surveys with 576 people. We find that, based on a person»s assessment of the eight latent properties of a feature in our exemplar scenario, we can accurately (> 85%) predict if the person will judge the use of the feature as fair. Our findings have important implications. At a high-level, we show that people»s unfairness concerns are multi-dimensional and argue that future studies need to address unfairness concerns beyond discrimination. At a low-level, we find considerable disagreements in people»s fairness judgments. We identify root causes of the disagreements, and note possible pathways to resolve them.

Subfields: Cognitive Neuroscience, Safety Research

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Citations in the corpus

How Do Fairness Definitions Fare?: Examining Public Attitudes Towards Algorithmic Definitions of Fairness (2018, background): “…factors influence views on algorithmic fairness, Plane et al. (2017) explored human perceptions of discrimination in targeted online advertising, Grgić-Hlača et al. (2018a) and Grgić-Hlača et al. (2018b) studied human perceptions of features used in algorithmic decision making, and Binns et…”
Getting a CLUE: A Method for Explaining Uncertainty Estimates (2020, methodology): “We select the points used in our main survey with a pilot procedure similar to Grgic-Hlaca et al. (2018).”
Algorithmic Fairness (2020, positive): “This need is further supported by several recent studies that have addressed the question of how devised mathematical notions of fairness are perceived by users [63, 128].”
A Review on Fairness in Machine Learning (2022, unknown): “This need is further supported by several recent studies that have addressed the question of how devised mathematical notions of fairness are perceived by users [90, 192].”
Fair Generative Modeling via Weak Supervision (2019, background): “For future work, we would like to investigate conducting human evaluations to mitigate such issues during evaluation (Grgic-Hlaca et al., 2018).”
Algorithmic bias: review, synthesis, and future research directions (2021, background): “…algorithm or algorithmic decision as being fair, whereas procedural fairness conceptua-lises fairness perceptions with respect to internal processes of data-driven systems such as the rules and logics incorporated into algorithms (Binns et al., 2018; Grgić-Hlača et al., 2018; Robert et al., 2020).”