On Fairness, Diversity and Randomness in Algorithmic Decision Making

Consider a binary decision making process where a single machine learning classifier replaces a multitude of humans. We raise questions about the resulting loss of diversity in the decision making process. We study the potential benefits of using random classifier ensembles instead of a single classifier in the context of fairness-aware learning and demonstrate various attractive properties: (i) an ensemble of fair classifiers is guaranteed to be fair, for several different measures of fairness, (ii) an ensemble of unfair classifiers can still achieve fair outcomes, and (iii) an ensemble of classifiers can achieve better accuracy-fairness trade-offs than a single classifier. Finally, we introduce notions of distributional fairness to characterize further potential benefits of random classifier ensembles

Machine Advice with a Warning about Machine Limitations. Experimentally Testing the Solution Mandated by the Wisconsin Supreme Court

The Wisconsin Supreme Court allows machine advice in the courtroom only if accompanied by a series of warnings. We test 878 US lay participants with jury experience on fifty past cases where we know ground truth. The warnings affect their estimates of the likelihood of recidivism and their confidence, but not their decision whether to grant bail. Participants do not get better at identifying defendants who recidivated during the next two years. Results are essentially the same if participants are warned in easily accessible language, and if they are additionally informed about the low accuracy of machine predictions. The decision to grant bail is also unaffected by the warnings mandated by the Supreme Court if participants do not first decide without knowing the machine prediction. Oversampling cases where defendants committed violent crime does not change results either, whether coupled with machine predictions for general or for violent crime. Giving participants feedback and incentivizing them for finding ground truth has a small, weakly significant effect. The effect becomes significant at conventional levels when additionally using strong graphical warnings. Then participants are less likely to follow the advice. But the effect is counterproductive: they follow the advice less if it actually is closer to ground truth

Taking Advice from (Dis)Similar Machines: {T}he Impact of Human-Machine Similarity on Machine-Assisted Decision-Making

Machine learning algorithms are increasingly used to assist human decision-making. When the goal of machine assistance is to improve the accuracy of human decisions, it might seem appealing to design ML algorithms that complement human knowledge. While neither the algorithm nor the human are perfectly accurate, one could expect that their complementary expertise might lead to improved outcomes. In this study, we demonstrate that in practice decision aids that are not complementary, but make errors similar to human ones may have their own benefits. In a series of human-subject experiments with a total of 901 participants, we study how the similarity of human and machine errors influences human perceptions of and interactions with algorithmic decision aids. We find that (i) people perceive more similar decision aids as more useful, accurate, and predictable, and that (ii) people are more likely to take opposing advice from more similar decision aids, while (iii) decision aids that are less similar to humans have more opportunities to provide opposing advice, resulting in a higher influence on people’s decisions overall

Dimensions of Diversity in Human Perceptions of Algorithmic Fairness

Algorithms are increasingly involved in making decisions that affect human lives. Prior work has explored how people believe algorithmic decisions should be made, but there is little understanding of which individual factors relate to variance in these beliefs across people. As an increasing emphasis is put on oversight boards and regulatory bodies, it is important to understand the biases that may affect human judgements about the fairness of algorithms. Building on factors found in moral foundations theory and egocentric fairness literature, we explore how people's perceptions of fairness relate to their (i) demographics (age, race, gender, political view), and (ii) personal experiences with the algorithmic task being evaluated. Specifically, we study human beliefs about the fairness of using different features in an algorithm designed to assist judges in making decisions about granting bail. Our analysis suggests that political views and certain demographic factors, such as age and gender, exhibit a significant relation to people's beliefs about fairness. Additionally, we find that people beliefs about the fairness of using demographic features such as age, gender and race, for making bail decisions about others, vary egocentrically: that is they vary depending on their own age, gender and race respectively

Beyond Distributive Fairness in Algorithmic Decision Making: {F}eature Selection for Procedurally Fair Learning

With widespread use of machine learning methods in numerous domains involving humans, several studies have raised questions about the potential for unfairness towards certain individuals or groups. A number of recent works have proposed methods to measure and eliminate unfairness from machine learning models. However, most of this work has focused on only one dimension of fair decision making: distributive fairness, i.e., the fairness of the decision outcomes. In this work, we leverage the rich literature on organizational justice and focus on another dimension of fair decision making: procedural fairness, i.e., the fairness of the decision making process. We propose measures for procedural fairness that consider the input features used in the decision process, and evaluate the moral judgments of humans regarding the use of these features. We operationalize these measures on two real world datasets using human surveys on the Amazon Mechanical Turk (AMT) platform, demonstrating that our measures capture important properties of procedurally fair decision making. We provide fast submodular mechanisms to optimize the tradeoff between procedural fairness and prediction accuracy. On our datasets, we observe empirically that procedural fairness may be achieved with little cost to outcome fairness, but that some loss of accuracy is unavoidable

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

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