ABSTRACT. We tackle the problem of building explainable recommendation systems that are based on a per-user decision tree, with decision rules that are based on single attribute values. We build the trees by applying learned regression functions to obtain the decision rules as well as the values at the leaf nodes. The regression functions receive as input the embedding of the user’s training set, as well as the embedding of the samples that arrive at the current node. The embedding and the regressors are learned end-to-end with a loss that encourages the decision rules to be sparse. By applying our method, we obtain a collaborative filtering solution that provides a direct explanation to every rating it provides. With regards to accuracy, it is competitive with other algorithms. However, as expected, explainability comes at a cost and the accuracy is typically slightly lower than the state of the art result reported in the literature. Our code is anonymously available at https://drive.google.com/file/d/1Yfs_cpAT3OKadaYo_5p25PaxFKvkFaP1/view?usp=sharing.

ABSTRACT. Given AI’s growing role in modeling and improving decision-making, how and when to present users with feedback is an urgent topic to address. We empirically examined the effect of feedback from false AI on moral decision-making about donor kidney allocation. We found some evidence that judgments about whether a patient should receive a kidney can be influenced by feedback about participants’ own decision-making perceived to be given by AI, even if the feedback is entirely random. We also discovered different effects between assessments presented as being from human experts and assessments presented as being from AI.

ABSTRACT. Fair decision-making in criminal justice relies on the recognition and incorporation of infinite shades of grey. In this paper, we detail how algorithmic risk assessment tools are counteractive to fair legal proceedings in social institutions where desired states of the world are contested ethically and practically. We provide a normative framework for assessing fair judicial decision-making, one that does not seek the elimination of human bias from decision-making as algorithmic fairness efforts currently focus on, but instead centers on sophisticating the incorporation of individualized or discretionary bias–a process that is requisitely human. Through analysis of a case study on social disadvantage, we use this framework to provide an assessment of potential features of consideration, such as political disempowerment and demographic exclusion, that are irreconcilable by current algorithmic efforts and recommend their incorporation in future reform.

ABSTRACT. Machine learning models are prone to biased decisions due to biases in the datasets they are trained on. In this paper, we introduce a novel data augmentation technique to create a fairer dataset for model training that could also lend itself to understanding the type of bias existing in the dataset i.e. if bias arises just from a lack of representation for a particular group (sampling bias) or if it arises because of human bias reflected in the labels (prejudice based bias). Given a dataset involving a protected attribute with a privileged and unprivileged group, we create an “ideal world” dataset: for every data sample, we create a new sample having the same features and label as the original sample but with the opposite protected attribute value. The synthetic data points are sorted in order of their proximity to the original training distribution and added successively to the real dataset to create intermediate datasets. We theoretically show that two different notions of fairness: statistical parity difference (independence) and average odds difference (separation) always change in the same direction using such an augmentation. We also show submodularity of the proposed fairness-aware augmentation approach that enables an efficient greedy algorithm. We empirically study the effect of training models on the intermediate datasets and show that this technique reduces the two bias measures while keeping the accuracy nearly constant for three datasets. We then discuss the implications of this study on the disambiguation of sample bias and prejudice based bias and discuss how pre-processing techniques should be evaluated in general. The proposed method can be used by policy makers who want to use unbiased datasets to train machine learning models for their applications to add a subset of synthetic points to an extent that they are comfortable with to mitigate unwanted bias.

ABSTRACT. To reduce human error and prejudice, many high-stakes decisions have been turned over to machine algorithms. However, recent research suggests that this \emph{does not} remove discrimination, and can perpetuate harmful stereotypes. While algorithms have been developed to improve fairness, they typically face at least one of three shortcomings: they are not interpretable, their prediction quality deteriorates quickly compared to unbiased equivalents, and %the methodology cannot easily extend other algorithms they are not easily transferable across models% (e.g., methods to reduce bias in random forests cannot be extended to neural networks) . To address these shortcomings, we propose a geometric method that removes correlations between data and any number of protected variables. Further, we can control the strength of debiasing through an adjustable parameter to address the trade-off between prediction quality and fairness. The resulting features are interpretable and can be used with many popular models, such as linear regression, random forest, and multilayer perceptrons. The resulting predictions are found to be more accurate and fair compared to several state-of-the-art fair AI algorithms across a variety of benchmark datasets. Our work shows that debiasing data is a simple and effective solution toward improving fairness.

ABSTRACT. Today, AI is increasingly being used in many high-stakes decision-making applications in which fairness is an important concern. Already, there are many examples of AI being biased and making questionable and unfair decisions. The AI research community has proposed many methods to measure and mitigate unwanted biases, but few of them involve inputs from human policy makers. We argue that because different fairness criteria sometimes cannot be simultaneously satisfied, and because achieving fairness often requires sacrificing other objectives such as model accuracy, it is key to acquire and adhere to human policy makers’ preferences on how to make the tradeoff among these objectives. In this paper, we propose a framework and some exemplar methods for eliciting such preferences and for optimizing an AI model according to these preferences.

ABSTRACT. We propose a framework for analyzing and evaluating system-wide algorithmic fairness. The core idea is to use simulation techniques in order to extend the scope of current fairness assessments by incorporating context and feedback to a phenomenon of interest. By doing so, we expect to better understand the interaction among the social behavior giving rise to discrimination, automated decision making tools, and fairness-inspired statistical constraints. In particular, we invite the community to use agent based models as an explanatory tool for causal mechanisms of population level properties. We also propose embedding these into a reinforcement learning algorithm to find optimal actions for meaningful change. As an incentive for taking a system-wide approach , we show through a simple model of predictive policing and trials that if we limit our attention to one portion of the system, we may determine some blatantly unfair practices as fair, and be blind to overall unfairness.

ABSTRACT. As machine intelligence is increasingly incorporated into educational technologies, it becomes imperative for instructors and students to understand the potential flaws of the algorithms on which their systems rely. This paper describes the design and implementation of an interactive post-hoc explanation of the Bayesian Knowledge Tracing algorithm which is implemented in learning analytics systems used across the United States. After a user-centered design process to smooth out interaction design difficulties, we ran a controlled experiment to evaluate whether the interactive or `static’ version of the explainable led to increased learning. Our results reveals that learning about an algorithm through an explainable depends on users’ educational background. For other contexts, designers of post-hoc explainables must consider their users’ educational background to best determine how to empower more informed decision-making with AI-enhanced systems.

ABSTRACT. Computer scientists have made great strides in characterizing different measures of algorithmic fairness, and showing that certain measures of fairness cannot be jointly satisfied. In this paper, I argue that the two most popular families of measures – target-conditional and score-conditional independence are actually best thought of as measures of the injustice of the contexts in which they are deployed. I begin by introducing three different ways of measuring bias – independence, prediction-conditional error and target-conditional error – and dis-cuss the implicit idealizations these measures make about the underlying causal structure of the contexts in which they are deployed. I then discuss three ways in which these idealizations fall apart in the con-text of deployment in an unjust world. In the final section, I suggest an alternative framework for measuring fairness in the context of existing injustice: justice-sensitive independence.

ABSTRACT. Work in Counterfactual Explanations tends to focus on the principle of “the closest possible world” that identifies small changes leading to the desired outcome. In this paper we argue that while this approach might initially seem intuitively appealing it exhibits shortcomings not addressed in the current literature. First, a counterfactual example generated by the state-of-the-art systems is not necessarily representative of the underlying data distribution, and may therefore prescribe unachievable goals (e.g., an unsuccessful life insurance applicant with severe disability may be advised to do more sports).Secondly, the counterfactuals may not be based on a “feasible path” between the current state of the subject and the suggested one, making actionable recourse infeasible (e.g., low-skilled unsuccessful mortgage applicants may be told to double their salary, which may be hard without first increasing their skill level). These two shortcomings may render counterfactual explanations impractical. To address these we propose a new line of Counterfactual Explanations research aimed at providing actionable and feasible paths to transform a selected instance into one that meets a certain goal. We address this challenge with FACE: an algorithmically sound way of uncovering these“feasible paths” based on the shortest path distances defined via density-weighted metrics. Our approach generates counterfactuals that are coherent with the underlying data distribution and supported by the “feasible paths” of change, which are achievable and can be tailored to the problem at hand.

ABSTRACT. In many judicial systems — including the United States courts of appeals, the European Court of Justice, the UK Supreme Court and the Supreme Court of Canada — for each case, a subset of judges is selected from the entire judicial body in order to hear the arguments and decide the judgment. Ideally, the subset selected is \emph{representative}, i.e., the decision of the subset would match what the decision of the entire judicial body would have been had they all weighed in on the case. Further, the process should be \emph{fair} in that all judges should have similar workloads, and the selection process should not allow for certain judge’s opinions to be silenced or amplified via case assignments. Lastly, in order to be practical and trustworthy, the process should also be \emph{interpretable}, easy to use, and (if algorithmic) computationally \emph{efficient}. In this paper, we propose an algorithmic method for the judicial subset selection problem that satisfies all of the above criteria. The method satisfies fairness by design, and we prove that it has optimal representativeness asymptotically for a large range of parameters and under noisy information models about judge opinions — something no existing methods can provably achieve. We then assess the benefits of our approach empirically by counterfactually comparing against the current practice and recent alternative algorithmic approaches using cases from the United States courts of appeals Database.

ABSTRACT. Numerous AI-assisted resource allocation decisions need to balance the conflicting goals of fairness and efficiency. Our paper studies the challenging task of defining and modeling a proper fairness-efficiency trade off. We define fairness with Rawlsian leximax fairness, which views the lexicographic maximum among all feasible outcomes as the most equitable; and define efficiency with Utilitarianism, which seeks to maximize the sum of utilities received by entities regardless of individual differences. Motivated by a justice-driven trade off principle: prioritize fairness to benefit the less advantaged unless too much efficiency is sacrificed, we propose a sequential optimization procedure to balance leximax fairness and utilitarianism in decision-making. Each iteration of our approach maximizes a social welfare function, and we provide a practical mixed integer/linear programming (MILP) formulation for each maximization problem. We illustrate our method on a budget allocation example. Compared with existing approaches of balancing equity and efficiency, our method is more interpretable in terms of parameter selection, and incorporates a strong equity criterion with a thoroughly balanced perspective.