Finocchiaro J, Abebe R, Shirali A.
Participatory Objective Design via Preference Elicitation, in
Fairness, Accountability, and Transparency (FAccT). Rio de Janeiro: ACM ; Forthcoming.
Abstract
In standard resource allocation problems, the designer sets the objective---such as utilitarian social welfare---that captures a societal goal and solves for the optimal allocation subject to fairness and item availability constraints. The participants, on the other hand, specify their preferences for the items being allocated, e.g., through stating how they rank the items or expressing their cardinal utility for each item. The objective function, which guides the overall allocation, is therefore determined by the designer in a top-down manner, whereas participants can only express their preferences for the items. This standard preference elicitation stage limits participants' ability to express preferences for the overall allocation, such as the level of inequality, and influence the overall objective function.
In this work, we examine whether it is possible to use this bottom-up preference elicitation stage to enable participants to express not only their preferences for individual items but also their preferences for the overall allocation, thereby indirectly influencing the objective function. We examine this question using a well-studied resource allocation problem where mm divisible items must be allocated to nn agents, who express their cardinal utilities over the items. The designer aims to optimize for the sum of the agents' utilities for the items they receive. In particular, this utilitarian objective is agnostic to the overall inequality level. We consider a setting where the agents' true utility is a function not only of their preferences for the items, but also the overall level of inequality. We model this using a popular social preference model from behavioral economics by \citeauthor{fehr1999theory}, where agents can express levels of inequality aversion.
We conduct a theoretical examination of this problem and show that there can be large gains in social welfare if the designer uses this richer inequality-aware preference model, instead of the standard inequality-agnostic preference model. Further, if we take the standard inequality-agnostic welfare as the benchmark, we show that the relative loss of welfare can be tightly bounded--shown to be independent of the number of agents and linear in the level of inequality aversion. With further assumptions on the preferences, we provide strictly tighter, distribution-free, and parametric bounds on the loss of welfare. We also discuss the worst-case drop in inequality-agnostic utility an agent might incur as a consequence of a designer allocating items using the inequality-averse preferences. We conclude with a discussion on possible designs to elicit the preferences of strategic agents over the goods and fairness. Taken together, our results argue for potentially large gains that can be obtained from using the richer social preference model and demonstrate the relatively minor losses from using the standard model, highlighting a promising avenue for using preference elicitation to empower participants to influence the overall objective function.
Gowda S, Joshi S, Zhang H, Ghassemi M.
Pulling Up by the Causal Bootstraps: Causal Data Augmentation for Pre-training Debiasing. Forthcoming.
AbstractMachine learning models achieve state-of-the-art performance on many supervised learning tasks. However, prior evidence suggests that these models may learn to rely on shortcut biases or spurious correlations (intuitively, correlations that do not hold in the test as they hold in train) for good predictive performance. Such models cannot be trusted in deployment environments to provide accurate predictions. While viewing the problem from a causal lens is known to be useful, the seamless integration of causation techniques into machine learning pipelines remains cumbersome and expensive. In this work, we study and extend a causal pre-training debiasing technique called causal bootstrapping (CB) under five practical confounded-data generation-acquisition scenarios (with known and unknown confounding). Under these settings, we systematically investigate the effect of confounding bias on deep learning model performance, demonstrating their propensity to rely on shortcut biases when these biases are not properly accounted for. We demonstrate that such a causal pre-training technique can significantly outperform existing base practices to mitigate confounding bias on real-world domain generalization benchmarking tasks. This systematic investigation underlines the importance of accounting for the underlying data-generating mechanisms and fortifying data-preprocessing pipelines with a causal framework to develop methods robust to confounding biases.
Singh H, Joshi S, Doshi-Velez F, Lakkaraju H.
Learning under adversarial and interventional shifts. Forthcoming.
Publisher's VersionAbstractMachine learning models are often trained on data from one distribution and deployed on others. So it becomes important to design models that are robust to distribution shifts. Most of the existing work focuses on optimizing for either adversarial shifts or interventional shifts. Adversarial methods lack expressivity in representing plausible shifts as they consider shifts to joint distributions in the data. Interventional methods allow more expressivity but provide robustness to unbounded shifts, resulting in overly conservative models. In this work, we combine the complementary strengths of the two approaches and propose a new formulation, RISe, for designing robust models against a set of distribution shifts that are at the intersection of adversarial and interventional shifts. We employ the distributionally robust optimization framework to optimize the resulting objective in both supervised and reinforcement learning settings. Extensive experimentation with synthetic and real world datasets from healthcare demonstrate the efficacy of the proposed approach.
Parbhoo S, Joshi S, Doshi-Velez F.
Generalizing Off-Policy Evaluation From a Causal Perspective For Sequential Decision-Making. Forthcoming.
Publisher's VersionAbstract Assessing the effects of a policy based on observational data from a different policy is a common problem across several high-stake decision-making domains, and several off-policy evaluation (OPE) techniques have been proposed. However, these methods largely formulate OPE as a problem disassociated from the process used to generate the data (i.e. structural assumptions in the form of a causal graph). We argue that explicitly highlighting this association has important implications on our understanding of the fundamental limits of OPE. First, this implies that current formulation of OPE corresponds to a narrow set of tasks, i.e. a specific causal estimand which is focused on prospective evaluation of policies over populations or sub-populations. Second, we demonstrate how this association motivates natural desiderata to consider a general set of causal estimands, particularly extending the role of OPE for counterfactual off-policy evaluation at the level of individuals of the population. A precise description of the causal estimand highlights which OPE estimands are identifiable from observational data under the stated generative assumptions. For those OPE estimands that are not identifiable, the causal perspective further highlights where more experimental data is necessary, and highlights situations where human expertise can aid identification and estimation. Furthermore, many formalisms of OPE overlook the role of uncertainty entirely in the estimation process. We demonstrate how specifically characterising the causal estimand highlights the different sources of uncertainty and when human expertise can naturally manage this uncertainty. We discuss each of these aspects as actionable desiderata for future OPE research at scale and in-line with practical utility.
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