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Towards Robust and Reliable Algorithmic Recourse
As predictive models are increasingly being deployed in high-stakes deci...
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Towards a Unified Framework for Fair and Stable Graph Representation Learning
As the representations output by Graph Neural Networks (GNNs) are increa...
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Towards the Unification and Robustness of Perturbation and Gradient Based Explanations
As machine learning black boxes are increasingly being deployed in criti...
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Can I Still Trust You?: Understanding the Impact of Distribution Shifts on Algorithmic Recourses
As predictive models are being increasingly deployed to make a variety o...
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Does Fair Ranking Improve Minority Outcomes? Understanding the Interplay of Human and Algorithmic Biases in Online Hiring
Ranking algorithms are being widely employed in various online hiring pl...
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Robust and Stable Black Box Explanations
As machine learning black boxes are increasingly being deployed in real-...
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When Does Uncertainty Matter?: Understanding the Impact of Predictive Uncertainty in ML Assisted Decision Making
As machine learning (ML) models are increasingly being employed to assis...
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Ensuring Actionable Recourse via Adversarial Training
As machine learning models are increasingly deployed in high-stakes doma...
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Incorporating Interpretable Output Constraints in Bayesian Neural Networks
Domains where supervised models are deployed often come with task-specif...
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Interpretable and Interactive Summaries of Actionable Recourses
As predictive models are increasingly being deployed in high-stakes deci...
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How Much Should I Trust You? Modeling Uncertainty of Black Box Explanations
As local explanations of black box models are increasingly being employe...
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Fair Influence Maximization: A Welfare Optimization Approach
Several social interventions (e.g., suicide and HIV prevention) leverage...
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"How do I fool you?": Manipulating User Trust via Misleading Black Box Explanations
As machine learning black boxes are increasingly being deployed in criti...
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How can we fool LIME and SHAP? Adversarial Attacks on Post hoc Explanation Methods
As machine learning black boxes are increasingly being deployed in domai...
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Interpretable & Explorable Approximations of Black Box Models
We propose Black Box Explanations through Transparent Approximations (BE...
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Identifying Unknown Unknowns in the Open World: Representations and Policies for Guided Exploration
Predictive models deployed in the real world may assign incorrect labels...
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Learning Cost-Effective Treatment Regimes using Markov Decision Processes
Decision makers, such as doctors and judges, make crucial decisions such...
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