Search Results for author: Sophie Hilgard

Found 8 papers, 2 papers with code

Feature Attributions and Counterfactual Explanations Can Be Manipulated

no code implementations23 Jun 2021 Dylan Slack, Sophie Hilgard, Sameer Singh, Himabindu Lakkaraju

As machine learning models are increasingly used in critical decision-making settings (e. g., healthcare, finance), there has been a growing emphasis on developing methods to explain model predictions.

BIG-bench Machine Learning counterfactual +1

Counterfactual Explanations Can Be Manipulated

no code implementations NeurIPS 2021 Dylan Slack, Sophie Hilgard, Himabindu Lakkaraju, Sameer Singh

In this work, we introduce the first framework that describes the vulnerabilities of counterfactual explanations and shows how they can be manipulated.

counterfactual Counterfactual Explanation +1

Does Fair Ranking Improve Minority Outcomes? Understanding the Interplay of Human and Algorithmic Biases in Online Hiring

no code implementations1 Dec 2020 Tom Sühr, Sophie Hilgard, Himabindu Lakkaraju

In this work, we analyze various sources of gender biases in online hiring platforms, including the job context and inherent biases of employers and establish how these factors interact with ranking algorithms to affect hiring decisions.

Reliable Post hoc Explanations: Modeling Uncertainty in Explainability

1 code implementation NeurIPS 2021 Dylan Slack, Sophie Hilgard, Sameer Singh, Himabindu Lakkaraju

In this paper, we address the aforementioned challenges by developing a novel Bayesian framework for generating local explanations along with their associated uncertainty.

Feature Importance

From Predictions to Decisions: Using Lookahead Regularization

no code implementations NeurIPS 2020 Nir Rosenfeld, Sophie Hilgard, Sai Srivatsa Ravindranath, David C. Parkes

Machine learning is a powerful tool for predicting human-related outcomes, from credit scores to heart attack risks.

Fooling LIME and SHAP: Adversarial Attacks on Post hoc Explanation Methods

2 code implementations6 Nov 2019 Dylan Slack, Sophie Hilgard, Emily Jia, Sameer Singh, Himabindu Lakkaraju

Our approach can be used to scaffold any biased classifier in such a way that its predictions on the input data distribution still remain biased, but the post hoc explanations of the scaffolded classifier look innocuous.

Learning Key-Value Store Design

no code implementations11 Jul 2019 Stratos Idreos, Niv Dayan, Wilson Qin, Mali Akmanalp, Sophie Hilgard, Andrew Ross, James Lennon, Varun Jain, Harshita Gupta, David Li, Zichen Zhu

The critical insight and potential long-term impact is that such unifying models 1) render what we consider up to now as fundamentally different data structures to be seen as views of the very same overall design space, and 2) allow seeing new data structure designs with performance properties that are not feasible by existing designs.

Layout Design

Learning Representations by Humans, for Humans

no code implementations29 May 2019 Sophie Hilgard, Nir Rosenfeld, Mahzarin R. Banaji, Jack Cao, David C. Parkes

When machine predictors can achieve higher performance than the human decision-makers they support, improving the performance of human decision-makers is often conflated with improving machine accuracy.

Decision Making Representation Learning

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