168 citations · 246 across the 9 of their papers we have counts for
8 papers · 1 filter
Gaming Consensus: Coordinated Manipulation in Crowdsourced Fact-Checking
Nikil Roashan Selvam, Jay Baxter, Sophie Hilgard +4
Crowdsourced fact-checking systems have been adopted by major social media companies such as X, Meta, TikTok and Google with the aim of combating misleading information at scale wi…
Feature Attributions and Counterfactual Explanations Can Be Manipulated
Dylan Slack, Sophie Hilgard, Sameer Singh +1
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 explai…
Counterfactual Explanations Can Be Manipulated
Dylan Slack, Sophie Hilgard, Himabindu Lakkaraju +1
Counterfactual explanations are emerging as an attractive option for providing recourse to individuals adversely impacted by algorithmic decisions. As they are deployed in critical…
Does Fair Ranking Improve Minority Outcomes? Understanding the Interplay of Human and Algorithmic Biases in Online Hiring
Tom Sühr, Sophie Hilgard, Himabindu Lakkaraju
Ranking algorithms are being widely employed in various online hiring platforms including LinkedIn, TaskRabbit, and Fiverr. Prior research has demonstrated that ranking algorithms…
Reliable Post hoc Explanations: Modeling Uncertainty in Explainability
Dylan Slack, Sophie Hilgard, Sameer Singh +1
As black box explanations are increasingly being employed to establish model credibility in high-stakes settings, it is important to ensure that these explanations are accurate and…
From Predictions to Decisions: Using Lookahead Regularization
Nir Rosenfeld, Sophie Hilgard, Sai Srivatsa Ravindranath +1
Machine learning is a powerful tool for predicting human-related outcomes, from credit scores to heart attack risks. But when deployed, learned models also affect how users act in…
Fooling LIME and SHAP: Adversarial Attacks on Post hoc Explanation Methods
Dylan Slack, Sophie Hilgard, Emily Jia +2
As machine learning black boxes are increasingly being deployed in domains such as healthcare and criminal justice, there is growing emphasis on building tools and techniques for e…
Learning Representations by Humans, for Humans
Sophie Hilgard, Nir Rosenfeld, Mahzarin R. Banaji +2
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 impro…