Research & Development

With over 50+ years of combined industry and academic experience in AI and ML operations, Arthur is proud to adopt a research-led approach to product development. Our experimental approach and expert researchers drive exclusive capabilities in LLMs, computer vision, bias mitigation, and other critical areas.

ML Research Fellows

Since Arthur’s inception, our Research Fellows program has recruited and curated relationships with top AI, ML, policy, and legal junior researchers, who spend a summer or semester with Arthur building toward a joint goal of public dissemination of a research result.

Angelina Wang

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Namrata Mukhija

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Michelle Bao

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Naveen Durvasula

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Lizzie Kumar

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Kweku Kwegyir-Aggrey

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Sahil Verma

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Publication Library

Model-Based Debiasing for Groupwise Item Fairness

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Geometric Repair for Fair Classification at Any Decision Threshold

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Reckoning with the Disagreement Problem: Explanation Consensus as a Training Objective

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Tensions Between the Proxies of Human Values in AI

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Equalizing Credit Opportunity in Algorithms: Aligning Algorithmic Fairness Research with U.S. Fair Lending Regulation

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Who’s Thinking? A Push for Human-Centered Evaluation of LLMs using the XAI Playbook

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Counterfactual Explanations for Machine Learning: A Review

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Characterizing Anomalies with Explainable Classifiers

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On the Generalizability and Predictability of Recommender Systems

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Amortized Generation of Sequential Counterfactuals for Black Box Models

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Robustness Disparities in Commercial Face Detection

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Counterfactual Explanations for Machine Learning: Challenges Revisited

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Conference Highlights & Talks