Responsible AI
Our research in Responsible AI aims to shape the field of artificial intelligence and machine learning in ways that foreground the human experiences and impacts of these technologies. We examine and shape emerging AI models, systems, and datasets used in research, development, and practice. This research uncovers foundational insights and devises methodologies that define the state-of-the-art across the field. We advance equity, fairness, transparency, robustness, interpretability, and inclusivity as key elements of AI systems. For example, recent research evaluates the generalizability of the fairness properties of medical AI algorithms and discusses the cultural properties of fair AI systems globally. We strive to ensure that the promise of AI is realized beneficially for all individuals and communities, prioritizing social and contextual implications.
Recent Publications
A roadmap for addressing the use of race and ethnicity in clinical algorithms
Divya Shanmugam
Sidhika Balachandar
Alexandra Chouldechova
James Diao
Kadija Ferryman
Arjun Manrai
Neil Powe
Emma Pierson
Deb Raji
Nature Health, 1 (2026), 789–791
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Removing race and ethnicity from clinical algorithms is feasible, but it requires careful evaluation of algorithmic changes and systemic efforts to address underlying disparities.
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What Limits Us? Analyzing Self-Reported Limitations in NLP Research
Tawan Thaepprasit
Peeranuth Kehasukcharoen
Peerapon Vateekul
2026
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Since late 2022, a Limitations section has become mandatory at many top-tier NLP conferences. The growing number of accepted papers at these venues has resulted in a vast corpus of self-reported limitations that cannot all be manually reviewed, yet remains systematically unanalyzed. Therefore, in this paper, we conduct a large-scale analysis of the Limitations sections from ACL and EMNLP papers published between 2020 and 2025 to understand what researchers disclose about their own work. To do so, we implement a novel human-AI framework for iterative hybrid qualitative coding. This framework enables us to investigate trends in self-reported limitations over time, their correlations with specific paper attributes, and the writing patterns that recur around these disclosures. Our findings offer a critical reflection on the diverse reported challenges as well as the self-reporting practices of researchers in the NLP community.
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How Tech Workers Contend with Hazards of Humanlikeness in Generative AI
Eric Corbett
Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems, ACM (2026), pp. 1-18
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Generative AI’s humanlike qualities are driving its rapid adoption in professional domains. However, this anthropomorphic appeal raises concerns from HCI and responsible AI scholars about potential hazards and harms, such as overtrust in system outputs. To investigate how technology workers navigate these humanlike qualities and anticipate emergent harms, we conducted focus groups with 30 professionals across six job functions (ML engineering, product policy, UX research and design, product management, technology writing, and communications). Our findings reveal an unsettled knowledge environment surrounding humanlike generative AI, where workers’ varying perspectives illuminate a range of potential risks for individuals, knowledge work fields, and society. We argue that workers require comprehensive support, including clearer conceptions of “humanlikeness” to effectively mitigate these risks. To aid in mitigation strategies, we provide a conceptual map articulating the identified hazards and their connection to conflated notions of “humanlikeness.”
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Working Together on Technologies: A Case Study of Collaboration in Aotearoa
Gabi Conlon
Greg Duncum
Jamie Towler
Carrie Jones
Tane Karamaina
Hautahi Kingi
Caroline Rainsford
Ngahiwi Apanui-Barr
2026
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This paper presents a case study in collaboration on TTS technologies between Te Taura Whiri and Google. The former is a Māori-led organisation whose mission is the revitalisation of te reo Māori. The latter is a global technology company which has a presence in Aotearoa but which is headquartered elsewhere. This case study focuses not on technical implementation details, but rather on the challenges in the partnership to mutual understanding and alignment, and the processes we co-developed to overcome those challenges, which led to the successful development of technology which was satisfactory to both partners.
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Evaluating Contextual Illegality: AI Compliance in Corporate Law Scenarios
Hilal Aka
Joe Kwon
Noam Kolt
Forty-third International Conference on Machine Learning (2026)
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AI models readily refuse explicitly unlawful requests, but real-world illegality often depends on context. We evaluate frontier models on contextual illegality across four corporate law domains in which routine actions—editing documents, trading stock, requesting payment, approving communications—become unlawful due to triggers such as pending investigations or bankruptcy filings. We study both chat and agentic settings and compare results to a human baseline. The best-performing models achieved near-zero compliance with illegal requests while maintaining high compliance with legal ones, though performance varied sharply by domain. We also identify distinct failure modes such as excessive refusal of legal requests and find improved performance from reasoning models and agentic environments. By utilizing the structure of contextual illegality to create controlled evaluations, our methodology provides empirical grounding for emerging research on law-following AI and extends naturally to additional legal domains.
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Audio description (AD) narrates important visual details which are played during dialogue gaps in video soundtracks, making them accessible to blind and low vision (BLV) audiences. AD professionals (producers, writers, narrators, mixers, and quality control specialists) possess expert knowledge of AD development and the constraints that affect their work. However, their perspectives remain largely absent in AD research. We present interviews with 17 AD professionals (8 BLV), detailing their workflows to produce AD for recorded media and live theater. We additionally explore their perspectives on recent changes impacting their work, revealing tensions between advocacy for culturally competent AD and the rise of automations—some beneficial, others with concerning implications for AD quality. Highlighting these tensions, we offer research directions to support AD professionals, and we pose guiding questions for AD and AI innovators on preserving the high-quality human touch professionals consider fundamental to the accessibility provision.
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