2.1k citations · 3k across the 25 of their papers we have counts for
27 papers
Bridging Predictions and Interventions: An Integrated Framework for Automated Decision-Systems
Inioluwa Deborah Raji, Lydia T. Liu, Angela Zhou +27
Automated decision systems (ADS) leverage predictions about individual future outcomes to inform consequential decision-making in organizational settings. Across various settings -…
Measuring what Matters: Construct Validity in Large Language Model Benchmarks
Andrew M. Bean, Ryan Othniel Kearns, Angelika Romanou +39
Evaluating large language models (LLMs) is crucial for both assessing their capabilities and identifying safety or robustness issues prior to deployment. Reliably measuring abstrac…
Bridging Prediction and Intervention Problems in Social Systems
Lydia T. Liu, Inioluwa Deborah Raji, Angela Zhou +32
Many automated decision systems (ADS) are designed to solve prediction problems -- where the goal is to learn patterns from a sample of the population and apply them to individuals…
The California Report on Frontier AI Policy
Rishi Bommasani, Scott R. Singer, Ruth E. Appel +20
The innovations emerging at the frontier of artificial intelligence (AI) are poised to create historic opportunities for humanity but also raise complex policy challenges. Continue…
Multi-lingual Functional Evaluation for Large Language Models
Victor Ojewale, Inioluwa Deborah Raji, Suresh Venkatasubramanian
Multi-lingual competence in large language models is often evaluated via static data benchmarks such as Belebele, M-MMLU and M-GSM. However, these evaluations often fail to provide…
Aggregated Individual Reporting for Post-Deployment Evaluation
Jessica Dai, Inioluwa Deborah Raji, Benjamin Recht +1
The need for developing model evaluations beyond static benchmarking, especially in the post-deployment phase, is now well-understood. At the same time, concerns about the concentr…
Medical Large Language Model Benchmarks Should Prioritize Construct Validity
Ahmed Alaa, Thomas Hartvigsen, Niloufar Golchini +4
Medical large language models (LLMs) research often makes bold claims, from encoding clinical knowledge to reasoning like a physician. These claims are usually backed by evaluation…
Evaluating Prediction-based Interventions with Human Decision Makers In Mind
Inioluwa Deborah Raji, Lydia Liu
Automated decision systems (ADS) are broadly deployed to inform and support human decision-making across a wide range of consequential settings. However, various context-specific d…
From Individual Experience to Collective Evidence: A Reporting-Based Framework for Identifying Systemic Harms
Jessica Dai, Paula Gradu, Inioluwa Deborah Raji +1
When an individual reports a negative interaction with some system, how can their personal experience be contextualized within broader patterns of system behavior? We study the rep…
The Data Addition Dilemma
Judy Hanwen Shen, Inioluwa Deborah Raji, Irene Y. Chen
In many machine learning for healthcare tasks, standard datasets are constructed by amassing data across many, often fundamentally dissimilar, sources. But when does adding more da…
"I Searched for a Religious Song in Amharic and Got Sexual Content Instead": Investigating Online Harm in Low-Resourced Languages on YouTube
Hellina Hailu Nigatu, Inioluwa Deborah Raji
Online social media platforms such as YouTube have a wide, global reach. However, little is known about the experience of low-resourced language speakers on such platforms; especia…
Towards AI Accountability Infrastructure: Gaps and Opportunities in AI Audit Tooling
Victor Ojewale, Ryan Steed, Briana Vecchione +2
Audits are critical mechanisms for identifying the risks and limitations of deployed artificial intelligence (AI) systems. However, the effective execution of AI audits remains inc…
AI auditing: The Broken Bus on the Road to AI Accountability
Abeba Birhane, Ryan Steed, Victor Ojewale +2
One of the most concrete measures to take towards meaningful AI accountability is to consequentially assess and report the systems' performance and impact. However, the practical n…
Concrete Problems in AI Safety, Revisited
Inioluwa Deborah Raji, Roel Dobbe
As AI systems proliferate in society, the AI community is increasingly preoccupied with the concept of AI Safety, namely the prevention of failures due to accidents that arise from…
Who Audits the Auditors? Recommendations from a field scan of the algorithmic auditing ecosystem
Sasha Costanza-Chock, Emma Harvey, Inioluwa Deborah Raji +2
AI audits are an increasingly popular mechanism for algorithmic accountability; however, they remain poorly defined. Without a clear understanding of audit practices, let alone wid…
REFORMS: Reporting Standards for Machine Learning Based Science
Sayash Kapoor, Emily Cantrell, Kenny Peng +16
Machine learning (ML) methods are proliferating in scientific research. However, the adoption of these methods has been accompanied by failures of validity, reproducibility, and ge…
Organizational Governance of Emerging Technologies: AI Adoption in Healthcare
Jee Young Kim, William Boag, Freya Gulamali +13
Private and public sector structures and norms refine how emerging technology is used in practice. In healthcare, despite a proliferation of AI adoption, the organizational governa…
The Fallacy of AI Functionality
Inioluwa Deborah Raji, I. Elizabeth Kumar, Aaron Horowitz +1
Deployed AI systems often do not work. They can be constructed haphazardly, deployed indiscriminately, and promoted deceptively. However, despite this reality, scholars, the press,…
Outsider Oversight: Designing a Third Party Audit Ecosystem for AI Governance
Inioluwa Deborah Raji, Peggy Xu, Colleen Honigsberg +1
Much attention has focused on algorithmic audits and impact assessments to hold developers and users of algorithmic systems accountable. But existing algorithmic accountability pol…
AI and the Everything in the Whole Wide World Benchmark
Inioluwa Deborah Raji, Emily M. Bender, Amandalynne Paullada +2
There is a tendency across different subfields in AI to valorize a small collection of influential benchmarks. These benchmarks operate as stand-ins for a range of anointed common…
About Face: A Survey of Facial Recognition Evaluation
Inioluwa Deborah Raji, Genevieve Fried
We survey over 100 face datasets constructed between 1976 to 2019 of 145 million images of over 17 million subjects from a range of sources, demographics and conditions. Our histor…
Data and its (dis)contents: A survey of dataset development and use in machine learning research
Amandalynne Paullada, Inioluwa Deborah Raji, Emily M. Bender +2
Datasets have played a foundational role in the advancement of machine learning research. They form the basis for the models we design and deploy, as well as our primary medium for…
ABOUT ML: Annotation and Benchmarking on Understanding and Transparency of Machine Learning Lifecycles
Inioluwa Deborah Raji, Jingying Yang
We present the "Annotation and Benchmarking on Understanding and Transparency of Machine Learning Lifecycles" (ABOUT ML) project as an initiative to operationalize ML transparency…
Closing the AI Accountability Gap: Defining an End-to-End Framework for Internal Algorithmic Auditing
Inioluwa Deborah Raji, Andrew Smart, Rebecca N. White +6
Rising concern for the societal implications of artificial intelligence systems has inspired a wave of academic and journalistic literature in which deployed systems are audited fo…