2.1k citations · 5.9k across the 14 of their papers we have counts for
17 papers
Modeling the Sacred: Considerations when Using Religious Texts in Natural Language Processing
Ben Hutchinson
This position paper concerns the use of religious texts in Natural Language Processing (NLP), which is of special interest to the Ethics of NLP. Religious texts are expressions of…
"It's how you do things that matters": Attending to Process to Better Serve Indigenous Communities with Language Technologies
Ned Cooper, Courtney Heldreth, Ben Hutchinson
Indigenous languages are historically under-served by Natural Language Processing (NLP) technologies, but this is changing for some languages with the recent scaling of large multi…
Cultural Incongruencies in Artificial Intelligence
Vinodkumar Prabhakaran, Rida Qadri, Ben Hutchinson
Artificial intelligence (AI) systems attempt to imitate human behavior. How well they do this imitation is often used to assess their utility and to attribute human-like (or artifi…
Underspecification in Scene Description-to-Depiction Tasks
Ben Hutchinson, Jason Baldridge, Vinodkumar Prabhakaran
Questions regarding implicitness, ambiguity and underspecification are crucial for understanding the task validity and ethical concerns of multimodal image+text systems, yet have r…
Scaling Autoregressive Models for Content-Rich Text-to-Image Generation
Jiahui Yu, Yuanzhong Xu, Jing Yu Koh +14
We present the Pathways Autoregressive Text-to-Image (Parti) model, which generates high-fidelity photorealistic images and supports content-rich synthesis involving complex compos…
Evaluation Gaps in Machine Learning Practice
Ben Hutchinson, Negar Rostamzadeh, Christina Greer +2
Forming a reliable judgement of a machine learning (ML) model's appropriateness for an application ecosystem is critical for its responsible use, and requires considering a broad r…
PaLM: Scaling Language Modeling with Pathways
Aakanksha Chowdhery, Sharan Narang, Jacob Devlin +64
Large language models have been shown to achieve remarkable performance across a variety of natural language tasks using few-shot learning, which drastically reduces the number of…
LaMDA: Language Models for Dialog Applications
Romal Thoppilan, Daniel De Freitas, Jamie Hall +57
We present LaMDA: Language Models for Dialog Applications. LaMDA is a family of Transformer-based neural language models specialized for dialog, which have up to 137B parameters an…
Re-imagining Algorithmic Fairness in India and Beyond
Nithya Sambasivan, Erin Arnesen, Ben Hutchinson +2
Conventional algorithmic fairness is West-centric, as seen in its sub-groups, values, and methods. In this paper, we de-center algorithmic fairness and analyse AI power in India. B…
Towards Accountability for Machine Learning Datasets: Practices from Software Engineering and Infrastructure
Ben Hutchinson, Andrew Smart, Alex Hanna +5
Rising concern for the societal implications of artificial intelligence systems has inspired demands for greater transparency and accountability. However the datasets which empower…
Social Biases in NLP Models as Barriers for Persons with Disabilities
Ben Hutchinson, Vinodkumar Prabhakaran, Emily Denton +3
Building equitable and inclusive NLP technologies demands consideration of whether and how social attitudes are represented in ML models. In particular, representations encoded in…
Diversity and Inclusion Metrics in Subset Selection
Margaret Mitchell, Dylan Baker, Nyalleng Moorosi +5
The ethical concept of fairness has recently been applied in machine learning (ML) settings to describe a wide range of constraints and objectives. When considering the relevance o…
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…
Advances and Open Problems in Federated Learning
Peter Kairouz, H. Brendan McMahan, Brendan Avent +56
Federated learning (FL) is a machine learning setting where many clients (e.g. mobile devices or whole organizations) collaboratively train a model under the orchestration of a cen…
Perturbation Sensitivity Analysis to Detect Unintended Model Biases
Vinodkumar Prabhakaran, Ben Hutchinson, Margaret Mitchell
Data-driven statistical Natural Language Processing (NLP) techniques leverage large amounts of language data to build models that can understand language. However, most language da…
50 Years of Test (Un)fairness: Lessons for Machine Learning
Ben Hutchinson, Margaret Mitchell
Quantitative definitions of what is unfair and what is fair have been introduced in multiple disciplines for well over 50 years, including in education, hiring, and machine learnin…
Model Cards for Model Reporting
Margaret Mitchell, Simone Wu, Andrew Zaldivar +6
Trained machine learning models are increasingly used to perform high-impact tasks in areas such as law enforcement, medicine, education, and employment. In order to clarify the in…