Ben Hutchinson | DeepAI

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344 publications
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∙ 11/19/2022

Cultural Incongruencies in Artificial Intelligence

Artificial intelligence (AI) systems attempt to imitate human behavior. ...

0 Vinodkumar Prabhakaran, et al. ∙

∙ 10/11/2022

Underspecification in Scene Description-to-Depiction Tasks

Questions regarding implicitness, ambiguity and underspecification are c...

4 Ben Hutchinson, et al. ∙

∙ 06/22/2022

Scaling Autoregressive Models for Content-Rich Text-to-Image Generation

We present the Pathways Autoregressive Text-to-Image (Parti) model, whic...

0 Jiahui Yu, et al. ∙

∙ 05/11/2022

Evaluation Gaps in Machine Learning Practice

Forming a reliable judgement of a machine learning (ML) model's appropri...

21 Ben Hutchinson, et al. ∙

∙ 04/05/2022

PaLM: Scaling Language Modeling with Pathways

Large language models have been shown to achieve remarkable performance ...

6 Aakanksha Chowdhery, et al. ∙

∙ 12/06/2021

Thinking Beyond Distributions in Testing Machine Learned Models

Testing practices within the machine learning (ML) community have center...

31 Negar Rostamzadeh, et al. ∙

∙ 01/25/2021

Re-imagining Algorithmic Fairness in India and Beyond

Conventional algorithmic fairness is West-centric, as seen in its sub-gr...

7 Nithya Sambasivan, et al. ∙

∙ 12/08/2020

Fairness Preferences, Actual and Hypothetical: A Study of Crowdworker Incentives

How should we decide which fairness criteria or definitions to adopt in ...

0 Angie Peng, et al. ∙

∙ 12/03/2020

Non-portability of Algorithmic Fairness in India

Conventional algorithmic fairness is Western in its sub-groups, values, ...

8 Nithya Sambasivan, et al. ∙

∙ 10/23/2020

Towards Accountability for Machine Learning Datasets: Practices from Software Engineering and Infrastructure

Rising concern for the societal implications of artificial intelligence ...

0 Ben Hutchinson, et al. ∙

∙ 05/02/2020

Social Biases in NLP Models as Barriers for Persons with Disabilities

Building equitable and inclusive NLP technologies demands consideration ...

0 Ben Hutchinson, et al. ∙

∙ 02/09/2020

Diversity and Inclusion Metrics in Subset Selection

The ethical concept of fairness has recently been applied in machine lea...

23 Margaret Mitchell, et al. ∙

∙ 01/03/2020

Closing the AI Accountability Gap: Defining an End-to-End Framework for Internal Algorithmic Auditing

Rising concern for the societal implications of artificial intelligence ...

0 Inioluwa Deborah Raji, et al. ∙

∙ 12/10/2019

Advances and Open Problems in Federated Learning

Federated learning (FL) is a machine learning setting where many clients...

33 Peter Kairouz, et al. ∙

∙ 10/09/2019

Perturbation Sensitivity Analysis to Detect Unintended Model Biases

Data-driven statistical Natural Language Processing (NLP) techniques lev...

0 Vinodkumar Prabhakaran, et al. ∙

∙ 08/01/2019

Interpreting Social Respect: A Normative Lens for ML Models

Machine learning is often viewed as an inherently value-neutral process:...

0 Ben Hutchinson, et al. ∙

∙ 06/14/2019

Detecting Bias with Generative Counterfactual Face Attribute Augmentation

We introduce a simple framework for identifying biases of a smiling attr...

0 Emily Denton, et al. ∙

∙ 11/25/2018

50 Years of Test (Un)fairness: Lessons for Machine Learning

Quantitative definitions of what is unfair and what is fair have been in...

0 Ben Hutchinson, et al. ∙

∙ 10/05/2018

Model Cards for Model Reporting

Trained machine learning models are increasingly used to perform high-im...

6 Margaret Mitchell, et al. ∙

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