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11/19/2022
Cultural Incongruencies in Artificial Intelligence
Artificial intelligence (AI) systems attempt to imitate human behavior. ...
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10/11/2022
Underspecification in Scene Description-to-Depiction Tasks
Questions regarding implicitness, ambiguity and underspecification are c...
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06/22/2022
Scaling Autoregressive Models for Content-Rich Text-to-Image Generation
We present the Pathways Autoregressive Text-to-Image (Parti) model, whic...
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05/11/2022
Evaluation Gaps in Machine Learning Practice
Forming a reliable judgement of a machine learning (ML) model's appropri...
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04/05/2022
PaLM: Scaling Language Modeling with Pathways
Large language models have been shown to achieve remarkable performance ...
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12/06/2021
Thinking Beyond Distributions in Testing Machine Learned Models
Testing practices within the machine learning (ML) community have center...
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01/25/2021
Re-imagining Algorithmic Fairness in India and Beyond
Conventional algorithmic fairness is West-centric, as seen in its sub-gr...
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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 ...
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12/03/2020
Non-portability of Algorithmic Fairness in India
Conventional algorithmic fairness is Western in its sub-groups, values, ...
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10/23/2020
Towards Accountability for Machine Learning Datasets: Practices from Software Engineering and Infrastructure
Rising concern for the societal implications of artificial intelligence ...
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05/02/2020
Social Biases in NLP Models as Barriers for Persons with Disabilities
Building equitable and inclusive NLP technologies demands consideration ...
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02/09/2020
Diversity and Inclusion Metrics in Subset Selection
The ethical concept of fairness has recently been applied in machine lea...
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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 ...
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12/10/2019
Advances and Open Problems in Federated Learning
Federated learning (FL) is a machine learning setting where many clients...
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10/09/2019
Perturbation Sensitivity Analysis to Detect Unintended Model Biases
Data-driven statistical Natural Language Processing (NLP) techniques lev...
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08/01/2019
Interpreting Social Respect: A Normative Lens for ML Models
Machine learning is often viewed as an inherently value-neutral process:...
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06/14/2019
Detecting Bias with Generative Counterfactual Face Attribute Augmentation
We introduce a simple framework for identifying biases of a smiling attr...
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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...
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10/05/2018