298 citations · 777 across the 10 of their papers we have counts for
10 papers
Much Ado About Gender: Current Practices and Future Recommendations for Appropriate Gender-Aware Information Access
Christine Pinney, Amifa Raj, Alex Hanna +1
Information access research (and development) sometimes makes use of gender, whether to report on the demographics of participants in a user study, as inputs to personalized result…
Reduced, Reused and Recycled: The Life of a Dataset in Machine Learning Research
Bernard Koch, Emily Denton, Alex Hanna +1
Benchmark datasets play a central role in the organization of machine learning research. They coordinate researchers around shared research problems and serve as a measure of progr…
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…
Do Datasets Have Politics? Disciplinary Values in Computer Vision Dataset Development
Morgan Klaus Scheuerman, Emily Denton, Alex Hanna
Data is a crucial component of machine learning. The field is reliant on data to train, validate, and test models. With increased technical capabilities, machine learning research…
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…
Against Scale: Provocations and Resistances to Scale Thinking
Alex Hanna, Tina M. Park
At the heart of what drives the bulk of innovation and activity in Silicon Valley and elsewhere is scalability. This unwavering commitment to scalability -- to identify strategies…
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…
Bringing the People Back In: Contesting Benchmark Machine Learning Datasets
Remi Denton, Alex Hanna, Razvan Amironesei +3
In response to algorithmic unfairness embedded in sociotechnical systems, significant attention has been focused on the contents of machine learning datasets which have revealed bi…
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…
Towards a Critical Race Methodology in Algorithmic Fairness
Alex Hanna, Emily Denton, Andrew Smart +1
We examine the way race and racial categories are adopted in algorithmic fairness frameworks. Current methodologies fail to adequately account for the socially constructed nature o…