activity
20192021
most citedTowards a Critical Race Methodology in Algorithmic Fairness

298 citations · 565 across the 4 of their papers we have counts for

collaborators

5 papers

cs.CV2021165 cited

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…

cs.CY202036 cited

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…

cs.LG2020

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…

cs.AI202066 cited

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…

cs.CY2019298 cited

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…