165 citations · 330 across the 8 of their papers we have counts for
8 papers
Who Bears the Cost of Honesty? A FAccT Workshop Synthesis and Research Agenda for Equitable AI Disclosure
Runlong Ye, Jessica He, Finola Finn +4
AI disclosure is increasingly promoted and sometimes required as a route to transparency, accountability, provenance, and trust. Yet disclosure can also expose AI users to suspicio…
Yes, But Not Always. Generative AI Needs Nuanced Opt-in
Wiebke Hutiri, Morgan Scheuerman, Shruti Nagpal +2
This paper argues that a one-size-fits-all approach to specifying consent for the use of creative works in generative AI is insufficient. Real-world ownership and rights holder str…
TEDI: Trustworthy and Ethical Dataset Indicators to Analyze and Compare Dataset Documentation
Wiebke Hutiri, Mircea Cimpoi, Morgan Scheuerman +2
Dataset transparency is a key enabler of responsible AI, but insights into multimodal dataset attributes that impact trustworthy and ethical aspects of AI applications remain scarc…
A Taxonomy of Challenges to Curating Fair Datasets
Dora Zhao, Morgan Klaus Scheuerman, Pooja Chitre +5
Despite extensive efforts to create fairer machine learning (ML) datasets, there remains a limited understanding of the practical aspects of dataset curation. Drawing from intervie…
Taxonomizing and Measuring Representational Harms: A Look at Image Tagging
Jared Katzman, Angelina Wang, Morgan Scheuerman +4
In this paper, we examine computational approaches for measuring the "fairness" of image tagging systems, finding that they cluster into five distinct categories, each with its own…
A Framework of Severity for Harmful Content Online
Morgan Klaus Scheuerman, Jialun Aaron Jiang, Casey Fiesler +1
The proliferation of harmful content on online social media platforms has necessitated empirical understandings of experiences of harm online and the development of practices for h…
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…
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…