The ACM Conference on Fairness, Accountability, and Transparency

FAccT '24 Town Hall
1:33:41
FAccT '24 Town Hall
FAcct '24 Keynote: Sanmi Koyejo, Stanford University The measure and mismeasure of AI
1:10:21
FAcct '24 Keynote: Sanmi Koyejo, Stanford University The measure and mismeasure of AI
FAccT '24: Keynote: Stephanie Nguyen, U.S. Federal Trade Commission
1:08:49
FAccT '24: Keynote: Stephanie Nguyen, U.S. Federal Trade Commission
FAccT '24: Community Keynote: Polinho Mota
1:03:47
FAccT '24: Community Keynote: Polinho Mota
FAccT '24 Keynote: Seeta Peña Gangadharan, London School of Economics
1:10:34
FAccT '24 Keynote: Seeta Peña Gangadharan, London School of Economics
Technodiversity & digital practices in Brazil: Joaquim Melo, Nina da Hora, Alexsandro Mesquita
1:07:00
Technodiversity & digital practices in Brazil: Joaquim Melo, Nina da Hora, Alexsandro Mesquita
FAccT '24 Keynote: Virgilio Almeida
1:14:41
FAccT '24 Keynote: Virgilio Almeida
FAccT'23 Community Keynote: Productivity Monitoring, Surveillance, Automation, and Big Tech Harms
1:12:04
FAccT'23 Community Keynote: Productivity Monitoring, Surveillance, Automation, and Big Tech Harms
Welfarist Moral Grounding for Transparent Artificial Intelligence
10:08
Welfarist Moral Grounding for Transparent Artificial Intelligence
Machine Explanations and Human Understanding
5:21
Machine Explanations and Human Understanding
Honor ethics: The Challenge of Globalizing Value Alignment in AI
9:58
Honor ethics: The Challenge of Globalizing Value Alignment in AI
Bias as Boundary Object: Critiquing An Algorithm For Austerity Using Bias Frameworks
9:44
Bias as Boundary Object: Critiquing An Algorithm For Austerity Using Bias Frameworks
Stronger Together: Articulation of Ethical Charters, Legal Tools, and Technical Documentation in ML
8:44
Stronger Together: Articulation of Ethical Charters, Legal Tools, and Technical Documentation in ML
Trustworthy AI and the Logics of Intersectional Resistance
9:49
Trustworthy AI and the Logics of Intersectional Resistance
Reconciling Individual Probability Forecasts
9:56
Reconciling Individual Probability Forecasts
Algorithmic decisions, Desire for Control, & the Preference for Human Review over Algorithmic Review
10:51
Algorithmic decisions, Desire for Control, & the Preference for Human Review over Algorithmic Review
How online behavioral advertising harms people
9:56
How online behavioral advertising harms people
Preventing Discriminatory Decision-making in Evolving Data Stream
8:51
Preventing Discriminatory Decision-making in Evolving Data Stream
Broadening AI Ethics Narratives: An Indic Art View
9:39
Broadening AI Ethics Narratives: An Indic Art View
Multi-dimensional discrimination in Law and Machine Learning - A comparative overview
12:02
Multi-dimensional discrimination in Law and Machine Learning - A comparative overview
How Biased are Your Features?Computing Fairness Influence Functions with Global Sensitivity Analysis
10:14
How Biased are Your Features?Computing Fairness Influence Functions with Global Sensitivity Analysis
Disparities in Text-to-Image Model Concept Possession Across Languages
10:01
Disparities in Text-to-Image Model Concept Possession Across Languages
Representation Online Matters Practical End-to-end Diversification in Search and Recommender Systems
10:41
Representation Online Matters Practical End-to-end Diversification in Search and Recommender Systems
On The Impact of Machine Learning Randomness on Group Fairness
10:00
On The Impact of Machine Learning Randomness on Group Fairness
Humans, AI, and Context: Understanding End-Users’ Trust in a Real-World Computer Vision Application
9:31
Humans, AI, and Context: Understanding End-Users’ Trust in a Real-World Computer Vision Application
WEIRD FAccTs: How Western, Educated, Industrialized, Rich, and Democratic is FAccT?
9:32
WEIRD FAccTs: How Western, Educated, Industrialized, Rich, and Democratic is FAccT?
Skin Deep Investigating Subjectivity in Skin Tone Annotations for Computer Vision Benchmark Datasets
9:57
Skin Deep Investigating Subjectivity in Skin Tone Annotations for Computer Vision Benchmark Datasets
Fairness in machine learning from the perspective of sociology of statistics
15:03
Fairness in machine learning from the perspective of sociology of statistics
In the Name of Fairness: Assessing the Bias in Clinical Record De-identification
10:25
In the Name of Fairness: Assessing the Bias in Clinical Record De-identification
Harms from Increasingly Agentic Algorithmic Systems
8:55
Harms from Increasingly Agentic Algorithmic Systems