activity
20202026
most citedDark Patterns after the GDPR: Scraping Consent Pop-ups and Demonstrating their Influence

483 citations · 531 across the 4 of their papers we have counts for

collaborators

5 papers

cs.HC2026

Measuring Perceptions of Fairness in AI Systems: The Effects of Infra-marginality

Schrasing Tong, Minseok Jung, Ilaria Liccardi +1

Differences in data distributions between demographic groups, known as the problem of infra-marginality, complicate how people evaluate fairness in machine learning models. We pres…

cs.HC2025

Analyzing Privacy Dynamics within Groups using Gamified Auctions

Hüseyin Aydın, Onuralp Ulusoy, Ilaria Liccardi +1

Online shared content, such as group pictures, often contains information about multiple users. Developing technical solutions to manage the privacy of such "co-owned" content is c…

cs.CV20207 cited

Debugging Tests for Model Explanations

Julius Adebayo, Michael Muelly, Ilaria Liccardi +1

We investigate whether post-hoc model explanations are effective for diagnosing model errors--model debugging. In response to the challenge of explaining a model's prediction, a va…

cs.HC202041 cited

Misplaced Trust: Measuring the Interference of Machine Learning in Human Decision-Making

Harini Suresh, Natalie Lao, Ilaria Liccardi

ML decision-aid systems are increasingly common on the web, but their successful integration relies on people trusting them appropriately: they should use the system to fill in gap…

cs.HC2020483 cited

Dark Patterns after the GDPR: Scraping Consent Pop-ups and Demonstrating their Influence

Midas Nouwens, Ilaria Liccardi, Michael Veale +2

New consent management platforms (CMPs) have been introduced to the web to conform with the EU's General Data Protection Regulation, particularly its requirements for consent when…