332 citations · 581 across the 17 of their papers we have counts for
17 papers
Co-designing for Compliance: Multi-party Computation Protocols for Post-Market Fairness Monitoring in Algorithmic Hiring
Changyang He, Nina Baranowska, Josu Andoni Eguiluz Castaneira +5
Post-market fairness monitoring is now mandated to ensure fairness and accountability for high-risk employment AI systems under emerging regulations such as the EU AI Act. However,…
From Clicks to Consensus: Collective Consent Assemblies for Data Governance
Lin Kyi, Paul Gölz, Robin Berjon +1
Obtaining meaningful and informed consent from users is essential for ensuring autonomy and control over one's data. Notice and consent, the standard for collecting consent, has be…
Turning to Online Forums for Legal Information: A Case Study of GDPR's Legitimate Interests
Lin Kyi, Cristiana Santos, Sushil Ammanaghatta Shivakumar +2
Practitioners building online services and tools often turn to online forums such as Reddit, Law Stack Exchange, and Stack Overflow for legal guidance to ensure compliance with the…
What's in a Query: Polarity-Aware Distribution-Based Fair Ranking
Aparna Balagopalan, Kai Wang, Olawale Salaudeen +2
Machine learning-driven rankings, where individuals (or items) are ranked in response to a query, mediate search exposure or attention in a variety of safety-critical settings. Thu…
Governance of Generative AI in Creative Work: Consent, Credit, Compensation, and Beyond
Lin Kyi, Amruta Mahuli, M. Six Silberman +3
Since the emergence of generative AI, creative workers have spoken up about the career-based harms they have experienced arising from this new technology. A common theme in these a…
The trade-off between data minimization and fairness in collaborative filtering
Nasim Sonboli, Sipei Li, Mehdi Elahi +1
General Data Protection Regulations (GDPR) aim to safeguard individuals' personal information from harm. While full compliance is mandatory in the European Union and the California…
Unlocking Fair Use in the Generative AI Supply Chain: A Systematized Literature Review
Amruta Mahuli, Asia Biega
Through a systematization of generative AI (GenAI) stakeholder goals and expectations, this work seeks to uncover what value different stakeholders see in their contributions to th…
"It doesn't tell me anything about how my data is used'': User Perceptions of Data Collection Purposes
Lin Kyi, Abraham Mhaidli, Cristiana Santos +2
Data collection purposes and their descriptions are presented on almost all privacy notices under the GDPR, yet there is a lack of research focusing on how effective they are at in…
The Role of Relevance in Fair Ranking
Aparna Balagopalan, Abigail Z. Jacobs, Asia Biega
Online platforms mediate access to opportunity: relevance-based rankings create and constrain options by allocating exposure to job openings and job candidates in hiring platforms,…
Overview of the TREC 2021 Fair Ranking Track
Michael D. Ekstrand, Graham McDonald, Amifa Raj +1
The TREC Fair Ranking Track aims to provide a platform for participants to develop and evaluate novel retrieval algorithms that can provide a fair exposure to a mixture of demograp…
(Re)Politicizing Digital Well-Being: Beyond User Engagements
Niall Docherty, Asia J. Biega
The psychological costs of the attention economy are often considered through the binary of harmful design and healthy use, with digital well-being chiefly characterised as a matte…
Estimation of Fair Ranking Metrics with Incomplete Judgments
Ömer Kırnap, Fernando Diaz, Asia Biega +3
There is increasing attention to evaluating the fairness of search system ranking decisions. These metrics often consider the membership of items to particular groups, often identi…
Operationalizing the Legal Principle of Data Minimization for Personalization
Asia J. Biega, Peter Potash, Hal Daumé +2
Article 5(1)(c) of the European Union's General Data Protection Regulation (GDPR) requires that "personal data shall be [...] adequate, relevant, and limited to what is necessary i…
Evaluating Stochastic Rankings with Expected Exposure
Fernando Diaz, Bhaskar Mitra, Michael D. Ekstrand +2
We introduce the concept of \emph{expected exposure} as the average attention ranked items receive from users over repeated samples of the same query. Furthermore, we advocate for…
Towards Query Logs for Privacy Studies: On Deriving Search Queries from Questions
Asia J. Biega, Jana Schmidt, Rishiraj Saha Roy
Translating verbose information needs into crisp search queries is a phenomenon that is ubiquitous but hardly understood. Insights into this process could be valuable in several ap…
Report on the First HIPstIR Workshop on the Future of Information Retrieval
Laura Dietz, Bhaskar Mitra, Jeremy Pickens +21
The vision of HIPstIR is that early stage information retrieval (IR) researchers get together to develop a future for non-mainstream ideas and research agendas in IR. The first ite…
Equity of Attention: Amortizing Individual Fairness in Rankings
Asia J. Biega, Krishna P. Gummadi, Gerhard Weikum
Rankings of people and items are at the heart of selection-making, match-making, and recommender systems, ranging from employment sites to sharing economy platforms. As ranking pos…