28 citations · 52 across the 6 of their papers we have counts for
6 papers
Collect, Measure, Repeat: Reliability Factors for Responsible AI Data Collection
Oana Inel, Tim Draws, Lora Aroyo
The rapid entry of machine learning approaches in our daily activities and high-stakes domains demands transparency and scrutiny of their fairness and reliability. To help gauge ma…
Explaining Search Result Stances to Opinionated People
Z. Wu, T. Draws, F. Cau +3
People use web search engines to find information before forming opinions, which can lead to practical decisions with different levels of impact. The cognitive effort of search can…
Disparate Impact Diminishes Consumer Trust Even for Advantaged Users
Tim Draws, Zoltán Szlávik, Benjamin Timmermans +3
Systems aiming to aid consumers in their decision-making (e.g., by implementing persuasive techniques) are more likely to be effective when consumers trust them. However, recent re…
Assessing Viewpoint Diversity in Search Results Using Ranking Fairness Metrics
Tim Draws, Nava Tintarev, Ujwal Gadiraju +2
The way pages are ranked in search results influences whether the users of search engines are exposed to more homogeneous, or rather to more diverse viewpoints. However, this viewp…
Helping users discover perspectives: Enhancing opinion mining with joint topic models
Tim Draws, Jody Liu, Nava Tintarev
Support or opposition concerning a debated claim such as abortion should be legal can have different underlying reasons, which we call perspectives. This paper explores how opinion…
Combine Statistical Thinking With Open Scientific Practice: A Protocol of a Bayesian Research Project
Alexandra Sarafoglou, Anna van der Heijden, Tim Draws +3
Current developments in the statistics community suggest that modern statistics education should be structured holistically, that is, by allowing students to work with real data an…