Search Results for author: Tim Draws

Found 5 papers, 1 papers with code

Collect, Measure, Repeat: Reliability Factors for Responsible AI Data Collection

1 code implementation22 Aug 2023 Oana Inel, Tim Draws, Lora Aroyo

We argue that data collection for AI should be performed in a responsible manner where the quality of the data is thoroughly scrutinized and measured through a systematic set of appropriate metrics.


This Is Not What We Ordered: Exploring Why Biased Search Result Rankings Affect User Attitudes on Debated Topics

no code implementations4 May 2021 Tim Draws, Nava Tintarev, Ujwal Gadiraju, Alessandro Bozzon, Benjamin Timmermans

To better understand the mechanisms underlying SEME, we present a pre-registered, 5 × 3 factorial user study investigating whether order effects (i. e., users adopting the viewpoint pertaining to higher-ranked documents) can cause SEME.

Disparate Impact Diminishes Consumer Trust Even for Advantaged Users

no code implementations29 Jan 2021 Tim Draws, Zoltán Szlávik, Benjamin Timmermans, Nava Tintarev, Kush R. Varshney, Michael Hind

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.

Decision Making Fairness Human-Computer Interaction

Assessing Viewpoint Diversity in Search Results Using Ranking Fairness Metrics

no code implementations27 Oct 2020 Tim Draws, Nava Tintarev, Ujwal Gadiraju, Alessandro Bozzon, Benjamin Timmermans

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.


Helping users discover perspectives: Enhancing opinion mining with joint topic models

no code implementations23 Oct 2020 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.

Opinion Mining Topic Models

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