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Found 5 papers, 1 papers with code
Collect, Measure, Repeat: Reliability Factors for Responsible AI Data Collection
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
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
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
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
Support or opposition concerning a debated claim such as abortion should be legal can have different underlying reasons, which we call perspectives.
