no code implementations • ACL (NL4XAI, INLG) 2020 • Alisa Rieger, Mariët Theune, Nava Tintarev
Cognitive biases in the context of consuming online information filtered by recommender systems may lead to sub-optimal choices.
no code implementations • ACL (NLP4PosImpact) 2021 • Myrthe Reuver, Nicolas Mattis, Marijn Sax, Suzan Verberne, Nava Tintarev, Natali Helberger, Judith Moeller, Sanne Vrijenhoek, Antske Fokkens, Wouter van Atteveldt
In this position paper, we present a research agenda and ideas for facilitating exposure to diverse viewpoints in news recommendation.
no code implementations • 18 Sep 2023 • Adarsa Sivaprasad, Ehud Reiter, Nava Tintarev, Nir Oren
A task based evaluation of mental models of these participants provide valuable feedback to enhance narrative global explanations.
1 code implementation • 11 Sep 2023 • Roan Schellingerhout, Francesco Barile, Nava Tintarev
Recent legislation proposals have significantly increased the demand for eXplainable Artificial Intelligence (XAI) in many businesses, especially in so-called `high-risk' domains, such as recruitment.
Explainable artificial intelligence Explainable Artificial Intelligence (XAI) +1
1 code implementation • 18 Dec 2022 • Rishav Hada, Amir Ebrahimi Fard, Sarah Shugars, Federico Bianchi, Patricia Rossini, Dirk Hovy, Rebekah Tromble, Nava Tintarev
We find that the diversity scores for both Fragmentation and Representation are lower for immigration than for DST.
no code implementations • 28 Oct 2022 • Federico Bianchi, Stefanie Anja Hills, Patricia Rossini, Dirk Hovy, Rebekah Tromble, Nava Tintarev
Well-annotated data is a prerequisite for good Natural Language Processing models.
no code implementations • 4 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.
no code implementations • 29 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
no code implementations • 15 Jan 2021 • Mats Mulder, Oana Inel, Jasper Oosterman, Nava Tintarev
We apply this notion to a re-ranking of topic-relevant recommended lists, to form the basis of a novel viewpoint diversification method.
no code implementations • 27 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.
no code implementations • 23 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.
no code implementations • 6 Sep 2020 • Boning Gong, Mesut Kaya, Nava Tintarev
We compare a global (context for all users) and personalized (context for each user) model based on these audio features.