Výsledky vyhledávání - "Avital Shulner-Tal"

Mitigating Bias in Algorithmic Systems—A Fish-eye View

Autor: Kalia Orphanou, Jahna Otterbacher, Styliani Kleanthous, Khuyagbaatar Batsuren, Fausto Giunchiglia, Veronika Bogina, Avital Shulner Tal, Alan Hartman, Tsvi Kuflik

Publikováno v: ACM Computing Surveys. 55:1-37

Mitigating bias in algorithmic systems is a critical issue drawing attention across communities within the information and computer sciences. Given the complexity of the problem and the involvement of multiple stakeholders—including developers, end

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::77bf871241a2b6c8a569ebda80a4104f
https://doi.org/10.1145/3527152

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Enhancing Fairness Perception – Towards Human-Centred AI and Personalized Explanations Understanding the Factors Influencing Laypeople’s Fairness Perceptions of Algorithmic Decisions

Autor: Avital Shulner-Tal, Tsvi Kuflik, Doron Kliger

Publikováno v: International Journal of Human–Computer Interaction. 39:1455-1482

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::1926e0bef4ba074c388968ffe6530a56
https://doi.org/10.1080/10447318.2022.2095705

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5th Workshop on Fairness in User Modeling, Adaptation, and Personalization (FairUMAP 2022)

Autor: Styliani Kleanthous, Bamshad Mobasher, Tsvika Kuflik, Bettina Berendt, Robin Burke, Jahna Otterbacher, Nasim Sonboli, Avital Shulner Tal

Publikováno v: Adjunct Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization.

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::070cebe7ccdda3c5be3cc06c84060dad
https://doi.org/10.1145/3511047.3536348

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Educating Software and AI Stakeholders About Algorithmic Fairness, Accountability, Transparency and Ethics

Autor: Tsvi Kuflik, Veronika Bogina, Avital Shulner-Tal, Alan Hartman

Publikováno v: International Journal of Artificial Intelligence in Education. 32:808-833

This paper discusses educating stakeholders of algorithmic systems (systems that apply Artificial Intelligence/Machine learning algorithms) in the areas of algorithmic fairness, accountability, transparency and ethics (FATE). We begin by establishing

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b631d8feac9f702bfa1a790dc8d79ac6
https://doi.org/10.1007/s40593-021-00248-0

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TExSS 22: Transparency and Explanations in Smart Systems

Autor: Tsvi Kuflik, Jonathan Dodge, Styliani Kleanthous Loizou, Brian Lim, Carina Negreanu, Avital Shulner-Tal, Simone Stumpf

Publikováno v: 27th International Conference on Intelligent User Interfaces.

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::e61fedc7ac802150e8d7eb6b66ff21ad
https://doi.org/10.1145/3490100.3511165

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Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system

Autor: Avital Shulner-Tal, Tsvi Kuflik, Doron Kliger

Publikováno v: Ethics and Information Technology. 24

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::6b5261d719589875e18041fa132f95ba
https://doi.org/10.1007/s10676-022-09623-4

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Recent Studies of XAI - Review

Autor: Tsvi Kuflik, Avital Shulner Tal, Ionela G. Mocanu, Zhongli Filippo Hu, Shabanam Najafian

Publikováno v: Adjunct Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization (UMAP)
UMAP (Adjunct Publication)

Over the past years, there has been an increasing concern regarding the risk of bias and discrimination in algorithmic systems, which re- ceived significant attention amongst the research communities. To ensure the system’s fairness, various method

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f1d63e573aef1b8d058685d700d8c1a3

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UMAP 2020 Fairness in User Modeling, Adaptation and Personalization (FairUMAP 2020) Chairs' Welcome

Autor: Jahna Otterbacher, Styliani Kleanthous, Michael D. Ekstrand, Bettina Berendt, Avital Shulner Tal, Bamshad Mobasher

Publikováno v: UMAP (Adjunct Publication)

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::82249cfd2425751b1cdec59dab08ad3d
https://doi.org/10.1145/3386392.3399565

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FairUMAP 2020: The 3rd Workshop on Fairness in User Modeling, Adaptation and Personalization

Autor: Bamshad Mobasher, Bettina Berendt, Michael D. Ekstrand, Styliani Kleanthous, Jahna Otterbacher, Avital Shulner Tal

Publikováno v: UMAP (Adjunct Publication)
Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization

The 3rd FairUMAP workshop brings together researchers working at the intersection of user modeling, adaptation, and personalization on the one hand, and bias, fairness and transparency in algorithmic systems on the other hand.

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d291c760d319209114139e4a7d14c698
https://doi.org/10.1145/3340631.3398671

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Akademický článek

Fairness in Machine Learning: A Survey.

Autor: Caton, Simon¹ (AUTHOR) simon.caton@ucd.ie, Haas, Christian² (AUTHOR) christianhaas@unomaha.edu

Publikováno v: ACM Computing Surveys. Jul2024, Vol. 56 Issue 7, p1-38. 38p.

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