abstract

Tell Me What You Know: GDPR Implications on Designing Transparency and Accountability for News Recommender Systems

Authors:

Luciana Monteiro Krebs,

Oscar Luis Alvarado Rodriguez,

Pierre Dewitte,

Laurens Naudts,

Katrien VerbertAuthors Info & Claims

CHI EA '19: Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems

Paper No.: LBW2610, Pages 1 - 6

https://doi.org/10.1145/3290607.3312808

Published: 02 May 2019 Publication History

Abstract

The GDPR has a significant impact on the way users interact with technologies, especially the everyday platforms used to personalize news and related forms of information. This paper presents the initial results from a study whose primary objective is to empirically test those platforms' level of compliance with the so-called 'right to explanation'. Four research topics considered as gaps in existing legal and HCI scholarship originated from the project's initial phase, namely (1) GDPR compliance through user-centered design; (2) the inclusion of values in the system; (3) design considerations regarding interaction strategies, algorithmic experience, transparency, and explanations; and (4) technical challenges. The second phase is currently ongoing and allows us to make some observations regarding the registration process and the privacy policies of three categories of news actors: first-party content providers, news aggregators and social media platforms.

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Cited By

Chatti MGuesmi MMuslim A(2024)Visualization for Recommendation Explainability: A Survey and New PerspectivesACM Transactions on Interactive Intelligent Systems10.1145/367227614:3(1-40)Online publication date: 11-Jun-2024
https://dl.acm.org/doi/10.1145/3672276
Bauer CBagchi CHundogan Ovan Es K(2024)Where Are the Values? A Systematic Literature Review on News Recommender SystemsACM Transactions on Recommender Systems10.1145/36548052:3(1-40)Online publication date: 28-Mar-2024
https://dl.acm.org/doi/10.1145/3654805
Eren MBhattarai MSolovyev NRichards LYus RNicholas CAlexandrov B(2022)One-Shot Federated Group Collaborative Filtering2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA)10.1109/ICMLA55696.2022.00107(647-652)Online publication date: Dec-2022
https://doi.org/10.1109/ICMLA55696.2022.00107
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Tell Me What You Know: GDPR Implications on Designing Transparency and Accountability for News Recommender Systems

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cover image ACM Conferences

CHI EA '19: Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems

May 2019

3673 pages

ISBN:9781450359719

DOI:10.1145/3290607

General Chairs:
Stephen Brewster
University of Glasgow, Scotland, UK
,
Geraldine Fitzpatrick
TU Wien, Austria
,
Program Chairs:
Anna Cox
University College London, UK
,
Vassilis Kostakos
University of Melbourne, Australia

Copyright © 2019 Owner/Author.

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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SIGCHI: ACM Special Interest Group on Computer-Human Interaction

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Association for Computing Machinery

New York, NY, United States

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Published: 02 May 2019

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CHI '19

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CHI '19: CHI Conference on Human Factors in Computing Systems

May 4 - 9, 2019

Glasgow, Scotland Uk

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Overall Acceptance Rate 6,164 of 23,696 submissions, 26%

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Cited By

Chatti MGuesmi MMuslim A(2024)Visualization for Recommendation Explainability: A Survey and New PerspectivesACM Transactions on Interactive Intelligent Systems10.1145/367227614:3(1-40)Online publication date: 11-Jun-2024
https://dl.acm.org/doi/10.1145/3672276
Bauer CBagchi CHundogan Ovan Es K(2024)Where Are the Values? A Systematic Literature Review on News Recommender SystemsACM Transactions on Recommender Systems10.1145/36548052:3(1-40)Online publication date: 28-Mar-2024
https://dl.acm.org/doi/10.1145/3654805
Eren MBhattarai MSolovyev NRichards LYus RNicholas CAlexandrov B(2022)One-Shot Federated Group Collaborative Filtering2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA)10.1109/ICMLA55696.2022.00107(647-652)Online publication date: Dec-2022
https://doi.org/10.1109/ICMLA55696.2022.00107
Mitova EBlassnig SStrikovic EUrman AHannak Ade Vreese CEsser F(2022)News recommender systems: a programmatic research reviewAnnals of the International Communication Association10.1080/23808985.2022.214214947:1(84-113)Online publication date: 11-Nov-2022
https://doi.org/10.1080/23808985.2022.2142149
Wang H(2022)Transparency as Manipulation? Uncovering the Disciplinary Power of Algorithmic TransparencyPhilosophy & Technology10.1007/s13347-022-00564-w35:3Online publication date: 22-Jul-2022
https://doi.org/10.1007/s13347-022-00564-w
Suresh HGomez SNam KSatyanarayan AKitamura YQuigley AIsbister KIgarashi TBjørn PDrucker S(2021)Beyond Expertise and Roles: A Framework to Characterize the Stakeholders of Interpretable Machine Learning and their NeedsProceedings of the 2021 CHI Conference on Human Factors in Computing Systems10.1145/3411764.3445088(1-16)Online publication date: 6-May-2021
https://dl.acm.org/doi/10.1145/3411764.3445088
Bellogín ASaid A(2021)Improving accountability in recommender systems research through reproducibilityUser Modeling and User-Adapted Interaction10.1007/s11257-021-09302-x31:5(941-977)Online publication date: 21-Oct-2021
https://doi.org/10.1007/s11257-021-09302-x
Zheng YToribio J(2021)The role of transparency in multi-stakeholder educational recommendationsUser Modeling and User-Adapted Interaction10.1007/s11257-021-09291-x31:3(513-540)Online publication date: 7-Apr-2021
https://doi.org/10.1007/s11257-021-09291-x
Monteiro-Krebs LZaman BHtun NCaregnato SGeerts D(2021)Depicting Recommendations in Academia: How ResearchGate Communicates with Its Users (via Design or upon Request) About Recommender AlgorithmsData and Information in Online Environments10.1007/978-3-030-77417-2_1(3-25)Online publication date: 15-Jun-2021
https://doi.org/10.1007/978-3-030-77417-2_1
Smirnova ASuvorova A(2021)Machine Learning Models Interpretations: User Demands ExplorationDigital Transformation and Global Society10.1007/978-3-030-65218-0_8(107-116)Online publication date: 9-Jan-2021
https://doi.org/10.1007/978-3-030-65218-0_8
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