Artificial Intelligence without Bias

Livrables

Research documentation on accounting for bias in results [to be updated]

Research documentation on accounting for bias in results (Leader: UNIPI, participation of SCHUFA, SOTON-LS, LUH-L3S) (M24, M42): Reports on research progress and results.

Report on WP2 Integration and application [to be updated]

Report on WP2 Integration and application (Leader: CERTH, participation of OU, GESIS-CSS, LUH-IRI) (M24, M42): Reports on integration and application activities related to bias mitigation in algorithms (in collaboration with WP4).

Research documentation on mitigating bias in algorithms [to be updated]

D2.1a/b: Research documentation on mitigating bias in algorithms (Leader: OU, participation of GESIS-CSS, CERTH, LUH-IRI) (M24, M42): Reports on research progress and results.

Training Report [to be updated]

Training Report (Leader: UNI-KLU; participation: all) (M24, M48). Reports on planning and implementation of training activities and resources.

Report on use cases and applications

Report on use cases and applications (Leader: SCHUFA, participation: all) (M42). Report on the application of the NoBIAS research in real use cases.

Report on WP3 integration and application [to be updated]

Report on WP3 integration and application (Leader: SCHUFA, participation of UNIPI, SOTON-LS, LUH-L3S) (M24, M42): Reports on integration and application activities related to accounting for bias in results (in collaboration with WP4).

Report on WP1 integration and application [to be updated]

Report on WP1 integration and application (Leader: LUH-L3S, participation of GESIS-DAS, CERTH, UNIPI, KULEUVEN) (M24, M42): Reports on integration and application activities related to understanding bias in data (in collaboration with WP4).

Research documentation on understanding bias in data [to be updated]

D1.1a/b: Research documentation on understanding bias in data (Leader: KULEUVEN, participation of GESIS-DAS, CERTH, UNIPI, LUH-L3S) (M24, M42): Reports on research progress and results.

Living Document on Bias

Living Document and Book on Bias SOTONECS There is to date no established resource that combines the interdisciplinary expertise necessary to address bias in AIdriven decision making NoBIAS will deliver this resource through the establishment of a living training document that will begin with core contributions from academic partners M6 and will be developed by the NoBIAS researchers as part of the interdisciplinary training stream and ultimately be published as a book M42 This process will develop substantive knowledge of interdisciplinary approaches and generic team working and collaborative writing skills

NoBIAS Best Practices and Policy Advice

NoBIAS Policy Advice and Best Practices (UNIPI): NoBIAS will promote the proactive participation of ESRs in initiatives for policy making and best practices with the results of their research and the use cases from their secondments. This will be facilitated by UNIPI's participation in the IEEE P7003 Working Group on Algorithmic Bias Considerations . An introduction to this will be given during Summer School 1. Training of ESRs will benefit from their involvement in such and similar standardization initiatives, e.g., by exploiting outcomes like conceptualizations, methodologies, recommendations, and use cases (such as the bias taxonomy being developed in IEEE P7003).

Book on Bias

Living Document and Book on Bias (SOTON-ECS): There is to date no established resource that combines the interdisciplinary expertise necessary to address bias in AI-driven decision making. NoBIAS will deliver this resource through the establishment of a “living training document” that will begin with core contributions from academic partners (M6) and will be developed by the NoBIAS researchers as part of the interdisciplinary training stream and ultimately be published as a book (M42). This process will develop substantive knowledge of interdisciplinary approaches and generic team working and collaborative writing skills.

NoBias Testbed

NoBIAS testbed (LUH-L3S): NoBIAS will create an integrated technology testbed as a crystallization point for the methods and algorithms developed in the project. It will support the evaluation of methods developed in the IRPs in a larger context, foster collaboration, and integrate results from all projects. The testbed will include an open source library of algorithms and methods developed during the project, fostering openness and reproducibility, and facilitating research on related problems.

Dissemination report [to be updated]

Dissemination Report Leader LUHL3S participation all M24 M48 Reports on the setup of the project website and social media accounts implementation of the dissemination strategy planning and implementation of the projectrelated events and activities

Publications

Declarative Reasoning on Explanations Using Constraint Logic Programming

Auteurs: Laura State; Salvatore Ruggieri; Franco Turini
Publié dans: Logics in Artificial Intelligence, JELIA 2023, 2023, Page(s) 132-141, ISBN 9783031436185
Éditeur: Springer Science and Business Media Deutschland GmbH
DOI: 10.1007/978-3-031-43619-2_10

Enhancing Fairness through Reweighting: A Path to Attain the Sufficiency Rule

Auteurs: Xuan Zhao, Klaus Broelemann, Salvatore Ruggieri and Gjergji Kasneci
Publié dans: European Conference on Artificial Intelligence, ECAI 2024, 2024
Éditeur: Accepted for publication
DOI: 10.48550/arxiv.2408.14126

Logic programming for XAI: A technical perspective

Auteurs: Laura State
Publié dans: ICLP Workshops, volume 2970 of CEUR Workshop Proceedings, Numéro 1, 2021
Éditeur: CEUR-WS.org

Quantile Encoder: Tackling High Cardinality Categorical Features in Regression Problems

Auteurs: Carlos Mougan; David Masip; Jordi Nin; Oriol Pujol
Publié dans: Modeling Decisions for Artificial Intelligence, Numéro 5, 2021
Éditeur: Springer
DOI: 10.1007/978-3-030-85529-1_14

Desiderata for Explainable AI in statistical production systems of the European Central Bank

Auteurs: Carlos Mougan, Georgios Kanellos, Thomas Gottron
Publié dans: Workshop on bias and fairness in AI at ECMLPKDD, Numéro 1, 2021
Éditeur: Springer International Publishing
DOI: 10.1007/978-3-030-93736-2_42

Can We Trust Fair-AI?

Auteurs: Ruggieri S.; Alvarez J. M.; Pugnana A.; State L.; Turini F.
Publié dans: AAAI Conference on Artificial Intelligence, AAAI 2023, 2023, Page(s) 15421-15430, ISBN 9781577358800
Éditeur: AAAI Press
DOI: 10.1609/aaai.v37i13.26798

Careful Explanations: A Feminist Perspective on XAI

Auteurs: Laura State, Miriam Fahimi
Publié dans: European Workshop on Algorithmic Fairness, EWAF 2023, 2023, Page(s) -
Éditeur: CEUR-WS.org

Introducing explainable supervised machine learning into interactive feedback loops for statistical production system

Auteurs: Carlos Mougan, George Kanellos, Johannes Micheler, Jose Martinez, Thomas Gottron
Publié dans: Irving Fisher Committee (IFC) - Bank of Italy workshop on Data science in central banking: Applications and tools, 2021
Éditeur: Arxiv

Fairness Implications of Encoding Protected Categorical Attributes

Auteurs: Carlos Mougan; Jose Manuel Alvarez; Salvatore Ruggieri; Steffen Staab
Publié dans: AAAI/ACM Conference on AI, Ethics, and Society, AIES 2023, 2023, Page(s) 454-465, ISBN 9798400702310
Éditeur: Association for Computing Machinery, Inc
DOI: 10.1145/3600211.3604657

The Explanation Dialogues: Understanding How Legal Experts Reason About XAI Methods

Auteurs: Laura State, Alejandra Bringas Colmenarejo, Andrea Beretta, Salvatore Ruggieri, Franco Turini, Stephanie Law
Publié dans: European Workshop on Algorithmic Fairness, EWAF 2023, 2023, ISBN 161300733442
Éditeur: CEUR Workshop Proceedings

Sum of Group Error Differences: A Critical Examination of Bias Evaluation in Biometric Verification and a Dual-Metric Measure

Auteurs: Alaa Elobaid, Nathan Ramoly, Lara Younes, Symeon Papadopoulos, Eirini Ntoutsi, Ioannis Kompatsiaris
Publié dans: 2024 IEEE 18th International Conference on Automatic Face and Gesture Recognition (FG), Numéro 2, 2024, Page(s) 1-9
Éditeur: IEEE
DOI: 10.1109/fg59268.2024.10582012

Time to Question if We Should: Data-Driven and Algorithmic Tools in Public Employment Services

Auteurs: Pieter Delobelle, Kristen M. Scott, Sonja Mei Wang, Milagros Miceli, David Hartmann, Tianling Yang, Elena Murasso, Karolina Sztandar-Sztanderska, Bettina Berendt
Publié dans: International workshop on Fair, Effective And Sustainable Talent management using data science, 2021
Éditeur: FEAST Workshop

Explainability in Practice: Estimating Electrification Rates from Mobile Phone Data in Senegal

Auteurs: Laura State; Hadrien Salat; Stefania Rubrichi; Zbigniew Smoreda
Publié dans: World Conference on eXplainable Artificial Intelligence, xAI 2023, 2023, Page(s) 110-125, ISBN 9783031440663
Éditeur: Springer Science and Business Media Deutschland GmbH
DOI: 10.1007/978-3-031-44067-0_6

Causal Fairness-Guided Dataset Reweighting using Neural Networks

Auteurs: Zhao X.; Broelemann K.; Ruggieri S.; Kasneci G.
Publié dans: IEEE International Conference on Big Data (BigData 2023), 2023, Page(s) 1386-1394, ISBN 9798350324464
Éditeur: Institute of Electrical and Electronics Engineers Inc.
DOI: 10.1109/bigdata59044.2023.10386836

Constructing Meaningful Explanations: Logic-based Approaches

Auteurs: Laura State
Publié dans: AAAI/ACM Conference on AI, Ethics, and Society, AIES 2022, 2022, Page(s) 916, ISBN 978-1-4503-9247-1
Éditeur: ACM
DOI: 10.1145/3514094.3539544

A Survey on Bias in Visual Datasets

Auteurs: Simone Fabbrizzi, Symeon Papadopoulos, Eirini Ntoutsi, Ioannis Kompatsiaris
Publié dans: Numéro 1, 2021
Éditeur: Arxiv

A Causal Framework for Evaluating Deferring Systems

Auteurs: Filippo Palomba, Andrea Pugnana, José M. Álvarez, Salvatore Ruggieri
Publié dans: 2024
Éditeur: Unpublished manuscript
DOI: 10.48550/arxiv.2405.18902

Semantic Web Technologies and Bias in Artificial Intelligence: A Systematic Literature Review

Auteurs: Paula Reyero Lobo, Enrico Daga, Harith Alani, Miriam Fernandez
Publié dans: Semantic Web Journal, 2021
Éditeur: Semantic Web Journal

Uncovering Algorithmic Discrimination: An Opportunity to Revisit the Comparator

Auteurs: José M. Álvarez, Salvatore Ruggieri
Publié dans: 2024
Éditeur: Unpublished manuscript
DOI: 10.48550/arxiv.2405.13693

Causal Perception

Auteurs: Alvarez, Jose M.; Ruggieri, Salvatore
Publié dans: 2024
Éditeur: Unpublished manuscript
DOI: 10.48550/arxiv.2401.13408

Data Privacy Issues in Big Biomedical Data

Auteurs: Maria-Esther Vidal, Mayra Russo, Philipp Rohde
Publié dans: 2021
Éditeur: Nomos Verlagsgese llschaft

Empowering machine learning models with contextual knowledge for enhancing the detection of eating disorders in social media posts

Auteurs: José Alberto Benítez-Andrades, María Teresa García-Ordás, Mayra Russo, Ahmad Sakor, Luis Daniel Fernandes Rotger, Maria-Esther Vidal
Publié dans: Semantic Web, Numéro 14, 2023, Page(s) 873-892, ISSN 1570-0844
Éditeur: IOS Press
DOI: 10.3233/sw-223269

Bias-aware ranking from pairwise comparisons

Auteurs: Antonio Ferrara, Francesco Bonchi, Francesco Fabbri, Fariba Karimi, Claudia Wagner
Publié dans: Data Mining and Knowledge Discovery, Numéro 38, 2024, Page(s) 2062-2086, ISSN 1384-5810
Éditeur: Kluwer Academic Publishers
DOI: 10.1007/s10618-024-01024-z

Policy advice and best practices on bias and fairness in AI

Auteurs: Jose M. Alvarez; Alejandra Bringas Colmenarejo; Alaa Elobaid; Simone Fabbrizzi; Miriam Fahimi; Antonio Ferrara; Siamak Ghodsi; Carlos Mougan; Ioanna Papageorgiou; Paula Reyero; Mayra Russo; Kristen M. Scott; Laura State; Xuan Zhao; Salvatore Ruggieri
Publié dans: Ethics and information technology, Numéro 26, 2024, Page(s) 31, ISSN 1572-8439
Éditeur: Springer
DOI: 10.1007/s10676-024-09746-w

Predicting and explaining employee turnover intention

Auteurs: Matilde Lazzari; Jose M. Alvarez; Salvatore Ruggieri
Publié dans: International Journal of Data Science and Analytics, Numéro 14, 2022, Page(s) 279–292, ISSN 2364-4168
Éditeur: Springer
DOI: 10.1007/s41060-022-00329-w

Measuring Shifts in Attitudes Towards COVID-19 Measures in Belgium

Auteurs: Kristen Scott, Pieter Delobelle, Bettina Berendt
Publié dans: Computational Linguistics in the Netherlands Journal, Numéro 11, 2021, Page(s) 161 - 171, ISSN 2211-4009
Éditeur: Computational Linguistics in the Netherlands

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