Artificial Intelligence without Bias

Deliverables

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

D11ab Research documentation on understanding bias in data Leader KULEUVEN participation of GESISDAS CERTH UNIPI LUHL3S 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 GESISCSS LUHIRI 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]

D21ab Research documentation on mitigating bias in algorithms Leader OU participation of GESISCSS CERTH LUHIRI M24 M42 Reports on research progress and results

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

Research documentation on accounting for bias in results Leader UNIPI participation of SCHUFA SOTONLS LUHL3S M24 M42 Reports on research progress and results

Report on WP1 integration and application [to be updated]

Report on WP1 integration and application Leader LUHL3S participation of GESISDAS CERTH UNIPI KULEUVEN M24 M42 Reports on integration and application activities related to understanding bias in data in collaboration with WP4

Training Report [to be updated]

Training Report Leader UNIKLU participation all M24 M48 Reports on planning and implementation of training activities and resources

Report on WP3 integration and application [to be updated]

Report on WP3 integration and application Leader SCHUFA participation of UNIPI SOTONLS LUHL3S M24 M42 Reports on integration and application activities related to accounting for bias in results in collaboration with WP4

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

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

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Publications

A Survey on Bias in Visual Datasets

Author(s): Simone Fabbrizzi, Symeon Papadopoulos, Eirini Ntoutsi, Ioannis Kompatsiaris
Published in: 1, 2021
Publisher: Arxiv

Opening the black box: a primer for anti-discrimination

Author(s): Ruggieri, S.; Giannotti, F.; Guidotti, R.; Monreale, A.; Pedreschi, D.; Turini, F.
Published in: 1, 2020
Publisher: CINECA IRIS Institutional Research Information System

Prior Signal Editing for Graph Filter Posterior Fairness Constraints

Author(s): Krasanakis, Emmanouil; Papadopoulos, Symeon; Kompatsiaris, Ioannis; Symeonidis, Andreas
Published in: 1, 2021
Publisher: Arxiv

A Survey on Bias in Visual Datasets

Author(s): Fabbrizzi, Simone; Papadopoulos, Symeon; Ntoutsi, Eirini; Kompatsiaris, Ioannis
Published in: 1, 2021
Publisher: Arxiv

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

Author(s): Paula Reyero Lobo, Enrico Daga, Harith Alani, Miriam Fernandez
Published in: Semantic Web Journal, 2021
Publisher: Semantic Web Journal

Measuring Shifts in Attitudes Towards COVID-19 Measures in Belgium Using Multilingual BERT

Author(s): Scott, Kristen; Delobelle, Pieter; Berendt, Bettina
Published in: 1, 2021
Publisher: Arxiv

Data Privacy Issues in Big Biomedical Data

Author(s): Maria-Esther Vidal, Mayra Russo, Philipp Rohde
Published in: 2021
Publisher: Nomos Verlagsgese llschaft

FAE: A Fairness-Aware Ensemble Framework

Author(s): Vasileios Iosifidis; Besnik Fetahu; Eirini Ntoutsi
Published in: BigData, 1, 2019
Publisher: IEEE Computer Society
DOI: 10.1109/bigdata47090.2019.9006487

Logic programming for XAI: A technical perspective

Author(s): Laura State
Published in: ICLP Workshops, volume 2970 of CEUR Workshop Proceedings., 1, 2021
Publisher: CEUR-WS

Quantile Encoder: Tackling High Cardinality Categorical Features in Regression Problems

Author(s): Carlos Mougan; David Masip; Jordi Nin; Oriol Pujol
Published in: Modeling Decisions for Artificial Intelligence, 5, 2021
Publisher: Springer
DOI: 10.1007/978-3-030-85529-1_14

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

Author(s): Carlos Mougan, Georgios Kanellos, Thomas Gottron
Published in: Workshop on bias and fairness in AI at ECMLPKDD, 1, 2021
Publisher: Springer International Publishing
DOI: 10.1007/978-3-030-93736-2_42

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

Author(s): Navarro, Carlos Mougan; Kanellos, Georgios; Gottron, Thomas
Published in: 1, 2021
Publisher: Springer International Publishing
DOI: 10.1007/978-3-030-93736-2_42

Logic programming for XAI: A technical perspective

Author(s): Laura State
Published in: ICLP Workshops, volume 2970 of CEUR Workshop Proceedings., 1, 2021
Publisher: CEUR-WS

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

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

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

Author(s): Pieter Delobelle, Kristen M. Scott, Sonja Mei Wang, Milagros Miceli, David Hartmann, Tianling Yang, Elena Murasso, Karolina Sztandar-Sztanderska, Bettina Berendt
Published in: International workshop on Fair, Effective And Sustainable Talent management using data science, 2021
Publisher: FEAST Workshop

Quantile Encoder: Tackling High Cardinality Categorical Features in Regression Problems

Author(s): Carlos Mougan; David Masip; Jordi Nin; Oriol Pujol
Published in: Modeling Decisions for Artificial Intelligence ISBN: 9783030855284, 5, 2021
Publisher: Springer
DOI: 10.1007/978-3-030-85529-1_14

Applying Fairness Constraints on Graph Node Ranks Under Personalization Bias

Author(s): Krasanakis E., Papadopoulos S., Kompatsiaris I.
Published in: Complex Networks & Their Applications IX. COMPLEX NETWORKS 2020, Studies in Computational Intelligence, 944, 2020, ISBN 978-3-030-65350-7
Publisher: Springer, Cham
DOI: 10.1007/978-3-030-65351-4_49

Introduction to The Special Section on Bias and Fairness in AI

Author(s): Toon Calders; Eirini Ntoutsi; Mykola Pechenizkiy; Bodo Rosenhahn; Salvatore Ruggieri
Published in: SIGKDD Explorations, 23(1):1, 1 - 3, 4, 2021, ISSN 1931-0145
Publisher: SIGKDD Explor. Newsl.
DOI: 10.1145/3468507.3468509

Introduction to The Special Section on Bias and Fairness in AI

Bias in Data-driven AI Systems -- An Introductory Survey

Author(s): Ntoutsi, Eirini; Fafalios, Pavlos; Gadiraju, Ujwal; Iosifidis, Vasileios; Nejdl, Wolfgang; Vidal, Maria-Esther; Ruggieri, Salvatore; Turini, Franco; Papadopoulos, Symeon; Krasanakis, Emmanouil; Kompatsiaris, Ioannis; Kinder-Kurlanda, Katharina; Wagner, Claudia; Karimi, Fariba; Fernandez, Miriam; Alani, Harith; Berendt, Bettina; Kruegel, Tina; Heinze, Christian; Broelemann, Klaus; Kasneci, Gjergji;
Published in: Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 10, 2020, ISSN 1942-4795
Publisher: Wiley Online Library
DOI: 10.1002/widm.1356

Deliverables

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