Artificial Intelligence without Bias

Livrables

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

Publications

A Survey on Bias in Visual Datasets

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

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

Data Privacy Issues in Big Biomedical Data

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

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

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

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

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

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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