EWAF 2023
European Workshop on Algorithmic Fairness

Table of Contents

• Preface
  Jose M. Alvarez, Alessandro Fabris, Christoph Heitz, Corinna Hertweck, Michele Loi, Meike Zehlike
  

Computer Science Track

• Approximate Inference for the Bayesian Fairness Framework
  Andreas Nikolaos Athanasopoulos, Amanda Belfrage, David Berg Marklund, Christos Dimitrakakis
  
• An Open-Source Toolkit to Generate Biased Datasets
  Joachim Baumann, Alessandro Castelnovo, Riccardo Crupi, Nicole Inverardi, Daniele Regoli
  
• Fair Machine Learning Through Post-processing: The Case of Predictive Parity
  Joachim Baumann, Anikó Hannák, Christoph Heitz
  
• Provable Fairness for Neural Network Models Using Formal Verification
  Giorgian Borca-Tasciuc, Xingzhi Guo, Stanley Bak, Steven Skiena
  
• Towards a Framework for the Global Assessment of Sensitive Attribute Bias Within Binary Classification Algorithms
  Adrian Byrne, Ivan Caffrey, Quan Le
  
• The Case for Correctability in Fair Machine Learning
  Mattia Cerrato, Alesia Vallenas Coronel, Marius Köppel
  
• A Search Engine for Algorithmic Fairness Datasets
  Alessandro Fabris, Fabio Giachelle, Alberto Piva, Gianmaria Silvello, Gian Antonio Susto
  
• Affinity Clustering Framework for Data Debiasing Using Pairwise Distribution Discrepancy
  Siamak Ghodsi, Eirini Ntoutsi
  
• Explainability Methods to Detect and Measure Discrimination in Machine Learning Models
  Sofie Goethals, David Martens, Toon Calders
  
• Model-Agnostic Auditing: A Lost Cause?
  Sakina Hansen, Joshua Loftus
  
• FairnessLab: A Consequence-Sensitive Bias Audit and Mitigation Toolkit
  Corinna Hertweck, Joachim Baumann, Michele Loi, Christoph Heitz
  
• Is a Fairness Metric Score Enough to Assess Discrimination Biases in Machine Learning?
  Fanny Jourdan, Ronan Pons, Nicholas Asher, Jean-Michel Loubes, Laurent Risser
  
• Breaking Bias: How Optimal Transport Can Help to Tackle Gender Biases in NLP Based Job Recommendation Systems?
  Fanny Jourdan, Titon Tshiongo-Kaninku, Nicholas Asher, Jean-Michel Loubes, Laurent Risser
  
• Arbitrary Decisions Are a Hidden Cost of Differentially Private Training
  Bogdan Kulynych, Hsiang Hsu, Carmela Troncoso, Flavio Calmon
  
• It's About Time: Counterfactual Fairness and Temporal Depth
  Joshua Loftus
  
• Body Measurement Prediction Fairness
  Alex Loosley, Amrollah Seifoddini, Alessandro Canopoli, Meike Zehlike
  
• Closing the Loop: Feedback Loops and Biases in Automated Decision-Making
  Nicolò Pagan, Joachim Baumann, Ezzat Elokda, Giulia De Pasquale, Saverio Bolognani, Anikó Hannák
  
• Pagerank Fairness in Networks
  Evaggelia Pitoura
  
• Fairness and Diversity in Information Access Systems
  Lorenzo Porcaro, Carlos Castillo, Emilia Gómez, João Vinagre
  
• Formally Verified Algorithmic Fairness Using Information-Flow Tools
  Samuel Teuber, Bernhard Beckert
  
• How Differential Robustness Creates Disparate Impact: A European Case Study
  Charles Wan, Leid Zejnilović, Susana Lavado
  

Philosophy Track

• Unification, Extension, and Interpretation of Group Fairness Metrics for ML-Based Decision-Making
  Joachim Baumann, Corinna Hertweck, Michele Loi, Christoph Heitz
  
• A Causal Analysis of Harm
  Sander Beckers, Hana Chockler, Joseph Y. Halpern
  
• How Data Quality Determines AI Fairness: The Case of Automated Interviewing
  Lou Therese Brandner, Philipp Mahlow, Anna Wilken, Annika Wölke, Hazar Harmouch, Simon David Hirsbrunner
  
• Classification Parity, Causal Equal Protection and Algorithmic Fairness
  Marcello Di Bello, Nicolo Cangiotti, Michele Loi
  
• Social Influence for Societal Interest: A Pro-Ethical Framework for Improving Human Decision-Making Through Multi-Stakeholder Recommender Systems
  Matteo Fabbri
  
• Through the Sands of Time: A Reliabilistic Account of Justified Credence in the Trustworthiness of AI Systems
  Andrea Ferrario
  
• Qualification and Quantification of Fairness for Sustainable Mobility Policies
  Camilla Quaresmini, Eugenia Villa, Valentina Breschi, Viola Schiaffonati, Mara Tanelli
  
• Using Fairness Metrics as Decision-Making Procedures: Algorithmic Fairness and the Problem of Action-Guidance
  Otto Sahlgren
  
• A 'Little Ethics' for Algorithmic Decision-Making
  Teresa Scantamburlo, Giovanni Grandi
  
• A Multidomain Relational Framework to Guide Institutional AI Research and Adoption
  Vincent Straub, Deborah Morgan, Youmna Hashem, John Francis, Saba Esnaashari, Jonathan Bright
  
• Complex Equality and Algorithmic Fairness: A Social Goods Approach to Make Statistical Fairness Metrics Less Abstract
  Bauke Wielinga
  
• Fairness After Intervention: Towards a Theory of Substantial Fairness for Machine Learning
  Sebastian Zezulka
  

Social Sciences Track

• Ethnic Classifications in Algorithmic Decision-Making Processes
  Sofia Jaime, Christoph Kern
  
• When Small Decisions Have Big Impact: Fairness Implications of Algorithmic Profiling Schemes
  Christoph Kern, Ruben Bach, Hannah Mautner, Frauke Kreuter
  
• Artificial Intelligence in Higher Education: Ethical Concerns for Students With Disabilities
  Oriane Pierrès, Alireza Darvishy, Markus Christen
  
• What If? Using Multiverse Analysis to Evaluate the Influence of Model Design Decisions on Algorithmic Fairness
  Jan Simson, Florian Pfisterer, Christoph Kern
  
• Careful Explanations: A Feminist Perspective on XAI
  Laura State, Miriam Fahimi
  
• A Reflection on How Cross-Cultural Perspectives on the Ethics of Facial Analysis AI Can Inform EU Policymaking
  Chiara Ullstein, Severin Engelmann, Orestis Papakyriakopoulos, Jens Grossklags
  

Law & Policy Track

• Algorithmic Bias in the Context of European Union Anti-Discrimination Directives
  Ahmet Bilal Aytekin
  
• Living with Opaque Technologies: Insights for AI from Digital Simulations
  Eugenia Cacciatori, Enzo Fenoglio, Emre Kazim
  
• Improving Fairness and Cybersecurity in the Artificial Intelligence Act
  Gabriele Carovano, Alexander Meinke
  
• From Digital Nudging to Users' Self-Determination: Explainability as a Framework for the Effective Implementation of the Transparency Requirements for Recommender Systems Set by the Digital Services Act of the European Union
  Matteo Fabbri
  
• Addressing Automation Bias through Verifiability
  Lukas Hondrich, Hannah Ruschemeier
  
• Fairness in Machine Learning as 'Algorithmic Positive Action'
  Jan-Laurin Müller
  
• Mitigating Diversity Biases of AI in the Labor Market
  Carlotta Rigotti, Alexandre Puttick, Eduard Fosch-Villaronga, Mascha Kurpicz-Briki
  
• Certification Labels for Trustworthy AI
  Nicolas Scharowski, Michaela Benk, Swen Kühne, Léane Wettstein, Florian Brühlmann
  
• The Explanation Dialogues: Understanding How Legal Experts Reason About XAI Methods
  Laura State, Alejandra Bringas Colmenarejo, Andrea Beretta, Salvatore Ruggieri, Franco Turini, Stephanie Law
  
• Algorithmic Unfairness Through the Lens of EU Non-Discrimination Law
  Hilde Weerts, Raphaële Xenidis, Fabien Tarissan, Henrik Palmer Olsen, Mykola Pechenizkiy
  
• Assessing the Legality of Using the Category of Race and Ethnicity in Clinical Algorithms - the EU Anti-Discrimination Law Perspective
  Malwina Anna Wojcik
  
• Compatibility of Fairness Metrics With EU Non-Discrimination Law: A Legal and Technical Case Study
  Yasaman Yousefi, Lisa Koutsoviti-Koumeri, Magali Legast, Christoph Schommer, Koen Vanhoof, Axel Legay