Stop the war!Остановите войну!
for scientists:
default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCIDEWAF 2023: Winterthur, Switzerland
- Jose M. Alvarez, Alessandro Fabris, Christoph Heitz, Corinna Hertweck, Michele Loi, Meike Zehlike:
Proceedings of the 2nd European Workshop on Algorithmic Fairness, Winterthur, Switzerland, June 7th to 9th, 2023. CEUR Workshop Proceedings 3442, CEUR-WS.org 2023 - Preface.
Computer Science Track
- Andreas Nikolaos Athanasopoulos, Amanda Belfrage, David Berg Marklund, Christos Dimitrakakis:
Approximate Inference for the Bayesian Fairness Framework. - Joachim Baumann, Alessandro Castelnovo, Riccardo Crupi, Nicole Inverardi, Daniele Regoli:
An Open-Source Toolkit to Generate Biased Datasets. - Joachim Baumann, Anikó Hannák, Christoph Heitz:
Fair Machine Learning Through Post-processing: The Case of Predictive Parity. - Giorgian Borca-Tasciuc, Xingzhi Guo, Stanley Bak, Steven Skiena:
Provable Fairness for Neural Network Models Using Formal Verification. - Adrian Byrne, Ivan Caffrey, Quan Le:
Towards a Framework for the Global Assessment of Sensitive Attribute Bias Within Binary Classification Algorithms. - Mattia Cerrato, Alesia Vallenas Coronel, Marius Köppel:
The Case for Correctability in Fair Machine Learning. - Alessandro Fabris, Fabio Giachelle, Alberto Piva, Gianmaria Silvello, Gian Antonio Susto:
A Search Engine for Algorithmic Fairness Datasets. - Siamak Ghodsi, Eirini Ntoutsi:
Affinity Clustering Framework for Data Debiasing Using Pairwise Distribution Discrepancy. - Sofie Goethals, David Martens, Toon Calders:
Explainability Methods to Detect and Measure Discrimination in Machine Learning Models. - Sakina Hansen, Joshua R. Loftus:
Model-Agnostic Auditing: A Lost Cause? - Corinna Hertweck, Joachim Baumann, Michele Loi, Christoph Heitz:
FairnessLab: A Consequence-Sensitive Bias Audit and Mitigation Toolkit. - Fanny Jourdan, Ronan Pons, Nicholas Asher, Jean-Michel Loubes, Laurent Risser:
Is a Fairness Metric Score Enough to Assess Discrimination Biases in Machine Learning? - Fanny Jourdan, Titon Tshiongo Kaninku, Nicholas Asher, Jean-Michel Loubes, Laurent Risser:
Breaking Bias: How Optimal Transport Can Help to Tackle Gender Biases in NLP Based Job Recommendation Systems? - Bogdan Kulynych, Hsiang Hsu, Carmela Troncoso, Flávio P. Calmon:
Arbitrary Decisions Are a Hidden Cost of Differentially Private Training. - Joshua R. Loftus:
It's About Time: Counterfactual Fairness and Temporal Depth. - Alex Loosley, Amrollah Seifoddini, Alessandro Canopoli, Meike Zehlike:
Body Measurement Prediction Fairness. - Nicolò Pagan, Joachim Baumann, Ezzat Elokda, Giulia De Pasquale, Saverio Bolognani, Anikó Hannák:
Closing the Loop: Feedback Loops and Biases in Automated Decision-Making. - Evaggelia Pitoura:
Pagerank Fairness in Networks. - Lorenzo Porcaro, Carlos Castillo, Emilia Gómez, João Vinagre:
Fairness and Diversity in Information Access Systems. - Samuel Teuber, Bernhard Beckert:
Formally Verified Algorithmic Fairness Using Information-Flow Tools. - Charles Wan, Leid Zejnilovic, Susana Lavado:
How Differential Robustness Creates Disparate Impact: A European Case Study.
Philosophy Track
- Joachim Baumann, Corinna Hertweck, Michele Loi, Christoph Heitz:
Unification, Extension, and Interpretation of Group Fairness Metrics for ML-Based Decision-Making. - Sander Beckers, Hana Chockler, Joseph Y. Halpern:
A Causal Analysis of Harm. - Lou Therese Brandner, Philipp Mahlow, Anna Wilken, Annika Wölke, Hazar Harmouch, Simon David Hirsbrunner:
How Data Quality Determines AI Fairness: The Case of Automated Interviewing. - Marcello Di Bello, Nicolò Cangiotti, Michele Loi:
Classification Parity, Causal Equal Protection and Algorithmic Fairness. - Matteo Fabbri:
Social Influence for Societal Interest: A Pro-Ethical Framework for Improving Human Decision-Making Through Multi-Stakeholder Recommender Systems. - Andrea Ferrario:
Through the Sands of Time: A Reliabilistic Account of Justified Credence in the Trustworthiness of AI Systems. - Camilla Quaresmini, Eugenia Villa, Valentina Breschi, Viola Schiaffonati, Mara Tanelli:
Qualification and Quantification of Fairness for Sustainable Mobility Policies. - Otto Sahlgren:
Using Fairness Metrics as Decision-Making Procedures: Algorithmic Fairness and the Problem of Action-Guidance. - Teresa Scantamburlo, Giovanni Grandi:
A 'Little Ethics' for Algorithmic Decision-Making. - Vincent J. Straub, Deborah Morgan, Youmna Hashem, John Francis, Saba Esnaashari, Jonathan Bright:
A Multidomain Relational Framework to Guide Institutional AI Research and Adoption. - Bauke Wielinga:
Complex Equality and Algorithmic Fairness: A Social Goods Approach to Make Statistical Fairness Metrics Less Abstract. - Sebastian Zezulka:
Fairness After Intervention: Towards a Theory of Substantial Fairness for Machine Learning.
Social Sciences Track
- Sofia Jaime, Christoph Kern:
Ethnic Classifications in Algorithmic Decision-Making Processes. - Christoph Kern, Ruben L. Bach, Hannah Mautner, Frauke Kreuter:
When Small Decisions Have Big Impact: Fairness Implications of Algorithmic Profiling Schemes. - Oriane Pierrès, Alireza Darvishy, Markus Christen:
Artificial Intelligence in Higher Education: Ethical Concerns for Students With Disabilities. - Jan Simson, Florian Pfisterer, Christoph Kern:
What If? Using Multiverse Analysis to Evaluate the Influence of Model Design Decisions on Algorithmic Fairness. - Laura State, Miriam Fahimi:
Careful Explanations: A Feminist Perspective on XAI. - Chiara Ullstein, Severin Engelmann, Orestis Papakyriakopoulos, Jens Grossklags:
A Reflection on How Cross-Cultural Perspectives on the Ethics of Facial Analysis AI Can Inform EU Policymaking.
Law & Policy Track
- Ahmet Bilal Aytekin:
Algorithmic Bias in the Context of European Union Anti-Discrimination Directives. - Eugenia Cacciatori, Enzo Fenoglio, Emre Kazim:
Living with Opaque Technologies: Insights for AI from Digital Simulations. - Gabriele Carovano, Alexander Meinke:
Improving Fairness and Cybersecurity in the Artificial Intelligence Act. - Matteo Fabbri:
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. - Lukas Hondrich, Hannah Ruschemeier:
Addressing Automation Bias through Verifiability. - Jan-Laurin Müller:
Fairness in Machine Learning as 'Algorithmic Positive Action'. - Carlotta Rigotti, Alexandre R. Puttick, Eduard Fosch-Villaronga, Mascha Kurpicz-Briki:
Mitigating Diversity Biases of AI in the Labor Market. - Nicolas Scharowski, Michaela Benk, Swen J. Kühne, Léane Wettstein, Florian Brühlmann:
Certification Labels for Trustworthy AI. - Laura State, Alejandra Bringas Colmenarejo, Andrea Beretta, Salvatore Ruggieri, Franco Turini, Stephanie Law:
The Explanation Dialogues: Understanding How Legal Experts Reason About XAI Methods. - Hilde J. P. Weerts, Raphaële Xenidis, Fabien Tarissan, Henrik Palmer Olsen, Mykola Pechenizkiy:
Algorithmic Unfairness Through the Lens of EU Non-Discrimination Law. - Malwina Anna Wojcik:
Assessing the Legality of Using the Category of Race and Ethnicity in Clinical Algorithms - the EU Anti-Discrimination Law Perspective. - Yasaman Yousefi, Lisa Koutsoviti Koumeri, Magali Legast, Christoph Schommer, Koen Vanhoof, Axel Legay:
Compatibility of Fairness Metrics With EU Non-Discrimination Law: A Legal and Technical Case Study.
a service of 
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
last updated on 2024-04-26 18:37 CEST by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by:
