BIAS 2020: Bias and Fairness in AI Ghent, Belgium, September 18, 2020 |
Conference website | https://sites.google.com/view/bias-2020/ |
Submission link | https://easychair.org/conferences/?conf=bias-and-fairness-in-ai-2020 |
Submission deadline | July 2, 2020 |
Description
Artificial Intelligence (AI) techniques based on big data and algorithmic processing are increasingly used to guide decisions in important societal spheres, including hiring decisions, university admissions, loan granting, and crime prediction. However, there are growing concerns with regard to the epistemic and normative quality of AI evaluations and predictions. In particular, there is strong evidence that algorithms may sometimes amplify rather than eliminate existing bias and discrimination, and thereby have negative effects on social cohesion and on democratic institutions.
Despite the increased amount of work in this area in the last few years, we still lack a comprehensive understanding of how pertinent concepts of bias or discrimination should be interpreted in the context of AI and which socio-technical options to combat bias and discrimination are both realistically possible and normatively justified. The main objective of the workshop is a contribution to the understanding of “How can standards of unbiased attitudes and non-discriminatory practices be met in (big) data analysis, AI and algorithm-based decision-making? “
The BIAS 2020 workshop is proudly a part of the FAccT network, to research and engage with fairness, accountability, and transparency scholars across connected disciplines.
Topics of Interest
The workshop solicits contributions including but not limited to the following topics in all areas of AI (supervised/unsupervised learning, information retrieval and recommender systems, HCI, constraint solving, complex systems and networks, etc.) and bridging interdisciplinary studies (law, social sciences):
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Bias and fairness by design:
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Fairness measures
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Counterfactual reasoning
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Metric learning
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Impossibility results
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Multi-objective strategies for fairness, explainability, privacy, class-imbalancing, rare events, etc.
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Federated learning
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Resource allocation
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Personalized interventions
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Debiasing strategies on data, algorithms, procedures
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Human-in-the-loop approaches
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Methods to audit, measure, and evaluate bias and fairness:
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Auditing methods and tools
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Benchmarks and case studies
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Standard and best practices
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Explainability, traceability, data and model lineage
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Visual analytics and HCI for understanding/auditing bias and fairness
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HCI for bias and fairness
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Software engineering approaches
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Submission and Review Process
Papers should be submitted in accordance to the ECMLPKDD formatting instruction. Submission should be limited to 16 pages including references (following again the main conference guidelines). Papers must be written in English and are to be submitted in PDF format online via the Easychair submission interface:
https://easychair.org/conferences/?conf=bias-and-fairness-in-ai-2020
Each submission will be evaluated on the basis of relevance, significance of contribution and quality by at least three members of the program committee. Submitted papers cannot be identical, or substantially similar to versions that are currently under review at another conference, have been previously published, or have been accepted for publication. All accepted papers will be included in the informal workshop proceedings and will be publicly available on the conference web site. A selection of accepted papers will be invited to submit to an open call for papers of a special issue of a journal (to be announced). At least one author of each accepted paper is required to attend the workshop to present. For the accepted papers, we will have regular talks and additional poster presentations to foster further discussions, based on local venue capabilities.
Important Dates
Workshop Paper Submission Deadline (extended): July 02, 2020
Workshop Paper Author Notification: August 06, 2020
Workshop paper camera ready deadline: August 27, 2020
Workshop date: to be confirmed, September 18, 2020
Organizers
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Toon Calders, University of Antwerp, Belgium
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Eirini Ntoutsi, Leibniz University Hannover & L3S Research Center, Germany
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Mykola Pechenizki, Eindhoven University of Technology, Netherlands
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Bodo Rosenhahn, Leibniz University Hannover & L3S Research Center, Germany
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Salvatore Ruggieri, University of Pisa, Italy.
Website
Workshop's website: https://sites.google.com/view/bias-2020/