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<h1 class="m-b-20"><strong>AEQUITAS 2023 </strong></h1>
<p class="m-b-40"><strong>First AEQUITAS Workshop on Fairness and Bias in AI | co-located with ECAI 2023</strong></p>
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<h1 class="m-b-20"><strong>AEQUITAS 2023 </strong></h1>
<p class="m-b-40"><strong>First AEQUITAS Workshop on Fairness and Bias in AI | co-located with ECAI 2023</strong></p>
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<h1 class="m-b-20"><strong>AEQUITAS 2023 </strong></h1>
<p class="m-b-40">First AEQUITAS Workshop on Fairness and Bias in AI | co-located with ECAI 2023</p></div></div>
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<h1>Workshop: Aims and Scope</h1>
<p style="text-align:justify; color:black;">
AI-based decision support systems are increasingly deployed in industry, in the public and private sectors, and in policymaking to guide decisions in important societal spheres, including hiring decisions, university admissions, loan granting, medical diagnosis, and crime prediction.
As our society is facing a dramatic increase in inequalities and intersectional discrimination, we need to prevent AI systems to amplify this phenomenon but rather mitigate it. As we use automated decision support systems to formalize, scale, and accelerate processes, we have the opportunity, as well as the duty, to revisit the existing processes for the better, avoiding perpetuating existing patterns of injustice, by detecting, diagnosing and repairing them. To trust these systems, domain experts and stakeholders need to trust the decisions.
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.
</p>
<div class=WordSection1>
<br><br>
<p class=Normale1 style='text-align:justify;color:black;'><span lang=EN-US style='mso-bidi-font-size:
10.0pt;mso-ansi-language:EN-US'>
This workshop provides a forum for the exchange of ideas, presentation of results and preliminary work in all areas related to
fairness and bias in AI; including, but not limited to:
<o:p></o:p></span></p>
<p class=Normale1 style='text-align:justify;color:black;'><b>Bias and Fairness by Design
</b></p>
<p class=Normale1 style='text-align:justify;color:black;'><b>Fairness measures and metrics
</b></p>
<p class=Normale1 style='text-align:justify;color:black;'><b>Counterfactual reasoning
</b></p>
<p class=Normale1 style='text-align:justify;color:black;'><b>Metric learning
</b></p>
<p class=Normale1 style='text-align:justify;color:black;'><b>Impossibility results
</b></p>
<p class=Normale1 style='text-align:justify;color:black;'><b>Multi-objective strategies for fairness, explainability, privacy, class-imbalancing, rare events, etc.
</b></p>
<p class=Normale1 style='text-align:justify;color:black;'><b>Federated learning
</b></p>
<p class=Normale1 style='text-align:justify;color:black;'><b>Resource allocation
</b></p>
<p class=Normale1 style='text-align:justify;color:black;'><b>Personalized interventions
</b></p>
<p class=Normale1 style='text-align:justify;color:black;'><b>Debiasing strategies on data, algorithms, procedures
</b></p>
<p class=Normale1 style='text-align:justify;color:black;'><b>Human-in-the-loop approaches
</b></p>
<p class=Normale1 style='text-align:justify;color:black;'><b>Methods to Audit, Measure, and Evaluate Bias and Fairness
</b></p>
<p class=Normale1 style='text-align:justify;color:black;'><b>Auditing methods and tools
</b></p>
<p class=Normale1 style='text-align:justify;color:black;'><b>Benchmarks and case studies
</b></p>
<p class=Normale1 style='text-align:justify;color:black;'><b>Standard and best practices
</b></p>
<p class=Normale1 style='text-align:justify;color:black;'><b>Explainability, traceability, data and model lineage
</b></p>
<p class=Normale1 style='text-align:justify;color:black;'><b>Visual analytics and HCI for understanding/auditing bias and fairness
</b></p>
<p class=Normale1 style='text-align:justify;color:black;'><b>HCI for bias and fairness
</b></p>
<p class=Normale1 style='text-align:justify;color:black;'><b>Software engineering approaches
</b></p>
<p class=Normale1 style='text-align:justify;color:black;'><b>Legal perspectives on fairness and bias
</b></p>
<p class=Normale1 style='text-align:justify;color:black;'><b>Social and critical perspectives on fairness and bias
</b></p>
<p class=MsoNormal><span lang=EN-US style='mso-ansi-language:EN-US'><o:p> </o:p></span></p>
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<a href="https://ecai2023.eu"><h3>co-located with ECAI 2023</h3></a>
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<li>26th European Conference on Artificial Intelligence
</li> <li><br> 30.09 - 5.10, 2023, Kraków, Poland </li>
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