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<html> <div class=„homepage-hero container“ readability=„38“> <p class=„homepage-hero__subtitle“>Fairness, Accountability, and Transparency in Machine Learning</p> </div><div class=„homepage-description container row col-sm-8 col-sm-offset-2“ readability=„54“> <h3>Bringing together a growing community of researchers and practitioners concerned with fairness, accountability, and transparency in machine learning</h3> <p>The past few years have seen growing recognition that machine learning raises novel challenges for ensuring non-discrimination, due process, and understandability in decision-making. In particular, policymakers, regulators, and advocates have expressed fears about the potentially discriminatory impact of machine learning, with many calling for further technical research into the dangers of inadvertently encoding bias into automated decisions.</p> <p>At the same time, there is increasing alarm that the complexity of machine learning may reduce the justification for consequential decisions to “the algorithm made me do it.”</p> <p>The annual event provides researchers with a venue to explore how to characterize and address these issues with computationally rigorous methods.</p> </div> </html>
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