Beyond Fairness in Machine Learning

The machine learning community is seeing an increased focus on fairness-oriented methods of model and dataset development. However, much of this work is constrained by a purely technical understanding of fairness -- an understanding that has come to mean parity of model performance across sociodemographic groups -- that offers a narrow way of understanding how machine learning technologies intersect with systems of oppression that structure their development and use in the real world. In contrast to this approach, we believe it is essential to approach machine learning technologies from a sociotechnical lens, examining how marginalized communities are excluded from their development and impacted by their deployment. Our tutorial will center the perspectives and stories of communities who have been harmed by machine learning technologies and the dominant logics operative within this field. We believe it is important to host these conversations from within the NeurIPS venue so that researchers and practitioners within the machine learning field can engage with these perspectives and understand the lived realities of marginalized communities impacted by the outputs of the field. In doing so, we hope to shift the focus away from singular technical understandings of fairness and towards justice, equity, and accountability. We believe this is a critical moment for machine learning practitioners and for the field as a whole to come together and reimagine what this field might look like. We have great faith in the machine learning community and hope that our tutorial will foster the difficult conversations and meaningful reflection upon the state of the field that is essential to begin constructing a different mode of operating. Our tutorial will highlight research on uncovering and mitigating issues of unfair bias and historical discrimination that machine learning systems learn to mimic and propagate. We will also highlight the lived realities of marginalized communities impacted by machine learning technologies. We will provide tutorial participants with tools and frameworks to incorporate into their own research practice that will facilitate socially aware work and help mitigate harmful impacts of their research.