Organized by MozFest
MozFest is a unique hybrid: part art, tech and society convening, part maker festival, and the premiere gathering for activists in diverse global movements fighting for a more humane digital world.
Over the last thriteen years, MozFest has fueled the movement to ensure the internet benefits humanity, rather than harms it. As the festival matures, we remain focused on our work to build a healthier internet and more Trustworthy AI.
Our previous research at Partnership on AI has highlighted how AI developers face a range of challenges when it comes to identifying and mitigating algorithmic bias. The demographic data required to conduct fairness assessments are not always readily available, prompting the need for additional data collection. However, the collection of demographic data, even in the pursuit of algorithmic fairness, carries a number of risks. These include misrepresentation, privacy, surveillance, and reinforcing oppressive categories.
Our previous work demonstrates just how pivotal practices of categorization and datafication are to organizational efforts to make algorithmic decision-making more “fair,” and highlights how participatory, inclusive practices around data collection and use are necessary to achieve “fairness” or “non-discrimination” (not only as an outcome, but as a process). One path forward towards realizing more participatory and inclusive practices of data collection can be found in the concept of data justice. The concept of data justice is defined by Linnet Taylor as “‘fairness in the way people are made visible, represented and treated as a result of their production of digital data.”
The Global Partnership on AI (GPAI)and Alan Turing Institute built upon this definition to develop a framework of six principles (power, equity, access, identity, participation, and knowledge) for applying data justice to the AI lifecycle. This session will include a keynote presentation from GPAI and Turing on their seminal data justice framework. PAI will then guide participants through an interactive activity to explore how to apply this framework to demographic data collection for the purpose of giving voice and power to those affected by algorithmic systems. Insights from this workshop will be used to inform a learning guide for developers, as part of PAI’s Resource Library.
Program and Research Associate for Fairness, Transparency, and Accountability & ABOUT ML
Lead of Fairness, Transparency, and Accountability & ABOUT ML