Partnership on AI (PAI) has launched a set of Data Enrichment Sourcing Guidelines for AI organizations to use to develop best practices with a positive impact on the lives of data enrichment workers, along with an accompanying case study, Resources for AI Practitioners, and blog post. Join us for a fascinating discussion around the importance of these resources, and a real-world exploration of putting these resources into practice by PAI Partner DeepMind.
Data enrichment workers who clean, label, and moderate large sets of data are essential to machine learning (ML). Yet these workers often face poor working conditions and few labor protections, which not only impacts the wellbeing of data enrichment workers, but also affects the quality of the data AI technology is built on.
This panel discussion moderated by Sonam Jindal, Program Lead for AI, Labor and the Economy will unpack why companies should be prioritizing responsible data enrichment practices, what this accomplishes, and what more we need to do, and through a real-world exploration of PAI Partner DeepMind’s process, challenges, and impact of putting the Guidelines into practice.REGISTER
Associate Director, Equity, Inclusion, & Justice
Operational Ethics and Safety Lead
Responsible Development and Innovation Manager
Cloudwork Postdoctoral Researcher Fairwork Foundation
Oxford Internet Institute
Program Lead for AI, Labor and the Economy
Partnership on AI