“AI2’s mission to contribute to humanity through high-impact AI research and engineering can only be achieved through the inclusion of diverse perspectives. We strive to make diversity, equity, and inclusion a cornerstone of our work and culture, and we're proud of our progress to date. As with any complex initiative, we still have work to do—we're committed to listening, learning, and refining our efforts to enrich our work and our team.”
Oren Etzioni, CEO
At AI2, we are committed to fostering a diverse, inclusive environment within our institute, and to encourage these values in the wider research community. A diverse group of employees brings a variety of perspectives that encourage novel ideas and new approaches oriented at the data and challenges present in AI research.
AI2 has a Diversity, Equity, and Inclusion Council that is open to any team member from across the organization. We meet regularly and make active progress across several initiatives to support DEI both internally and externally.
For our team at AI2, we offer:
AI2’s nonprofit status and unique mission of AI for the Common Good allow us to provide our team members the autonomy and support to pursue projects related to diversity and inclusion.
AI2’s recent study Gender Trends in Computer Science Authorship by Lucy Lu Wang, Gabriel Stanovsky, Luca Weihs, and Oren Etzioni highlighted an important diversity gap in the computer science field. If current trends continue, the gender divide among authors publishing computer science research will not close for more than a century. Fair representation of women and other minorities is crucial to the future of the field, and we want to catalyze the conversation and inspire action by making findings like these known and by providing this important data to others interested in helping to foster equity in the field.
Mark Yatskar, a Young Investigator at AI2, has published multiple works concerning gender bias and amplification in machine learning datasets, including Adversarial Removal of Gender from Deep Image Representations by Tianlu Wang, Jieyu Zhao, Mark Yatskar, Kai-Wei Chang, and Vicente Ordonez.
The Semantic Scholar team actively explores meaningful meta analyses of scientific literature, recently quantifying the problematic sex bias present in clinical studies in their study Quantifying Sex Bias in Clinical Studies at Scale With Automated Data Extraction by Sergey Feldman, Waleed Ammar, Kyle Lo, Elly Trepman, Madeleine van Zuylen, and Oren Etzioni.