AI4ALL, Columbia School of Social Work, Columbia Data Science Institute, and Others Collaborate on Research to Foster a Diverse AI Workforce
New research funded by NSF Convergence Accelerator aims to create an AI toolkit for inclusion
AI is shaping the world. The global economic impact of AI is estimated to be nearly $15.7 trillion by 2030. We’re already seeing AI incorporated into a variety of areas like finance, retail, transportation, manufacturing, healthcare, and agriculture, among others. Despite the potential for broad and transformative impact, access to this technology is not evenly distributed. It is critical that we take action now to ensure that everyone has equitable access to shape and drive this world-changing technology.
To create pathways for meaningful inclusion in AI, AI4ALL, with the SAFElab at Columbia University’s School of Social Work, the Data Science Institute at Columbia University, Stanford University’s Pre-Collegiate Studies, Stanford University faculty, and Princeton University faculty, are undertaking a research effort funded by the National Science Foundation. We join a group of 43 research teams selected from a pool of over 500 applications, as the inaugural cohort of the National Science Foundation’s Convergence Accelerator, which awarded $39 million in total across the cohort. This new NSF initiative is designed to support multidisciplinary and team-based efforts that address challenges of national importance.
Our joint research is designed to identify key factors that contribute to underrepresented groups entering and persisting in the field of AI, with the long-term goal of fostering a diverse AI workforce. The students who will particularly benefit from this work are girls, students of color, and low-income students, all of whom have been historically underrepresented in the AI field. The outcome of the research will be an AI toolkit for inclusion, designed for youth-serving organizations, educators, and industry partners. The toolkit will include best practices to teach AI to historically underrepresented groups, models for student recruitment, internship partnerships with companies, and a compilation of measurement tools to assess the efficacy of the programs. The research team includes experts in the field of social work who contribute knowledge of the social context and appropriate measurement tools; AI researchers who are AI content-matter experts; and education outreach practitioners with knowledge and experience developing STEM education programs.
The research will be led by:
- Dr. Augustin Chaintreau (Columbia University)
- Dr. Desmond Patton (Columbia University)
- Dr. Chris Piech (Stanford University)
- Tess Posner (AI4ALL)
- Dr. Olga Russakovsky (AI4ALL, Princeton University)
- Alivia Shorter (Stanford University)
- Tiffany Shumate (AI4ALL)
The full NSF Convergence Accelerator press release is available here.