Stanford University Institute for Human Centered AI, USA
Elizabeth M. Adams is a Responsible AI leader recognized as one of Forbes "15 AI Ethics Leaders Showing The World The Way Of The Future." For over two decades, Elizabeth has studied the science of business and technology influences on society while leading large-scale technology initiatives for Fortune 500 companies and various government organizations. As a scholar-practitioner, Elizabeth has developed her expertise working with technical and non-technical leaders, creating alliances with community that translate theory into results. Elizabeth is pursuing an executive doctoral degree at Pepperdine University with a research focus on Leadership of Responsible AI™. In 2021, Elizabeth was awarded Affiliate Fellow status at Stanford University’s Institute for Human-Centered AI, a two-year appointment. She is also the CEO of EMA Advisory Services.
INVITED TALK - Leadership of Responsible AI: Representation Matters
People of color are adversely affected by artificial intelligence (AI) bias. The effects of AI bias have been noted in facial recognition technology, mortgage lending, and algorithms used to determine healthcare treatments. People impacted by AI bias are rarely represented in the development of AI technology (Atker et al., 2021). To prevent AI bias, including diverse perspectives in the creation of Responsible AI (RAI), artifacts that shape policies, procedures, and governance models, could address potential problems in the development of AI.
RAI is an emerging business discipline that examines legal, ethical, and moral standpoints of technology development to help reduce AI bias (Barredo et al., 2020, Taylor et al., 2018). By tying impacted people to innovation and incorporating their ideas as stakeholders, innovations gain substantive and symbolic support from those who are most affected by innovation (Boon et al., 2021). My motivation is explore broader employee stakeholder participation in IS, AI and Organizational Learning. Therefore, I seek to answer the following research question: "How does the participation of African American employee stakeholders in the creation of Responsible AI "shaping artifacts" reduce bias in AI?"