The Data Discrepancy: Solving America's Digital Inequality Issue
The Center for Data Innovation is hosting a panel discussion on August 30, 2022, from 12:00 PM to 1:15 PM (EDT). The discussion, moderated by Gillian Diebold, Policy Analyst at the Center for Data Innovation, will focus on the importance of addressing the data divide, its impact, and the steps policymakers can take to reduce disparities and promote equitable participation in the data-driven economy.
The panel will feature Denice Ross, U.S. Chief Data Scientist, Christopher Wood, Executive Director of LGBT Tech, Traci Morris, Executive Director of the American Indian Policy Institute at Arizona State University, Dominique Harrison, Director of Racial Equity Design and Data Initiative (REDDI), Citi Ventures Innovation, and Ioana Tanase, Accessibility Program Manager at Microsoft.
Advances in technology have made it cheaper and easier to produce, collect, and use data. However, not everyone has enough high-quality data collected about them or their communities, creating a data divide or data gaps. This divide can create or exacerbate social and economic inequalities, negatively impacting one's ability to participate in the data economy.
The panel will highlight several steps policymakers can take to address data disparities and promote equitable participation. These steps emphasize coordinated policy, structural interventions, regulatory transparency, inclusive data practices, and community engagement.
- Enforce Algorithmic Transparency and Regulatory Oversight: Policymakers can establish regulatory frameworks that require transparency in algorithms used in AI and data systems to prevent biases and discrimination embedded in data processes.
- Shift from Individual to Structural Approaches: Instead of focusing solely on improving individual digital literacy, policies should address broader structural issues such as economic disparities, cultural differences, and systemic biases that influence access and engagement in the data economy.
- Support Research That Links Misinformation, AI, and Social Marginalization: Developing inclusive knowledge infrastructures requires interdisciplinary collaboration across research, education, technology, policy, and design to confront how AI shapes visibility, trust, and access, especially for marginalized communities.
- Expand Access to Data by Demographics: Advocating for public-use datasets disaggregated by race, ethnicity, and other demographic factors can illuminate disparities and inform targeted policy interventions.
- Targeted Outreach and Inclusive Service Design: Policies should promote mobile and accessible services, targeted outreach to uninsured or underserved populations, and neighbourhood-level infrastructure investments to ensure equitable data participation and benefits.
- Invest in Data Collection and Use for Equity: Encouraging the collection of relevant and high-quality data to identify disparities and track progress, combined with applying this data in managed care and policy decisions, supports equity goals.
- Collaborate Across Disciplines and Stakeholders: Effective solutions depend on partnerships among government, academia, communities, and industry to align efforts addressing systemic inequities in the data economy.
The historic infrastructure package is intended to help close the digital divide. Data plays a critical role in identifying and addressing critical social and economic issues at the community level. Data empowers individuals to access data-driven financial, educational, and healthcare services at the individual level. Data-backed AI algorithms can produce new actionable insights.
Join us on August 30, 2022, for this insightful panel discussion on addressing the data divide and promoting equitable participation in the data economy.
- The Center for Data Innovation is hosting a panel discussion on August 30, 2022, addressing the importance of addressing the data divide and promoting equitable participation in the data-driven economy.
- The panel, moderated by Gillian Diebold and featuring industry experts, will highlight the need for enforcing algorithmic transparency and regulatory oversight to prevent biases and discrimination in AI and data systems.
- Policymakers should shift from focusing on individual digital literacy to addressing broader structural issues that influence access and engagement in the data economy.
- Supporting research that links misinformation, AI, and social marginalization is essential to developing inclusive knowledge infrastructures that help address disparities for marginalized communities.
- Advocating for public-use datasets disaggregated by race, ethnicity, and other demographic factors can help illuminate disparities and inform targeted policy interventions.
- Policies should encourage the collection of relevant and high-quality data to identify disparities and track progress to support equity goals in the data economy.
- Effective solutions to systemic inequities in the data economy depend on partnerships among government, academia, communities, and industry to align efforts for equitable participation in the data economy, data economy, and data-driven policy-making.
