The Intersection of AI and Racial Justice: Tackling Inequality and Automation

The Intersection of AI and Racial Justice: Tackling Inequality and Automation

Table of Contents

1. Introduction
2. The Intersection of Algorithmic Tools and Racial Justice
   • 2.1 Implications for Racial Justice
   • 2.2 Algorithmic Tools in Legal Systems
3. AI Now and the Criminal Justice System
   • 3.1 Role of AI in the Criminal Justice System
   • 3.2 Impact of AI on Austerity and Inequality
4. Interview with Jin Eubanks
   • 4.1 Professor Jin Eubanks: Background and Expertise
   • 4.2 Automating Inequality: A Breakthrough Book
5. Interview with Phillip Austin
   • 5.1 Professor Phillip Austin: Background and Expertise
   • 5.2 AI and Extreme Poverty: United Nations Report
6. Importance of the Human Rights Dimension
   • 6.1 Macro Focus vs. Human Rights Focus
   • 6.2 Linking Inequality, Human Rights, and AI
7. Automated Decision Systems in Public Services
   • 7.1 Integration of Automated Tools in Public Services
   • 7.2 Effects of Automated Systems on Individuals
8. The Problem with Triage and Rationing
   • 8.1 Political Choices and Allocating Resources
   • 8.2 Integrated Tools and Diverting Resources
9. Automating Inequality: Case Studies
   • 9.1 Automating Welfare Eligibility in Indiana
   • 9.2 Coordinated Entry System for the Unhoused in Los Angeles
   • 9.3 Predictive Modeling in Child Welfare Services in Allegheny County
10. Political Decisions and Automation
    • 10.1 Impact of Policy and Government Decisions
    • 10.2 Tax Cuts, Austerity, and Social Services
11. The Role of AI in Combating Bias
    • 11.1 Bias-Fighting Tools in Public Services
    • 11.2 Bias in Automated Decision-Making
12. The Racial Dimension of Welfare and Automation
    • 12.1 Relationship between Race and Attitudes to Welfare
    • 12.2 Racial Disproportionality and Automated Systems
13. Building Equitable Systems with AI
    • 13.1 Moving Beyond Efficiency and Optimization
    • 13.2 Incorporating Equity Gears into Automated Systems
14. The Role of AI and Technology for Good
    • 14.1 Using Technology to Combat Homelessness
    • 14.2 Addressing the Underlying Political Problem
15. The Global Landscape of Automation and Inequality
    • 15.1 Global Similarities in Welfare Policies and AI
    • 15.2 Promoting Accountability and Voices of Impacted Communities
16. Conclusion

👉 The Intersection of Algorithmic Tools and Racial Justice

In today's rapidly advancing technological era, the use of algorithmic tools in various systems governing our lives has raised concerns about racial justice and inequality. Vincent Sutherland, the executive director of the Center on Race, Inequality, and the Law at NYU School of Law, leads a conversation with panelists Jin Eubanks and Phillip Austin, who are prominent figures in AI and automated systems. This article delves into their insightful discussions surrounding the implications of algorithmic tools for racial justice and inequality.

Interview with Jin Eubanks 🎙️

4.1 Professor Jin Eubanks: Background and Expertise

Jin Eubanks, a political science professor at SUNY Albany and author of the groundbreaking book "Automating Inequality," joins the conversation. Her research focuses on the integration of new automated decision systems in public service programs in the United States. With a profound understanding of the potential and pitfalls of these systems, Eubanks sheds light on the deep social programming underlying their design and their impact on marginalized communities.

4.2 Automating Inequality: A Breakthrough Book

Eubanks describes her extensive research, spanning eight years, which culminated in "Automating Inequality." This book examines three significant case studies that illustrate the integration of automated decision systems in welfare, homelessness services, and child welfare programs. Eubanks unveils how these tools, instead of easing the burden of accessing public assistance, divert individuals from the resources they need, ultimately perpetuating inequality and discrimination.

👉 Importance of the Human Rights Dimension

Phillip Austin, a law professor at NYU and UN Human Rights Council Special Rapporteur on extreme poverty and human rights, emphasizes the vital role of a human rights approach in addressing inequality and discrimination. Austin's reports on extreme poverty in the United States and his recognition of the intersection between human rights and AI bring a unique perspective to the conversation.

The discussion delves into the distinction between a macro focus on inequality and a human rights focus, which emphasizes the rights of individuals facing discrimination and neglect. The challenge lies in bridging the gap between the AI community, often focused on ethics, and the human rights community, hesitant to engage with AI. Austin's report on the United States highlights the need to link issues of inequality, human rights, and the uses of AI, fostering a more comprehensive and accountable approach.

👉 Automated Decision Systems in Public Services

Automated decision systems have become an integral part of public service programs in the United States. Their implementation aims to streamline processes, lower barriers to access, and optimize efficiency. However, whereas efficiency is prioritized, the potential harm these systems can cause to marginalized communities is often overlooked.

Eubanks recounts her case studies, revealing the unintended consequences of automated systems in welfare eligibility, homeless services, and child welfare. Instead of enhancing access to social services, these systems end up diverting individuals from their entitled resources, criminalizing them instead of providing the support they need. Moreover, the data collected by these systems is used to create predictive models, reinforcing the denial of resources to those deemed "risky" in the future.

👉 Building Equitable Systems with AI

The conversation highlights the need to move beyond a narrative that focuses solely on efficiency and optimization when designing AI systems. Eubanks suggests incorporating equity gears into these systems from the beginning, ensuring they increase the self-determination and dignity of the individuals they target.

This approach involves challenging the assumption of scarcity and embracing the idea that there is enough for everyone. It requires shifting the conversation from triage and rationing to universal floors of social protection. By building technology that addresses underlying political problems and considers the perspectives of those impacted, AI can be a tool for combating inequality and promoting social justice.

👉 The Global Landscape of Automation and Inequality

The concerns surrounding the intersection of automation and inequality are not unique to the United States. Internationally, countries like the United Kingdom and Australia face similar challenges. Austerity measures, stigmatization of welfare recipients, and the privatization of social protection programs are prevalent trends.

By recognizing these global similarities, it becomes necessary to promote accountability and foster the voices of impacted communities. Designers, engineers, and policymakers must engage with those directly affected by these systems to create Meaningful change. International collaboration, sharing best practices, and challenging narratives that reinforce inequality will contribute to a more equitable approach globally.

👉 Conclusion

The conversation between Vincent Sutherland, Jin Eubanks, and Phillip Austin sheds light on the profound implications of algorithmic tools for racial justice and inequality. Automation and AI offer both promise and peril, depending on how these systems are designed and implemented. By incorporating equity, human rights, and a focus on the voices of marginalized communities, technology can serve as a tool for combating inequality and creating a more just society.

Resource:

• Automating Inequality by Virginia Eubanks