Summary:
As government agencies, housing providers, healthcare systems, and
employers increasingly rely on automated decision-making, errors can
have life-altering consequences for the people affected. This
presentation examines real-world housing, disability, and civil rights
cases where automated processes, data inaccuracies, and administrative
failures resulted in significant harm.
Participants will learn how to identify automation-driven failures,
evaluate the evidence behind institutional decisions, document
procedural breakdowns, and develop effective advocacy strategies. The
session will also introduce emerging concepts in AI accountability,
validation, and transparency that can help legal professionals better
assess decisions made by automated systems.
Featuring Teresa Villa, Founder of JusticeTree.ai.
Learning Objectives:
• Identify common risks associated with automated decision-making systems.
• Recognize how data errors can cascade across multiple agencies and
institutions.
• Analyze housing, disability, and civil rights cases involving
administrative automation.
• Apply evidence-based methods for investigating and challenging
questionable decisions.
• Understand emerging approaches to AI accountability and validation.