An Introduction to Artificial Intelligence and Solutions to the Problems of Algorithmic Discrimination

Algorithmic bias is a documented problem with real legal consequences: AI and ML systems demonstrably generate discrimination against minorities, women, and other protected classes in high-stakes decisions like credit, housing, and employment—areas already regulated by federal and state anti-discrimination laws.

Current laws apply to algorithms, but enforcement requires technical expertise: Existing protections like the Equal Credit Opportunity Act cover algorithmic decisions, but regulators and companies need deep understanding of how these systems work to detect violations of both "disparate treatment" (overt discrimination) and "disparate impact" (neutral policies that harm protected groups).

Bias can be evaluated and reduced through systematic methodology: The paper proposes that algorithmic fairness risks are not inevitable—they can be measured and mitigated through proper evaluation frameworks, though this requires moving beyond treating algorithms as "black boxes."

Transparency requirements matter for accountability: Laws requiring companies to explain why consumers are denied loans or services apply to AI decisions too; this transparency requirement is essential for detecting and addressing discrimination.

Context and domain knowledge are critical: Effective bias mitigation isn't just a technical fix—it requires understanding the specific business context, data sources, and real-world impacts of algorithms in each application, meaning solutions must be tailored rather than one-size-fits-all.