This article analyzes modern regulatory methods aimed at mitigating bias and discrimination within artificial intelligence (AI) systems. The study demonstrates how traditional antidiscrimination frameworks (such as the EPC and Title VII) begin to fray when confronted with the "Black Box Problem" and the fragmented liability chains inherent in the AI era. By examining a typology of four primary AI regulatory approaches-prohibition, process, input-based, and output-based regulations-the author evaluates the viability of output-oriented regimes under the U.S. Supreme Court's landmark decisions in Ricci v. DeStefano and SFFA. Ultimately, the paper advocates for a revision of human decisionmaking standards and the codification of algorithmic accountability to secure equal protection and the "right to a human decision" in a digital society.

AI law, antidiscrimination doctrine, disparate impact, Black Box problem, output-based regulation, algorithmic auditing, Ricci v. DeStefano, SFFA, proxy discrimination, human-in-the-loop.

United States v. Meta Platforms, Inc., No. 1:22-cv-05187 (S.D.N.Y. filed June 21, 2022).

Ricci v. DeStefano, 557 U.S. 557 (2009).

Students for Fair Admissions, Inc. v. President & Fellows of Harvard College (SFFA), 600 U.S. 181 (2023).

Civil Rights Act of 1964, Title VII, 42 U.S.C. § 2000e et seq.

Fair Housing Act, 42 U.S.C. §§ 3601–3619.

The Fourteenth Amendment to the U.S. Constitution — Equal Protection Clause (EPC).

European Union Artificial Intelligence Act (EU AI Act), Regulation (EU) 2024/1689 of the European Parliament and of the Council.

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