This working paper examines how automated decision systems reproduce and obscure the forms of discrimination that civil-rights law was built to prevent, and proposes both a vocabulary and an accountability framework for addressing them. Grounded in the disparate-impact doctrine established in Griggs v. Duke Power Co. (1971) and codified across Title VII, the Fair Housing Act, and the Equal Credit Opportunity Act, the paper analyzes three legally significant cases — Mobley v. Workday (employment screening), Louis v. SafeRent Solutions (tenant scoring), and United States v. Meta Platforms (advertising delivery) — each of which extends liability to the operators of algorithmic systems. It distinguishes these genuine algorithmic-discrimination actions from adjacent enforcement matters, such as the 2024 CFPB order against Apple and Goldman Sachs, that are frequently miscategorized as discrimination cases. The paper introduces two analytic contributions: the Communicado / Incommunicado distinction, which names the boundary between what a system discloses to a subject and what it withholds without the subject's knowledge; and the Heritage Schema, a proposal to replace color-based racial classification with nationality-based self-identification in administrative data. It concludes with a tiered accountability framework combining mandatory bias auditing with a graduated, revenue-indexed civil-penalty structure. The aim is neither to indict automation as such nor to defend it, but to specify the conditions under which automated decisions can be made legible, contestable, and accountable.

NOTES: This working paper is part of the Masterisk_Asterisk :: The Digital Reckoning project, an open algorithmic-accountability platform published by KR0M3D1A CORP. The paper presents, in academic form, the legal and conceptual framework underlying that platform. Contents: (1) Introduction; (2) The Legal Architecture of Disparate Impact; (3) Proxy Discrimination and the Mechanics of Algorithmic Exclusion; (4) Case Studies in Algorithmic-Discrimination Litigation; (5) The Communicado / Incommunicado Distinction; (6) The Heritage Schema; (7) Toward an Accountability Framework; (8) Conclusion; References. Methodology and verification: All case citations and factual claims were checked against primary and authoritative secondary sources prior to deposit. The paper deliberately distinguishes verified algorithmic-discrimination actions from adjacent enforcement matters that are commonly miscategorized. Disclosures: The author is the founder of KR0M3D1A CORP, which develops the algorithmic-accountability tools referenced in Section 7. No external funding supported this work. Drafting and formatting were assisted by an AI writing tool; all citations and analysis were independently verified, and the author is solely responsible for the content. Companion materials: Live platform — masterisk-asterisk-eight.vercel.app · Source repository — github.com/Pant1f3r/masterisk-asterisk