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Absolute prohibition on the model serving as the sole or final decision-maker for any individual-consequential determination -- employment, credit, housing, healthcare, education, legal, or public services. AI may assist and analyze; humans must decide an
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Project Name 2026 TWG Evaluation Recommendations Date Proposal Submitted January 6, 2026 Date of Requested Decision March 6, 2026 Completed By Jenise Bauman Date of Decision 1 March 6, 2026 1 Decision will become final if committee members who were not present at this meeting do not oppose this proposed decision within 7 days. FTC Decision and Justification The TWG subcommittee seeks approval from the FTC in implementi
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Template and framework for creating an organizational AI Use Policy -- the foundational governance document defining permitted uses, prohibited uses, user rights, liability limitations, and the complaint process. This is the policy document the AI Output
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Provides the professional judgment, communication framework, and domain knowledge of a CTO, CIO, and IT leadership team. Load when generating technology evaluations, architecture decision records, incident reports, change management communications, IT str
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Three-tier framework (Permitted / Restricted / Prohibited) for classifying AI task requests before execution. Gates restricted tasks pending human approval. Declines prohibited tasks with clear explanation. Operationalizes the Organizational AI Use Policy
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Defines the communication style for content directed at C-suite executives and board members. Brevity, precision, and decision-orientation are the governing principles. Bottom-line-up-front structure. No preamble. Action-oriented conclusion.
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Complete mental model of the Centralpoint platform: nine functional layers (Data, Import, Enrichment, Automation, Presentation, Interaction, Integration, AI, Governance), full feature dependency map showing what each feature depends on and what it feeds into, and a key decision guide for choosing between similar approaches (Standard vs EXT, Web API vs CP API Services, Data Governance vs Data Triggers, etc.).
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Defines email formatting, structure, and tone standards for all AI-generated email content including broadcast emails, triggered notifications, workflow emails, and one-to-one communications. Covers subject line writing, body structure, call-to-action for
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Five-phase AI incident response protocol: Contain, Preserve Evidence, Assess Harm, Remediate, Document and Review. Covers harm categories, severity tiers, notification requirements, evidence preservation, and root cause investigation. Required when AI pro
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Ensures the model never denies being an AI, discloses AI nature at first contact in chat contexts, never impersonates named human individuals without disclosure, and flags AI involvement when users are making consequential decisions.
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Governs when the AI should ask clarifying questions before generating output, what to ask, and how to ask efficiently. Defines trigger conditions (ambiguous feature, undefined scope, missing data source, access control unspecified, technical conflict, existing code not provided), what questions to ask per topic, and how to frame questions with built-in assumptions.
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Overview of Data Lineage and Its Importance Introduction Data lineage refers to the tracking and visualization of data as it flows from its origin to its final destination within an organization. This process involves documenting the data's journey, transformations, and any processes it undergoes. Data lineage provides transparency and clarity, helping organizations understand the data's lifecycle, its various transformations,
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