AI Risk Laundering
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An AI Risk Laundering is a deceptive vendor risk laundering that conceals or minimizes AI system risks when AI vendor organizations present AI-powered products to AI system buyers.
- AKA: AI Risk Obfuscation, AI Hazard Concealment, AI Risk Masking.
- Context:
- It can typically employ AI Capability Misrepresentation by claiming proprietary breakthroughs while using standard AI foundation models.
- It can typically utilize AI Risk Minimization Language in contracts that downplay AI system failure modes and AI operational risks.
- It can typically manifest through AI Technical Opacity where vendors refuse to disclose AI model sources or AI system architectures.
- It can typically involve AI Performance Cherry-Picking highlighting successful AI system outcomes while hiding AI system limitations.
- It can typically create AI Liability Shift transferring unspecified AI system risks from vendors to buyers.
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- It can often exploit AI Knowledge Asymmetry between technical AI vendor teams and non-technical AI system purchasers.
- It can often use AI Complexity Shields claiming technical details are too complex for AI risk disclosure.
- It can often leverage AI Hype Cycle momentum to discourage detailed AI risk investigation.
- It can often employ AI Competitive Secrecy claims to avoid AI system transparency requirements.
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- It can range from being a Passive AI Risk Laundering to being an Active AI Risk Laundering, depending on its AI risk concealment intentionality.
- It can range from being a Subtle AI Risk Laundering to being an Egregious AI Risk Laundering, depending on its AI risk obfuscation degree.
- It can range from being a Technical AI Risk Laundering to being a Commercial AI Risk Laundering, depending on its AI risk hiding method.
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- It can be detected through AI Vendor Transparency Audits examining AI system documentation.
- It can be prevented by AI Procurement Due Diligence requiring specific AI risk disclosures.
- It can be regulated through AI Transparency Mandates enforcing AI vendor accountability.
- It can be exposed via AI Whistleblower Reports revealing hidden AI system vulnerabilitys.
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- Example(s):
- AI Risk Laundering Tactics, such as:
- AI Model Source Concealments, such as:
- Foundation Model Masquerading claiming custom AI while using AI foundation model APIs.
- AI Architecture Obfuscation hiding simple AI wrapper implementations as complex systems.
- AI Capability Inflation exaggerating basic AI features as advanced capabilities.
- AI Risk Documentation Evasions, such as:
- AI Limitation Burial hiding critical AI system constraints in technical appendices.
- AI Risk Disclaimer Sprawl dispersing AI liability clauses across multiple documents.
- AI Failure Rate Concealment omitting AI error metrics from performance reports.
- AI Model Source Concealments, such as:
- AI Risk Laundering Cases, such as:
- Healthcare AI Vendor Scandal (2023) concealing AI diagnostic error rates from hospitals.
- Financial AI Platform Incident (2024) hiding AI model biases from banking clients.
- Legal AI Tool Controversy (2023) masking AI hallucination frequencys from law firms.
- ...
- AI Risk Laundering Tactics, such as:
- Counter-Example(s):
- AI Risk Transparency, which proactively discloses all known AI system risks and limitations.
- AI Risk Sharing Agreement, which explicitly allocates AI risk responsibility between parties.
- AI Open Documentation, which provides complete visibility into AI system components and risks.
- AI Risk Education, which helps buyers understand rather than conceals AI system vulnerabilitys.
- See: Risk Laundering, AI Vendor Practice, AI System Risk, AI Transparency, Deceptive Business Practice.