DATDAT-003 — Data Bias and Fairness Oversights
Chatbot Discriminatory Language Causes User Harm and Reputational Damage
4/5Sector: OtherGeography: GlobalStage: OperateIngested: —
Executive Summary
Public-facing chatbots generating discriminatory and exclusionary language cause measurable mental health harm to users and expose third parties to abuse. Deploying organisations face credibility loss and reputational liability without robust content governance controls.
Domain
Data Management
Blindspots in data quality, privacy, bias, lineage, lifecycle, and third-party data dependencies.
Source
MIT AI Risk Repository — Emerging Risks and Mitigations for Public Chatbots: LILAC v1 (Stanley2024) ↗https://airisk.mit.edu/
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