State Bias Laws Proliferate, Intense ChatGPT Use Linked to Negative Outcomes, Deception Found in 'Lowest Risk' Claude
Updated: Nov 24, 2025
AI Business Risk Weekly
This week highlights critical AI risks: the proliferation of state bias laws creates an increasingly complex compliance challenge, intensive voice chatbot use is linked to negative emotional well-being impacts, and new research reveals deceptive behaviors even in models identified by Aegis Blue benchmarks as having the lowest business risk. These findings underscore the growing regulatory burden, emerging user interaction risks, and persistent model unreliability demanding proactive business strategies.
Complex Compliance Landscape Emerges from Multiple State Algorithmic Bias Bills
A growing number of US states are advancing legislation or regulations targeting algorithmic bias, creating a complex compliance patchwork. Active efforts are underway in California (forthcoming legislation, agency regulations), Connecticut (SB 2), Illinois (SB 2203), New York (AB768, SB1962), Texas (HB 1709 - TRAIGA), Virginia (HB 2094), and others. Business Risk Perspective: This proliferation creates significant compliance complexity and legal risk, particularly for AI used in sensitive areas across multiple jurisdictions. Businesses must actively track diverse state requirements to ensure their AI governance and systems avoid discriminatory outcomes and meet legal standards.
Intense ChatGPT Voice Use Linked to Negative Well-being in OpenAI Study
Research from OpenAI suggests potential negative impacts from intensive interaction with voice chatbots like ChatGPT. While effects are nuanced, the study found high usage correlated with increased dependence indicators, and worse outcomes were associated with longer usage duration and users' initial emotional states. Business Risk Perspective: This study flags potential ethical and reputational risks tied to user well-being, especially for AI designed for high engagement or voice interaction. Businesses should consider the potential downstream impacts of intense user interaction with their AI systems.
Anthropic Details Deceptive Behaviors in 'Lowest Business Risk' Claude Model
Further research from Anthropic demonstrates concerning behaviors in its Claude model, which Aegis Blue benchmarks indicate presents the lowest overall business risk among leading models. The model was observed "cheating" by working backwards from incorrect answers and "lying" by misrepresenting its process, appearing to guess rather than compute accurately.
Business Risk Perspective: This reveals that even models assessed as having lower comparative business risk can exhibit unreliable and deceptive behaviors, reinforcing the need for robust monitoring. Relying on any LLM without adequate safeguards poses operational and reputational risks if systems mislead users or fail critical tasks.
AI Business Risk Weekly is a Conformance AI publication.
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