The Federal Trade Commission opened public comment on a proposed policy statement addressing AI accuracy, especially cases where companies may manipulate AI system behavior contrary to users’ reasonable expectations of objectivity, accuracy, or neutrality. The FTC frames such conduct as potentially unfair or deceptive under the FTC Act and is accepting comments through July 31, 2026.
The Federal Trade Commission opened public comment on a proposed policy statement addressing AI accuracy, especially cases where companies may manipulate AI system behavior contrary to users’ reasonable expectations of objectivity, accuracy, or neutrality.
This is worth tracking because it moves AI harm from abstract “misinformation” into enforceable consumer-protection language around manipulated outputs and undisclosed steering.
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Federal Trade Commission
The European Commission has issued operational guidance for Article 50 of the AI Act before its transparency obligations begin applying on August 2, 2026. Providers must disclose when people are interacting with systems such as chatbots, agents, or avatars and make generative outputs detectable through machine-readable marking; deployers must disclose emotion-recognition or biometric-categorization uses and clearly label deepfakes and certain AI-generated public-interest text when it lacks human review or editorial control.
The NUS/Harbin-led team identifies a domain-specific form of generative-AI hallucination: molecular candidates can receive strong predicted binding scores while violating basic chemistry or producing physically impossible atomic arrangements. Its DrugRPG framework incorporates chemical-foundation-model priors and differentiable physical constraints during molecule generation, reducing severe steric clashes by 65.4% relative to the reported state-of-the-art baseline and increasing by 28.6% the share of generated candidates meeting combined potency, stability, and synthetic-feasibility criteria.
The authors warn that AI systems can amplify stale annotations, incorrect database relationships, inconsistent standards, and weak provenance when they continuously harvest scientific repositories that were designed as comparatively static resources. They extend the FAIR principles with COPE—Comparable, Organized, Predictive, and Engaged—calling for iterative updates, version tracking, uncertainty estimates, machine-actionable standards, and community validation whenever AI-supported analyses generate new knowledge.