This article includes a legal and regulatory perspective on AI behavior and technology, covering U.S. and international frameworks, legal risks, compliance requirements, and the evolving landscape of AI law.
What Is “AI Behavior” in Legal Terms?
In legal contexts, “AI behavior” refers to the outputs or actions of an AI system (e.g., decisions, recommendations, predictions, content generation) and the implications of those actions for:
- Human rights
- Liability
- Privacy
- Discrimination
- Autonomy
- Accountability
- Key Legal Issues Around AI Technology
a. Data Privacy and Protection
AI systems process massive amounts of personal data. Legal issues arise around:
- Lawful basis for data collection
- Informed consent
- Cross-border data transfers
- Profiling and automated decision-making
Relevant Laws:
- U.S.: CCPA/CPRA (California), HIPAA, FTC Act
- EU: GDPR (Article 22 prohibits fully automated decision-making without safeguards)
b. Bias and Discrimination
AI algorithms may replicate or amplify societal biases, leading to discriminatory outputs in:
- Hiring tools
- Credit scoring
- Law enforcement (e.g., facial recognition)
- Housing eligibility
Regulatory Oversight:
- EEOC, CFPB, FTC, and DOJ in the U.S. can pursue cases under anti-discrimination or consumer protection laws.
c. Transparency and Explainability
Many AI models are “black boxes” — legally problematic in regulated industries like:
- Finance (e.g., denial of credit)
- Healthcare (e.g., diagnosis decisions)
- Criminal justice (e.g., predictive policing)
Regulators increasingly demand algorithmic explainability and auditability.
d. Intellectual Property (IP)
Legal questions include:
- Who owns AI-generated content?
- Can AI be listed as an “inventor”?
- How are training datasets protected?
U.S. and global patent offices currently require human inventorship. The courts have consistently denied IP rights to AI as a creator. See Thaler v. Perlmutter wherein the United States Court of Appeals stated: “We affirm the denial of Dr. Thaler’s copyright application. The Creativity Machine cannot be the recognized author of a copyrighted work because the Copyright Act of 1976 requires all eligible work to be authored in the first instance by a human being.”
e. Liability and Accountability
If AI causes harm (e.g., defamation, injury, wrongful denial of services), legal systems ask:
- Who is liable? (Developer, deployer, user?)
- Can negligence or product liability laws apply?
- Was there adequate oversight and risk mitigation?
- Regulatory Bodies & Frameworks (U.S. and Global)
United States
Currently no comprehensive federal AI law, but multiple sectoral regulations apply:
| Agency | AI-Relevant Mandate |
| FTC | Deceptive/unfair AI practices; AI marketing claims |
| EEOC | AI in employment discrimination |
| CFPB | AI in lending decisions |
| NIST | AI risk management frameworks |
| FDA | Regulates AI in medical devices |
| DOT/NHTSA | Autonomous vehicle safety |
Executive Order on Safe, Secure, and Trustworthy AI (Oct. 2023):
- Requires federal agencies to assess and regulate AI risk.
- Establishes developer obligations for national security, civil rights, and privacy.
- Introduces algorithmic impact assessments and safety testing.
European Union
EU AI Act (adopted 2024, phased enforcement through 2026):
World’s first comprehensive AI law, classifying systems by risk:
| Risk Level | Examples | Regulation |
| Unacceptable | Social scoring, mass surveillance | Banned |
| High-risk | CV screening, medical diagnostics | Strict conformity assessments |
| Limited risk | Chatbots, customer service AI | Disclosure requirements |
| Minimal risk | AI in games, filters | No restrictions |
Key mandates include:
- Human oversight
- Data governance
- Transparency obligations
- CE-marking for compliant AI
Other Jurisdictions
- UK: Proposes context-based, regulator-led approach.
- Canada: AI and Data Act (AIDA) — risk-based AI regulation.
- China: AI providers must ensure “socialist values” alignment and submit algorithms to government review.
- Common Legal Risks for AI Developers and Users
- Unauthorized use of copyrighted training data
- Failure to mitigate biased outputs
- Violation of privacy laws during data collection or inference
- False advertising about AI capabilities
- Injury or harm caused by automated decisions
- Best Practices for Legal Compliance
- Perform AI risk assessments (e.g., using NIST AI RMF or ISO/IEC 42001)
- Document data lineage, training inputs, and model behavior
- Establish audit trails and logging
- Use human-in-the-loop for high-impact decisions
- Ensure fairness, accountability, transparency, and explainability (FATE) in AI systems
Key Takeaways
- AI is regulated indirectly today, but the legal landscape is rapidly evolving.
- Companies and professionals using or developing AI should:
- Stay updated on federal and state laws.
- Monitor international AI regulations (especially the EU AI Act).
- Prioritize privacy, fairness, and transparency in their tech stack.
Artificial intelligence technology is still a new and emerging industry with unknown or undiscovered components. The governments and judicial systems are grappling with this new technology in an effort to streamline the process. Please feel free to contact our law firm to speak with an artificial intelligence attorney and discuss your questions.