United States Legal Articles & Guides — 87 Articles

Banks increasingly use artificial intelligence to detect fraud, suspicious transactions and potential financial crime. But automated systems can also produce false positives, freeze legitimate transactions or incorrectly flag innocent customers. This guide examines the legal and compliance issues surrounding AI fraud detection, AML monitoring, account restrictions, model risk, human review and financial institutions' responsibilities when algorithms get it wrong.

Banks and fintech lenders increasingly use artificial intelligence to assess creditworthiness, detect risk and automate loan decisions. But when an algorithm determines whether someone receives credit, questions of explainability, discrimination, data accuracy and consumer rights become unavoidable. This guide explains how AI credit scoring interacts with ECOA, Regulation B, the Fair Credit Reporting Act, adverse-action requirements and financial-sector model governance.

Banks and fintech companies increasingly use artificial intelligence for credit underwriting, fraud detection, customer service, marketing and financial advice. But AI does not operate outside financial regulation. This guide explains how existing banking and consumer-finance laws interact with AI, including ECOA, Regulation B, adverse-action notices, model risk management, automated underwriting, third-party AI vendors and the changing 2026 regulatory landscape.

Artificial intelligence increasingly influences decisions about employment, lending, housing, insurance, education and access to services. But an algorithm can reproduce discrimination even when no human explicitly instructs it to discriminate. This guide explains algorithmic bias, disparate impact, proxy discrimination, protected characteristics and the growing legal challenge of applying civil-rights principles to automated decision-making.

An employer cannot necessarily avoid legal responsibility by saying that “the algorithm made the decision.” When AI is used for hiring, employee monitoring, promotion, discipline or termination, responsibility can involve the employer, technology vendor and human decision-makers. This guide explains employer liability, AI vendor contracts, discrimination claims, negligent deployment, human oversight and the legal risks of relying on automated employment decisions.

Artificial intelligence increasingly influences decisions about employment, lending, housing, insurance, education and access to services. But an algorithm can reproduce discrimination even when no human explicitly instructs it to discriminate. This guide explains algorithmic bias, disparate impact, proxy discrimination, protected characteristics and the growing legal challenge of applying civil-rights principles to automated decision-making.

Employers increasingly use artificial intelligence to monitor productivity, analyse communications, track work activity and manage remote employees. But how far can workplace surveillance legally go? This guide explains AI employee monitoring, privacy expectations, biometric surveillance, productivity tracking, consent, data protection and the legal risks employers should consider before deploying workplace-monitoring systems.

Artificial intelligence is increasingly used to screen CVs, rank candidates, analyse interviews and recommend hiring decisions. But what happens when an algorithm disadvantages applicants because of race, sex, age, disability or another protected characteristic? This guide explains how existing employment-discrimination law applies to AI hiring systems, including disparate impact, disability accommodation, algorithmic bias and New York City's automated employment decision tool requirements.

Artificial intelligence is making cybercrime faster, cheaper and more convincing. Criminals can use generative AI for phishing, voice cloning, deepfake impersonation, synthetic identities and financial fraud. This guide explains how AI-enabled crime works, which existing criminal laws may apply, and what businesses can do to reduce their exposure.

Artificial intelligence can generate convincing photographs, videos, audio recordings and documents that never existed. As synthetic media becomes increasingly realistic, courts face a fundamental evidentiary question: how should judges determine whether digital evidence is authentic? This guide explains authentication, admissibility, provenance, metadata, expert testimony, deepfake detection and the emerging evidentiary challenges created by generative AI.

Generative AI can produce false statements about real people, businesses and organisations. But when an AI system generates a defamatory statement, who can be sued—the person who prompted it, the AI provider, the publisher, or nobody? This guide explains how traditional defamation principles interact with generative AI, AI hallucinations, chatbots and synthetic content.

Deepfakes can imitate a person's face, voice or actions with remarkable realism. As synthetic media becomes easier to create, existing rules on privacy, defamation, fraud, intellectual property and non-consensual intimate imagery are increasingly being applied alongside new AI-specific regulation. This guide explains the legal risks associated with deepfakes in 2026.