Mediation vs Arbitration for AI Disputes: Which Process Fits the Problem?
Not every AI dispute should go straight to arbitration, and not every one should be pushed into mediation first. The better question is which process fits the actual problem: a technical contract fight, a privacy-sensitive business conflict, a relationship worth preserving, or a dispute that needs a binding answer. This…
AI Hallucination and Reliance Disputes: When Wrong Outputs Create Real Liability
A guide to AI hallucination and reliance disputes, including wrong outputs, causation, disclaimers, consumer harm, workplace use, vendor liability, and evidence preservation. AI hallucination disputes are not only about whether a model got something wrong. They are about who relied on the output, what the system was supposed to do,…
AI Dispute Resolution Resources: Official Rules, Guidance, and Sources
A curated AI dispute resolution resources page covering official arbitration rules, AI guidance, California sources, privacy regulators, employment guidance, and technical standards. The best AI dispute resolution work starts with source discipline. This resource page gathers the official rules, guidance, standards, California sources, and regulator materials most useful for understanding…
AI Neutral Disclosure Checklist for AI-Related Arbitrations
An AI neutral disclosure checklist covering tool use, materiality, confidentiality, conflicts, human judgment, and when disclosure should be made in arbitration. As arbitrators and parties begin using AI tools more often, the real question is no longer whether disclosure might matter. It is what should be disclosed, when, and at…
AI Governance Disputes: Oversight, Accountability, and Risk Management Failures
A practical guide to AI governance disputes, including oversight failures, unclear ownership, risk assessments, escalation gaps, board management tension, and evidence preservation. Many serious AI disputes are mislabeled as product disputes or vendor disputes when the deeper conflict is really about governance. Who approved the system, who owned the risk,…
AI Employment Disputes: Hiring, Monitoring, Bias, and Workplace Risk
A practical guide to AI employment disputes, including hiring tools, workplace surveillance, disability accommodation, adverse impact, recordkeeping, and arbitration risk. AI employment disputes are not only about futuristic hiring bots. They also arise from resume screening, video interviews, productivity scoring, workplace monitoring, accommodation failures, and automated decisions about pay, promotion,…
AI Dispute Risk Matrix: A Practical Framework for AI Legal and Business Risk
A practical AI dispute risk matrix for assessing confidentiality, evidence, bias, vendor, governance, consumer, employment, and urgency risks before conflict escalates. Many AI disputes look similar on the surface and then break in completely different ways once confidentiality, evidence, consumer sensitivity, or governance failure enters the picture. This risk matrix…
AI Consumer Disputes: When Chatbots, Automated Decisions, and AI Claims Go Wrong
A practical guide to AI consumer disputes, including deceptive AI claims, chatbot failures, privacy issues, automated decisions, refunds, evidence, and arbitration risk. AI consumer disputes are rarely just about a model giving a wrong answer. They usually involve something more operational: a misleading product claim, a broken chatbot support loop,…
Training Data Disputes: Ownership, Permission, and Proof
A practical guide to training data disputes, including ownership claims, permission, provenance, licensing, privacy, contractual restrictions, and proof problems. Training data disputes often sound like debates about AI policy, but in practice they are usually disputes about permission, provenance, contracts, privacy, proof, and who can actually show what happened. This…