Research Blog / Data Repository
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
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
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
A practical explainer on Ciarb’s Guideline on the Use of AI in Arbitration, including party use, arbitrator use, disclosure, tribunal powers, and model language. Ciarb’s
A practical AI evidence preservation checklist covering prompts, outputs, logs, version history, incidents, internal records, and confidentiality controls. If an AI dispute looks likely, evidence
A practical guide to AI vendor disputes, including performance failures, hallucinations, misleading claims, confidentiality issues, product changes, and evidence problems. Many AI disputes will start
A practical guide to AI model licensing disputes, including access rights, scope limits, fine-tuning, sublicensing, output rights, termination, and evidence problems. AI model licensing disputes
A practical guide to California AI arbitration, including neutral ethics, disclosure, confidentiality, privacy, consumer and employment sensitivity, and contract drafting issues. California matters in AI
A practical comparison of AI dispute resolution and litigation, including confidentiality, speed, cost, evidence, technical complexity, injunctive relief, and enforceability. Not every AI dispute belongs
An AI arbitration clause does not need to be flashy. It needs to fit the contract. If the relationship could produce disputes over model access,