Understanding Legal Liability for AI Tools

Your AI demo impressed everyone. Then Legal killed the deal. This pattern reveals the true adoption blocker most technical founders never see coming. After observing hundreds of AI deals, I've noticed something striking: technical validation is rarely the final hurdle. The real deal-killer emerges when procurement asks: "Who's liable when this fails?" This "indemnity gap" shows up consistently in my work with founder-led B2B companies. You've built impressive technology, exhausted your network, and finally landed meetings with enterprise buyers who love your solution. Then... silence. What happened? While you're celebrating a successful technical evaluation, your prospect's legal team is raising red flags about undefined liability boundaries. The more mission-critical your solution, the more pronounced this becomes. Here's the counterintuitive truth: liability isn't friction—it's lubricant when designed properly. Forward-thinking founders aren't avoiding liability conversations; they're weaponizing them as competitive advantages. How? 1️⃣ Create tiered indemnity structures that grow with implementation phases. Different caps for pilots vs. production deployments align incentives while limiting early-stage exposure. 2️⃣ Partner with specialty insurers who understand your domain. One medical AI startup I work with bundled domain-specific insurance coverage, converting abstract risk into a fixed premium. 3️⃣ Build traceability infrastructure as core product functionality. Capturing model versions, inputs, and decision contexts makes failure attribution clear and speeds resolution. 4️⃣ Draft pre-negotiated liability schedules by use case. This transforms vague anxieties into concrete terms, helping buyers quantify and compare risks. The results speak for themselves. A founder I advise shifted from selling "AI capabilities" to selling "predictable outcomes with defined risk boundaries." Their close rates tripled. Converting uncertainty into defined risk boundaries isn't legal work—it's product work. This perspective shift matters especially for domain experts hitting growth ceilings. Your deep technical knowledge created your product; now your clarity on risk allocation will scale it. AI adoption is fundamentally about risk conversion, not just capability delivery. #startups #founders #growth #ai

Board Directors: A flawed algorithm isn’t just the vendor’s problem…it’s yours also. Because when companies license AI tools, they don’t just license the software. They license the risk. I was made aware of this in a compelling session led by Fayeron Morrison, CPA, CFE for the Private Directors Association®-Southern California AI Special Interest Group. She walked us through three real cases: 🔸 SafeRent – sued over AI tenant screening tool that disproportionately denied housing to Black, Hispanic and low-income applicants 🔸 Workday – sued over allegations that its AI-powered applicant screening tools discriminate against job seekers based on age, race, and disability status. 🔸 Amazon – scrapped a recruiting tool which was found to discriminate against women applying for technical roles Two lessons here: 1.\ Companies can be held legally responsible for the failures or biases in AI tools, even when those tools come from third-party vendors. 2.\ Boards could face personal liability if they fail to ask the right questions or demand oversight. ❎ Neither ignorance nor silence is a defense. Joyce Cacho, PhD, CDI.D, CFA-NY, a recognized board director and governance strategist recently obtained an AI certification (@Cornell) because: -She knows AI is a risk and opportunity. -She assumes that tech industry biases will be embedded in large language models. -She wants it to be documented in the minutes that she asked insightful questions about costs - including #RAGs and other techniques - liability, reputation and operating risks. If you’re on a board, here’s a starter action plan (not exhaustive): ✅ Form an AI governance team to shape transparency culture 🧾 Inventory all AI tools: internal, vendor & experimental 🕵🏽♀️ Conduct initial audits 📝 Review vendor contracts (indemnification, audit rights, data use) Because if your board is serious about strategy, risk, and long-term value… Then AI oversight belongs on your agenda. ASAP What’s your board doing to govern AI?

U.S. state lawmakers are increasingly addressing AI's impact through legislation, focusing on its use in consequential decisions affecting livelihoods, like healthcare and employment. A new report by the Future of Privacy Forum, published 13 Sept 2024, highlights key trends in AI regulation. U.S. state legislation regularly follows a "Governance of AI in Consequential Decisions" approach, regulating AI systems involved in decisions that have a material, legal, or similarly significant impact on an individual’s life, particularly in areas such as education, employment, healthcare, housing, financial services, and government services. These high-stakes decisions are subject to stricter oversight to prevent harm, ensuring fairness, transparency, and accountability by setting responsibilities for developers and deployers, granting consumers rights, and mandating transparency and ongoing risk assessments for systems affecting life opportunities. Examples of key laws regulating AI in consequential decisions include Colorado SB 24-205 (will enter into force in Feb 2026), California AB 2930, Connecticut SB 2, and Virginia HB 747 (all proposed). * * * This approach typically defines responsibilities for developers and deployers: Developer: A developer is an individual or organization that creates or builds the AI system. They are responsible for tasks such as: - Determining the purpose of the AI, - Gathering and preprocessing data, - Selecting algorithms, training models, and evaluating performance. - Ensuring the AI system is transparent, fair, and safe during the design phase. - Providing documentation about the system’s capabilities, limitations, and risks. - Supporting deployers in integrating and using the AI system responsibly. Deployer: A deployer is an individual or organization that uses the AI system in real-world applications. Their obligations typically include: - Providing notice to affected individuals when AI is involved in decision-making. - Conducting post-deployment monitoring to ensure the system operates as expected and does not cause harm. - Maintaining a risk management program and testing the AI system regularly to ensure it aligns with legal and ethical standards. * * * U.S. State AI regulations often grant consumers rights when AI affects their lives, including: 1. Notice: Consumers must be informed when AI is used in decisions like employment or credit.    2. Explanation and Appeal: Individuals can request an explanation and challenge unfair outcomes. 3. Transparency: AI decision-making must be clear and accountable. 4. Ongoing Risk Assessments: Regular reviews are required to monitor AI for biases or risks. Exceptions for certain technologies, small businesses, or public interest activities are also common to reduce regulatory burdens. by Tatiana Rice, Jordan Francis, Keir Lamont

🛑 Only tracking new AI laws? You could be missing the bigger risk.🛑 State agencies and attorneys general (AGs) are issuing guidance on how existing laws apply to AI—often shaping enforcement more than headline legislation. 💡 Why Agency and AG Guidance Deserves Your Attention * Immediate Impact: These interpretations clarify how current consumer protection, anti-discrimination, and privacy laws already govern AI. * Enforcement is Here: Agencies use this guidance as their roadmap for investigations—sometimes before new statutes are active. * Industry Insights: Guidance often targets AI risks in specific sectors like healthcare and employment, where broad legislation may fall short. 🗺️ Recent Examples from Across the US * Massachusetts: Advisory confirms consumer protection and civil rights laws apply to AI tools. * New Jersey: Guidance warns of liability for algorithmic discrimination—even when unintentional. * Oregon: AG reminds companies AI is covered under existing privacy and equality acts. For a deeper dive, follow the link in the comments for the full post. #AI #ResponsibleAI #AIGovernance

Big news from Florida with important ramifications for AI responsibility, safety and the public interest: A judge just ruled that she is NOT prepared to hold chatbot outputs is speech, and hence not protected under the First Amendment. Allowing a lawsuit against Character.ai by the family of a teen who died by suicide after becoming obsessed with his “AI companion” to move forward could be a watershed moment for AI accountability. AI companies shouldn't get the same sweetheart 1A + Section 230 protections that Big Tech has enjoyed for decades. When your AI product causes real harm, they need to own that responsibility, not foist it off on society. Here's what matters:  🎯 No First Amendment shield - The judge basically said "nice try" to Character.AI's argument that chatbot outputs deserve 1st Amendment rights. Phew. This is an important step to ensure that humans not AI bots are the beneficiaries of constitutional rights. ⚖️ Product liability applies - Character.AI is a product, not just a service, so Florida's strict product liability laws kick in (this is a big deal for victims seeking justice). (We Bipin Kumar B Jess Rapson Johanna Barop Suryansh Mehta dived deeper into this in our recent G7 Policy Brief on AI Competition and Consumer Rights: https://lnkd.in/gmpQEsBK) 🎪 Google can't hide - Claims against Google AND the company's founders survive, even though they tried to dodge responsibility. Let’s recall that according to reports they developed the idea at Google, which initially refused to roll out their AI companion product over safety concerns, prompting them to leave to start their own company only to be hired back and aquire their tech. 📱 Accountability - A 14-year-old died after his chatbot told him to "come home" right before he took his own life. This isn't about stifling innovation - it's about basic responsibility and accountability for ensuring that AI products don’t go to market before they are safe and lawful, and that if you choose to take the risk so you can profit, you must also bear the responsibility and the cost. We’ve seen how expansive protections against liability allowed Big Tech to pursue socially corrosive, addictive, and mentally detrimental products and content moderation approaches, making it more important than ever that we avoid doing the same for generative AI. We can't let AI innovation become just another way for Big Tech to privatize profits while socializing risks. The future of AI should benefit everyone, not just shareholders. #AI #TechAccountability #ProductLiability #AIPolicy #innovation #BigTech https://lnkd.in/gCE2t9UZ

Who's liable when AI gets it wrong in healthcare? It's the question I hear from physicians constantly: "If I follow the AI recommendation and it's wrong, am I liable? If I ignore it and it was right, am I also liable?" Europe might be changing the answer. Three new EU regulations are shifting AI liability from a "prove harm after the fact" model to a "prove safety upfront" approach: 🔹 EU AI Act: High-risk healthcare AI must meet strict safety requirements by 2026 🔹 EU Product Liability Directive: Software developers now treated like device manufacturers—if your AI doesn't meet safety standards and causes harm, you're presumed liable 🔹 European Health Data Space: Mandates data sharing for AI development with €20M+ fines for non-compliance Similarly, the UK just classified AI ambient scribes as medical devices requiring full regulatory approval. The shift: Instead of physicians wondering "did I make the right clinical decision with this AI?" the question becomes "did the AI company prove their system was safe before I used it?" Even if you're not in Europe, these standards often become global norms (think GDPR). Read more here: https://lnkd.in/gWSYVQn8