{"id":111058,"date":"2025-11-07T16:08:06","date_gmt":"2025-11-07T15:08:06","guid":{"rendered":"https:\/\/simplifier.io\/news\/ai-responsibility-in-management-why-mathematical-understanding-determines-liability-risks\/"},"modified":"2025-12-04T14:22:50","modified_gmt":"2025-12-04T13:22:50","slug":"ai-responsibility-in-management-why-mathematical-understanding-determines-liability-risks","status":"publish","type":"post","link":"https:\/\/simplifier.io\/en\/strategy\/ai-responsibility-in-management-why-mathematical-understanding-determines-liability-risks\/","title":{"rendered":"AI responsibility in the company: Why mathematical understanding determines liability risks"},"content":{"rendered":"

AI – the hype is huge, the opportunities and potential seemingly endless, but a worrying trend is emerging: Companies are using AI systems in critical business processes without understanding the fundamental liability risks. The problem? They are personally liable for every mistake – while AI providers deny any responsibility. <\/p>\n

Here’s the framework for responsible AI deployment that every business leader needs to understand before integrating AI into operational processes – and how to protect your business from existential risk.<\/p>\n

The mathematical foundation: Why AI systems inevitably produce incorrect outputs<\/h2>\n

Most managers treat AI like deterministic software. This is a dangerous mistake. <\/p>\n<\/div><\/div>

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AI systems work in a fundamentally different way to conventional software.<\/strong> <\/span>They calculate probabilities for each answer based on patterns from their training data. This means <\/span>Every AI output is a statistical estimate, not a factual certainty.<\/strong> <\/span>Mathematically speaking, these systems can never guarantee a zero percent probability of convincing but incorrect answers.<\/p>\n

This is not a weakness of the system – it is a property of the loss function for which these systems are optimized.<\/p>\n<\/div><\/div>

The four critical qualities you need to internalize:<\/h3>\n
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  1. The system advises in case of uncertainty,<\/strong> instead of admitting ignorance, because the optimization goals in the training reward this<\/li>\n
  2. Plausibility and correctness are statistically independent<\/strong> <\/span>– convincingly formulated does not mean factually correct<\/li>\n
  3. Reproducibility does not exist<\/strong> <\/span>across model updates – what works today may not work tomorrow<\/li>\n
  4. You are liable for every output<\/strong> <\/span>in business processes – the vendor is not liable<\/li>\n<\/ol>\n

    The legal reality: Full transfer of liability to the user<\/h2>\n

    The legal situation is clear and devastating for companies:<\/p>\n<\/div><\/div>

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    OpenAI Terms of Service 2025<\/strong>: “THE SERVICES ARE PROVIDED ‘AS IS’ […] WE MAKE NO WARRANTIES REGARDING ACCURACY”. Anthropic, Google and Meta use identical wording. <\/span>The provider is not liable for incorrect outputs. The user bears the full risk. <\/strong><\/p>\n

    Managing director liability according to \u00a7 43 GmbHG<\/h3>\n

    Anyone who uses systems for which they do not know the types of error is acting negligently. Unlike established technologies, where responsibility can be delegated to qualified persons – such as qualified persons in pharmaceutical manufacturing – there are no state-recognized certifications or regulated responsible parties for LLMs. <\/p>\n

    The management bears the responsibility directly and cannot delegate it in a legally binding manner.<\/strong><\/p>\n

    GDPR compliance: an unsolvable problem<\/h3>\n

    GDPR Article 22 requires explainability for automated decisions. LLMs cannot causally explain why token X was chosen. Fines: up to 20 million euros or four percent of annual turnover. <\/p>\n

    Empirical cases: When AI errors become expensive<\/h2>\n

    Air Canada 2024<\/strong>: The chatbot gave the wrong refund policy. The company had to pay – not the AI provider. A Transformer-based LLM generated highly-confident, syntactically correct, semantically plausible false statements that were indistinguishable from correct outputs for non-experts. <\/p>\n<\/div><\/div>

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    This case shows the reality: <\/span>The user is liable, not the technology provider.<\/strong><\/p>\n

    The insurance dilemma: incalculable risks<\/h2>\n

    The insurance industry has recognized the problem:<\/p>\n