Admissibility of AI-generated conclusions

Admissibility of AI-generated conclusions refers to the legal and procedural acceptability of AI-derived conclusions. It requires AI systems to be transparent, explainable, and verifiable. According to the EU AI Act (Regulation (EU) 2024/1689) and ISO 42001, AI-generated outputs must be understandable by human supervisors to ensure they are not arbitrary or discriminatory. This concept is central to AI governance, as it dictates whether an AI's output can be used as a basis for legal or administrative actions. The focus is on the 'right to explanation'—ensuring that any individual affected by an AI decision can understand the reasoning behind it, which is a cornerstone of modern AI ethics and regulation.

In practice, enterprises must implement three key elements: Explainability, Traceability, and Human Oversight. First, companies should adopt Explainable AI (XAI) techniques to ensure AI conclusions are not 'black boxes,' making them understandable for compliance audits. Second, a robust documentation system must be established, recording the model version, input data, and decision-making logic, as required by ISO 42001. Third, a 'Human-in-the-loop'-protocol must be implemented, where AI conclusions are treated as recommendations rather than final decisions, especially in high-risk applications like credit scoring or recruitment. For example, a European company using AI for hiring must be able to demonstrate to regulators that its AI does not discriminate based on protected characteristics, or face fines up to €35 million or 7% of global turnover.

Taiwan enterprises face three primary challenges: technical complexity of modern AI models, the high cost of compliance documentation, and the lack of localized expertise in AI law. To overcome these, companies should: 1. Invest in XAI tools like SHAP or LIME to make AI outputs interpretable; 2. Establish an AI Governance Committee comprising legal, technical, and business experts to oversee AI deployments; 3. Implement a phased approach, starting with low-risk AI applications before moving to high-risk systems. The priority should be the EU AI Act compliance for any company exporting services or products to the European market, with a typical implementation timeline of 6 to 12 months to achieve full alignment with international standards.

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