Trustworthy AI is not perfect AI
Introduction
Trustworthy AI is not perfect AI: it is AI that is transparent, supervised by humans and able to recognise its own limits. In industry, where every minute counts, recognising that AI can be wrong is an essential condition for strengthening the trust of technicians and maintenance managers.
This is a central concern for industrial intelligence platforms such as Mimorian, which models equipment, structures failure diagnosis and captures the know-how of maintenance teams through a multi-agent AI architecture.
Why AI makes and will keep making mistakes
AI is not an oracle. It relies on probabilistic models and on training data that, by their very nature, come with limits:
- Biases and gaps in the data: some situations or rare failures are barely represented.
- Drift over time (data drift): the machine evolves, its components wear out, but historical data may no longer match reality.
- Out-of-distribution cases: a new or atypical failure can throw the AI off balance.
Studies show that systems that are too confident in their results (or conversely too hesitant) disrupt human-AI collaboration and undermine trust【arxiv.org】.
Transparency and supervision: the keys to handling error
Acknowledging error is a strength, not a weakness. For an AI to remain credible:
- It must state its limits: specify the level of confidence, indicate when the data is insufficient, and warn in cases of uncertainty.
- It must be supervised by humans: technicians and engineers always keep control, validating, correcting and deciding.
This is also what the European AI Act requires: for high-risk systems, proportionate human oversight must always be in place【artificialintelligenceact.eu】.
Acceptance on the ground: a key concern
On the ground, technicians do not reject AI because it makes mistakes. They reject it when it gets things wrong without warning or when it remains opaque.
By contrast, a tool that explains its lines of reasoning, that shows its uncertainties and that leaves control to the user strengthens trust.
👉 This is exactly what maintenance managers are looking for: a copilot, not a substitute.
How to design useful AI despite its mistakes
For an industrial AI to remain reliable while admitting its limits, several practices are essential:
- Confidence scores: indicate the probability associated with each hypothesis.
- Transparency in the interface: clearly flag the cases where the AI is not sure.
- Field feedback loops (lessons learned): incorporate the corrections made by technicians to improve the model.
- Training the teams: explain to users what the AI can and cannot do.
Conclusion
Accepting that AI makes mistakes is not a weakness. It is an essential condition for it to become a credible tool, adopted and used day to day.
At Mimorian, we have made this choice: our AI does not replace teams, it supports them. It proposes hypotheses ranked by probability, traceable back to their source, and the technician always keeps control to validate, dismiss or set aside each line of reasoning. By making its lines of reasoning, its uncertainties and its limits visible, it strengthens trust on the ground.
Because in the end, perfect AI does not exist. But trustworthy AI does.
For an overview of the topic, read our complete guide: What is trustworthy AI in industry? A complete guide for maintenance.
📚 Sources :
- Ethics Guidelines for Trustworthy AI - Commission Européenne【digital-strategy.ec.europa.eu】
- AI Act - Article 14 : supervision humaine obligatoire【artificialintelligenceact.eu】
- Overconfident and Unconfident AI Hinder Human-AI Collaboration - arXiv (2024)【arxiv.org】
- Transparency and accountability in AI systems - Frontiers (2024)【frontiersin.org】
- Challenges and Limitations of Human Oversight in Ethical AI - PMC (2024)【pmc.ncbi.nlm.nih.gov】