Can AI ever win our trust?

This blog was written by Harald van der Weel, Data Management Architect at SynTouch, a subsidiary of SUPERP.

According to many psychological and philosophical theories, trust rests on three foundations: empathy, logic, and authenticity. The question then is: if these three conditions are leading, can AI ever truly win our trust? And if so, under what conditions?

1. Logic – The rationale behind AI.
   AI excels at handling logic. It can analyze enormous amounts of data at lightning speed, make connections, and take decisions based on statistical probabilities. In many cases, this makes AI systems more accurate, consistent, and less biased than humans.
   Think of making medical diagnoses, risk assessment in insurance, or pattern recognition in cybersecurity. AI applications have been in use in these areas for years and, provided they are properly trained, are considered reasonably reliable, especially in terms of logic.
   However, this requires that the AI is correctly fed with representative, high-quality data and that the algorithm is transparent and explainable. Only then can we trust that the system reasons logically and fairly.
2. Empathy – Empathizing or simulating?
   Empathy requires the ability to put yourself in someone else’s shoes. AI can now recognize emotions from voice, facial expressions, or word usage. Based on this, it can respond appropriately, from a friendly chatbot to a digital coach that signals stress.
   But AI does not really feel this empathy: it is simulated. An AI knows what sad behavior is, but not what sadness itself is. This arouses suspicion in people: we can sense unerringly when compassion is genuine or seems to be feigned.
   Nevertheless, research and practical experience show that simulation can be sufficient to build trust, as long as the behavior is predictable, consistent, and helpful. This already plays a role in healthcare and customer service: users sometimes become attached to AI assistants that respond well to the user’s emotions, even though they know that this AI is ‘not a real person’.
3. Authenticity or transparency?
   Authenticity is about honesty, sincerity, and intentions. With people, this means knowing where someone stands and trusting that they have no hidden agenda.
   AI itself, however, has no will of its own, no consciousness, and therefore no intrinsic intention. What it does stems from the goals that people program into it and the data it uses.

This is therefore an important prerequisite for trust: transparency. Users need to know:

• Who designed the AI system?
• For what purpose it is used
• What data it uses
• What the limitations are
• And who is responsible for mistakes

Without this transparency, it is impossible to consider AI as authentic or reliable.

Can AI win our trust?

Based on the above, the answer to the main question is nuanced: yes, AI can win our trust, but within limits.
AI is ideally suited to earning functional trust: we trust it with specific, defined tasks as long as we understand how it works and why it works. Think of navigation, document analysis, or process automation.
But human trust, based on moral values, genuine empathy, and shared beliefs, seems out of reach for now. AI feels nothing, thinks nothing, and knows no emotion. The moral framework is missing.

The answer lies with us

Ultimately, trust lies not in AI itself, but in the way we as humans use AI. If we are transparent about its purpose, scope, and operation, if we inform users properly, and if we continue to monitor for errors and safety, then AI can become a reliable partner.

An important condition for this is adding the human component to the process in which AI is used. Human supervision, by monitoring, assessing, and adjusting where necessary, further contributes to trust. Not only because errors are then noticed more quickly, but also because users feel that ultimately a human being is watching and is responsible.