  Artificial Intelligence (AI) is becoming the corner stone of many systems
used in our daily lives such as autonomous vehicles, healthcare systems, and
unmanned aircraft systems. Machine Learning is a field of AI that enables
systems to learn from data and make decisions on new data based on models to
achieve a given goal. The stochastic nature of AI models makes verification and
validation tasks challenging. Moreover, there are intrinsic biaises in AI
models such as reproductibility bias, selection bias (e.g., races, genders,
color), and reporting bias (i.e., results that do not reflect the reality).
Increasingly, there is also a particular attention to the ethical, legal, and
societal impacts of AI. AI systems are difficult to audit and certify because
of their black-box nature. They also appear to be vulnerable to threats; AI
systems can misbehave when untrusted data are given, making them insecure and
unsafe. Governments, national and international organizations have proposed
several principles to overcome these challenges but their applications in
practice are limited and there are different interpretations in the principles
that can bias implementations. In this paper, we examine trust in the context
of AI-based systems to understand what it means for an AI system to be
trustworthy and identify actions that need to be undertaken to ensure that AI
systems are trustworthy. To achieve this goal, we first review existing
approaches proposed for ensuring the trustworthiness of AI systems, in order to
identify potential conceptual gaps in understanding what trustworthy AI is.
Then, we suggest a trust (resp. zero-trust) model for AI and suggest a set of
properties that should be satisfied to ensure the trustworthiness of AI
systems.
