In a recent report on Ethics Guidelines for Trustworthy Artificial Intelligence (AI), AI systems were defined as “ software (and possibly also hardware) systems designed by humans that, given a complex goal, act in the physical or digital dimension by perceiving their environment through data acquisition, interpreting the collected structured or unstructured data, reasoning on the knowledge, or processing the information, derived from this data and deciding the best action(s) to take to achieve the given goal. AI systems can either use symbolic rules or learn a numeric model, and they can also adapt their behavior by analyzing how the environment is affected by their previous actions. As a scientific discipline, AI includes several approaches and techniques, such as machine learning (of which deep learning and reinforcement learning are specific examples), machine reasoning (which includes planning, scheduling, knowledge representation and reasoning, search, and optimization), and robotics (which includes control, perception, sensors and actuators, as well as the integration of all other techniques into cyber-physical systems).” Likewise, according to the same guidelines a “trustworthy AI has three components, which should be met throughout the system's entire life cycle; it should be:
- Lawful, complying with all applicable laws and regulations;
- Ethical, ensuring adherence to ethical principles and values, and
- Robust, both from a technical and social perspective since, even with good intentions, AI systems can cause unintentional harm.”
There are still many open ethical, scientific and technological challenges to build the capabilities that would be needed to achieve a trustworthy AI, especially if, for example, we consider a general AI system which is intended to be a system that can perform most activities that humans can do, such as common-sense reasoning, self-awareness, and the ability of the machine to define its own purpose. Nevertheless, currently deployed AI systems are examples of narrow AI (systems that can perform only one or few specific tasks).
Moreover, ensuring a trustworthy AI requires efforts during its whole life cycle, as such systems can inherited many issued. Constrains may refer to data bias and/or model explicability. In fact, since AI systems rely on data to perform well, if the training data is imbalanced or biased the model will not have the capacity to generalize. Explicability refer to the capacity to provide a form of explanation for the system's decisions, i.e, transparency in terms of understanding how they make decisions.To sum up, overall, achieving a Trustworthy AI must be translated into concrete requirements
(ii) Prevention of harm,
(iii) Fairness and