Artificial intelligence is single-handedly changing decision-making at different levels and sectors in often unpredictable and uncontrolled ways. Due to their black-box nature, existing models are difficult to interpret and hence trust. Explainable AI is an emergent field, but, to ensure no loss of predictive power, many of the proposed approaches just build local explanators on top of powerful black-box models. To change this paradigm and create an equally powerful, yet fully explainable model, we need to be able to learn its structure. However, searching for both structure and parameters is extremely challenging. Moreover, there is the risk that the necessary variables and operators are not provided to the algorithm, which leads to more complex and less general models.
It is clear that state-of-the-art, yet practical, real-world solutions cannot come only from the computer science world. Our approach therefore consists in involving human intelligence in the discovery process, resulting in AI and humans working in concert to find better solutions (i.e. models that are effective, comprehensible and generalisable). This is made possible by employing ‘explainable-by-design’ symbolic models and learning algorithms, and by adopting a human-centric, ‘guided empirical’ learning process that integrates cognition, machine learning and human-machine interaction, ultimately resulting in a Transparent, Reliable and Unbiased Smart Tool (TRUST).
This project has designed TRUST, which is both a framework concept and a software platform. The TRUST concept is based on an iterative collaboration between model developers and domain experts, involved in a learning loop with genetic programming algorithms. Multiple tools assist humans in this loop, such as counterfactual, what-if analysis and graphical visualisation. These tools are materialised in the TRUST platform, a modular open-source application, which combines state-of-the-art algorithms with customisable user interfaces.
The TRUST concept has been tested in three main use cases, including predictive and prescriptive problems, in the healthcare, retail and energy sectors. The use cases have shown promising results and have guided the design and customisation of the TRUST platform to a broad range of applications.
