How to avoid misinterpretations in AI and how to apply methodological rigor to have reliable, useful and fair tools?

With the experts Santi Seguí, Oriol Pujol and Jordi Vitrià of the University of Barcelona

In this Breakfast & Learn we talked about Artificial Intelligence and Mental Health.

One of the key points of the session was the importance of developing fair and reliable AI tools involves working to identify and mitigate misinterpretations. Misinterpretations are not inevitable, they can be corrected firstly by ensuring the collection of diverse and representative data and, secondly, by designing functions that truly respond to the health needs raised. It should also be taken into account that mitigating biases is a continuous process: it requires continuing to audit and validate the system even when it is already in operation, and to do so often and systematically.

The speakers emphasized the fundamental role of methodological rigor to avoid erroneous conclusions and ensure that AI tools are truly useful in decision-making. In this sense, they have emphasized the importance of differentiating between correlation and causation to avoid biased interpretations that could compromise the effectiveness and safety of these systems.

All three speakers have highlighted the need for constant vigilance in the development of AI to ensure fairness and minimize risks in critical areas such as mental health.

Here you can see the video of the session.