Artificial intelligence has assumed such relevance in the global debate that not a day goes by without a multiplicity of articles and interviews, scientific reports, and interventions by authorities and public institutions. It is difficult to disentangle oneself from this flood of information and even more challenging to keep up with the analysis of the different critical profiles raised by artificial intelligence tools, or by machine learning or deep learning mechanisms. Profiles that do not only concern data protection, but involve many fundamental rights, security aspects, have social and even global policy implications in relation to the development of democratic societies.
In this article – having the GDPR regulation on automated decision-making processes as a reference horizon and taking some cues from the proposed AI Act whose legislative path should be in the final stage – we will try to highlight some basic considerations on the subject.
AI vs. applied statistics
In an interview that appeared in the Financial Times of 2 June 2023, Chinese American science fiction writer Ted Chiang, when asked by a journalist what alternative appellation to ‘artificial intelligence’ he would use, replied without hesitation: ‘applied statistics’. Chiang pointed out that computers (or algorithms) are not yet conscious, hence the incorrect use of the term ‘intelligence’.
Originally, AI system operations were predetermined by rules of logical deduction; later, predetermination gave way – as Luciano Floridi points out – to statistical ‘correlation’ of data. If we want to be reasonably confident that we will not be contradicted by technological evolution, we can generally define an algorithm as a sequence of logical steps that allows a certain result to be produced. In the case of machine learning or deep learning, the interaction between machines creates more complex processes that incorporate statistics and probability criteria.
