Abstract
The use of AI algorithms to support public decisions is spreading in public administrations while with an asymmetrical speed it has already transformed the private sector. Substantial questions are still open about the algorithmic legality of AI choices. Deep neural networks do not allow full eXplainability of the decision-making process as the programmer himself does not have the ability to be aware of the logical steps taken to achieve the proposed goal. In these cases the question arises of the “AI black box” which collides with the rules of Italian administrative law which, on the contrary, seeks total transparency as a great metaphor for a “glass house” public administration and is oriented towards a shared administration with citizens. The objective of the paper is to explore the torsion points of algorithmic decision making towards the rules of the administrative procedure, bringing the algorithmic procedure back to a transparency in line with the European Commission's choice of an anthropocentric and transparent artificial intelligence by design that aims the improvement of the algorithm's eXplainability techniques (XAI). If refining XAI will be the key to a complete application of AI in the PA, this path, in order not to slow down the spread of AI and thus increase the gap to the detriment of the public sector, will have to be preceded by a logic of experimentation and incremental input: from the widespread automation of routine activities and administrative procedures with no margin of discretion to a gradual experimentation of AI solutions in complex use cases to support public decision-making through parallel analysis and verification processes. This approach can only be guaranteed through a joint work of jurists and computer scientists in the design of transparent algorithms by design, in the dogmatic construction of an Algorithmic Decision Making to respond to the principle of enhanced transparency required to guarantee the legitimacy of A.I. in administrative activity.
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Carlizzi, D.N., Quattrone, A. (2023). The Algorithmic Public Decision, Between Explainability, Administrative Discretion and Data-Driven Decision Making. In: Marino, D., Monaca, M. (eds) Artificial Intelligence and Economics: the Key to the Future. Lecture Notes in Networks and Systems, vol 523. Springer, Cham. https://doi.org/10.1007/978-3-031-14605-3_11
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