Machine Learning (ML) systems, whether predictive or generative, not only reproduce biases and stereotypes but, even more worryingly, amplify them. Strategies for bias detection and mitigation typically focus on either ex post or ex ante approaches, but are always limited to two...

We present a method for quantitative, in-depth analyses of fairness issues in AI systems with an application to credit scoring. To this aim we use BRIO, a tool for the evaluation of AI systems with respect to social unfairness and, more in general, ethically undesirable behaviours....

We present a tool for the detection of biased behaviours in AI systems. Using a specific probability-based algorithm, we provide the means to compare the action of the user’s algorithm of choice on a specific feature that they deem “sensible” with respect to fixed classes and to a...

In this paper we present the probabilistic typed natural deduction calculus TPTND, designed to reason about and derive trustworthiness properties of probabilistic computational processes, like those underlying current AI applications. Derivability in TPTND is interpreted as the...

The extensive deployment of probabilistic algorithms has radically changed our perspective on several well-established computational notions. Correctness is probably the most basic one. While a typical probabilistic program cannot be said to compute the correct result, we often have...

The notion of trustworthiness, central to many fields of human inquiry, has recently attracted the attention of various researchers in logic, computer science, and artificial intelligence (AI). Both conceptual and formal approaches for modeling trustworthiness as a (desirable)...