Giulia De Pasquale

Jialin Li, Marta Zagorowska, Giulia De Pasquale et al.

Ensuring safety is a key aspect in sequential decision making problems, such as robotics or process control. The complexity of the underlying systems often makes finding the optimal decision challenging, especially when the safety-critical system is time-varying. Overcoming the problem of optimizing an unknown time-varying reward subject to unknown time-varying safety constraints, we propose TVSafeOpt, a new algorithm built on Bayesian optimization with a spatio-temporal kernel. The algorithm is capable of safely tracking a time-varying safe region without the need for explicit change detection. Optimality guarantees are also provided for the algorithm when the optimization problem becomes stationary. We show that TVSafeOpt compares favorably against SafeOpt on synthetic data, both regarding safety and optimality. Evaluation on a realistic case study with gas compressors confirms that TVSafeOpt ensures safety when solving time-varying optimization problems with unknown reward and safety functions.

Giulia De Pasquale, Sarah Dean, Paolo Frasca

We propose a control-theoretic interpretation of recommender systems and use this perspective to analyze how fairness interventions shape long-term system behavior. Fairness concerns arise for both users and creators, ranging from opinion polarization and representation bias on the user side to popularity bias on the creator side. A central insight of our analysis is that fairness should not be viewed as a simple trade-off against utility. When optimized over time, it can in fact be beneficial for overall system performance. Realizing these gains, however, requires a clear understanding of the underlying dynamics.

Lukas Schüepp, Carmen Amo Alonso, Florian Dörfler et al.

Recommender systems shape online interactions by matching users with creators content to maximize engagement. Creators, in turn, adapt their content to align with users preferences and enhance their popularity. At the same time, users preferences evolve under the influence of both suggested content from the recommender system and content shared within their social circles. This feedback loop generates a complex interplay between users, creators, and recommender algorithms, which is the key cause of filter bubbles and opinion polarization. We develop a social network-aware recommender system that explicitly accounts for this user-creators feedback interaction and strategically exploits the topology of the user's own social network to promote diversification. Our approach highlights how accounting for and exploiting user's social network in the recommender system design is crucial to mediate filter bubble effects while balancing content diversity with personalization. Provably, opinion clusterization is positively correlated with the influence of recommended content on user opinions. Ultimately, the proposed approach shows the power of socially-aware recommender systems in combating opinion polarization and clusterization phenomena.

Camilla Quaresmini, Valentina Breschi, Jessica Leoni et al.

Fairness in online advertising is often formalized as a distributive justice problem, aiming to ensure that impressions, opportunities, or outcomes are allocated comparably across protected groups. Yet online advertising can still produce harms arising from ads' content and from how recipients interpret and uptake them. To capture this dimension, we draw on Miranda Fricker's notion of hermeneutical injustice. We model ad delivery as a mechanism that distributes interpretative resources and can fail in two ways: relevant concepts can be withheld through systematic under-exposure, leading to hermeneutical deprivation; and recipients may experience hermeneutical distortions when saturated with low-uptake or skewed framings. Grounded in exploratory correlational patterns from the AIDS Advertising Evaluation surveys (1986-1987), we introduce a group-level hermeneutical fairness constraint and a hermeneutically aware utility cost. We integrate them into a benchmark, utility-driven ad allocation framework that already enforces distributive justice, yielding a distributively fair, hermeneutically aware framework that prevents deprivation and distortion from concentrating within protected groups. Through controlled simulations, we explore trade-offs between economic utility, classical distributive fairness constraints, and hermeneutical cost. The results show that purely utility-based allocation drives under-delivery to the disadvantaged group. When the hermeneutical stakes of withholding ads are high, distributive constraints reduce hermeneutical cost at modest utility loss. Conversely, weighting hermeneutical cost without distributive constraints can yield policies concentrated on the disadvantaged group. These findings motivate expanding fairness analyses of online advertising beyond distributive notions to include epistemic conditions of interpretation and uptake.

Nicolò Pagan, Joachim Baumann, Ezzat Elokda et al.

Prediction-based decision-making systems are becoming increasingly prevalent in various domains. Previous studies have demonstrated that such systems are vulnerable to runaway feedback loops, e.g., when police are repeatedly sent back to the same neighborhoods regardless of the actual rate of criminal activity, which exacerbate existing biases. In practice, the automated decisions have dynamic feedback effects on the system itself that can perpetuate over time, making it difficult for short-sighted design choices to control the system's evolution. While researchers started proposing longer-term solutions to prevent adverse outcomes (such as bias towards certain groups), these interventions largely depend on ad hoc modeling assumptions and a rigorous theoretical understanding of the feedback dynamics in ML-based decision-making systems is currently missing. In this paper, we use the language of dynamical systems theory, a branch of applied mathematics that deals with the analysis of the interconnection of systems with dynamic behaviors, to rigorously classify the different types of feedback loops in the ML-based decision-making pipeline. By reviewing existing scholarly work, we show that this classification covers many examples discussed in the algorithmic fairness community, thereby providing a unifying and principled framework to study feedback loops. By qualitative analysis, and through a simulation example of recommender systems, we show which specific types of ML biases are affected by each type of feedback loop. We find that the existence of feedback loops in the ML-based decision-making pipeline can perpetuate, reinforce, or even reduce ML biases.