Rossi, Francesca


Voting with Random Classifiers (VORACE)

arXiv.org Artificial Intelligence

In many machine learning scenarios, looking for the best classifier that fits a particular dataset can be very costly in terms of time and resources. Moreover, it can require deep knowledge of the specific domain. We propose a new technique which does not require profound expertise in the domain and avoids the commonly used strategy of hyper-parameter tuning and model selection. Our method is an innovative ensemble technique that uses voting rules over a set of randomly-generated classifiers. Given a new input sample, we interpret the output of each classifier as a ranking over the set of possible classes. We then aggregate these output rankings using a voting rule, which treats them as preferences over the classes. We show that our approach obtains good results compared to the state-of-the-art, both providing a theoretical analysis and an empirical evaluation of the approach on several datasets.


Using deceased-donor kidneys to initiate chains of living donor kidney paired donations: algorithms and experimentation

arXiv.org Artificial Intelligence

We design a flexible algorithm that exploits deceased donor kidneys to initiate chains of living donor kidney paired donations, combining deceased and living donor allocation mechanisms to improve the quantity and quality of kidney transplants. The advantages of this approach have been measured using retrospective data on the pool of donor/recipient incompatible and desensitized pairs at the Padua University Hospital, the largest center for living donor kidney transplants in Italy. The experiments show a remarkable improvement on the number of patients with incompatible donor who could be transplanted, a decrease in the number of desensitization procedures, and an increase in the number of UT patients (that is, patients unlikely to be transplanted for immunological reasons) in the waiting list who could receive an organ.


Building Ethically Bounded AI

arXiv.org Artificial Intelligence

The more AI agents are deployed in scenarios with possibly unexpected situations, the more they need to be flexible, adaptive, and creative in achieving the goal we have given them. Thus, a certain level of freedom to choose the best path to the goal is inherent in making AI robust and flexible enough. At the same time, however, the pervasive deployment of AI in our life, whether AI is autonomous or collaborating with humans, raises several ethical challenges. AI agents should be aware and follow appropriate ethical principles and should thus exhibit properties such as fairness or other virtues. These ethical principles should define the boundaries of AI's freedom and creativity. However, it is still a challenge to understand how to specify and reason with ethical boundaries in AI agents and how to combine them appropriately with subjective preferences and goal specifications. Some initial attempts employ either a data-driven example-based approach for both, or a symbolic rule-based approach for both. We envision a modular approach where any AI technique can be used for any of these essential ingredients in decision making or decision support systems, paired with a contextual approach to define their combination and relative weight. In a world where neither humans nor AI systems work in isolation, but are tightly interconnected, e.g., the Internet of Things, we also envision a compositional approach to building ethically bounded AI, where the ethical properties of each component can be fruitfully exploited to derive those of the overall system. In this paper we define and motivate the notion of ethically-bounded AI, we describe two concrete examples, and we outline some outstanding challenges.


CPDist: Deep Siamese Networks for Learning Distances Between Structured Preferences

arXiv.org Artificial Intelligence

Preference are central to decision making by both machines and humans. Representing, learning, and reasoning with preferences is an important area of study both within computer science and across the sciences. When working with preferences it is necessary to understand and compute the distance between sets of objects, e.g., the preferences of a user and a the descriptions of objects to be recommended. We present CPDist, a novel neural network to address the problem of learning to measure the distance between structured preference representations. We use the popular CP-net formalism to represent preferences and then leverage deep neural networks to learn a recently proposed metric function that is computationally hard to compute directly. CPDist is a novel metric learning approach based on the use of deep siamese networks which learn the Kendal Tau distance between partial orders that are induced by compact preference representations. We find that CPDist is able to learn the distance function with high accuracy and outperform existing approximation algorithms on both the regression and classification task using less computation time. Performance remains good even when CPDist is trained with only a small number of samples compared to the dimension of the solution space, indicating the network generalizes well.


Interpretable Multi-Objective Reinforcement Learning through Policy Orchestration

arXiv.org Artificial Intelligence

Autonomous cyber-physical agents and systems play an increasingly large role in our lives. To ensure that agents behave in ways aligned with the values of the societies in which they operate, we must develop techniques that allow these agents to not only maximize their reward in an environment, but also to learn and follow the implicit constraints of society. These constraints and norms can come from any number of sources including regulations, business process guidelines, laws, ethical principles, social norms, and moral values. We detail a novel approach that uses inverse reinforcement learning to learn a set of unspecified constraints from demonstrations of the task, and reinforcement learning to learn to maximize the environment rewards. More precisely, we assume that an agent can observe traces of behavior of members of the society but has no access to the explicit set of constraints that give rise to the observed behavior. Inverse reinforcement learning is used to learn such constraints, that are then combined with a possibly orthogonal value function through the use of a contextual bandit-based orchestrator that picks a contextually-appropriate choice between the two policies (constraint-based and environment reward-based) when taking actions. The contextual bandit orchestrator allows the agent to mix policies in novel ways, taking the best actions from either a reward maximizing or constrained policy. In addition, the orchestrator is transparent on which policy is being employed at each time step. We test our algorithms using a Pac-Man domain and show that the agent is able to learn to act optimally, act within the demonstrated constraints, and mix these two functions in complex ways.


Incorporating Behavioral Constraints in Online AI Systems

arXiv.org Artificial Intelligence

AI systems that learn through reward feedback about the actions they take are increasingly deployed in domains that have significant impact on our daily life. However, in many cases the online rewards should not be the only guiding criteria, as there are additional constraints and/or priorities imposed by regulations, values, preferences, or ethical principles. We detail a novel online agent that learns a set of behavioral constraints by observation and uses these learned constraints as a guide when making decisions in an online setting while still being reactive to reward feedback. To define this agent, we propose to adopt a novel extension to the classical contextual multi-armed bandit setting and we provide a new algorithm called Behavior Constrained Thompson Sampling (BCTS) that allows for online learning while obeying exogenous constraints. Our agent learns a constrained policy that implements the observed behavioral constraints demonstrated by a teacher agent, and then uses this constrained policy to guide the reward-based online exploration and exploitation. We characterize the upper bound on the expected regret of the contextual bandit algorithm that underlies our agent and provide a case study with real world data in two application domains. Our experiments show that the designed agent is able to act within the set of behavior constraints without significantly degrading its overall reward performance.


Towards Composable Bias Rating of AI Services

arXiv.org Artificial Intelligence

A new wave of decision-support systems are being built today using AI services that draw insights from data (like text and video) and incorporate them in human-in-the-loop assistance. However, just as we expect humans to be ethical, the same expectation needs to be met by automated systems that increasingly get delegated to act on their behalf. A very important aspect of an ethical behavior is to avoid (intended, perceived, or accidental) bias. Bias occurs when the data distribution is not representative enough of the natural phenomenon one wants to model and reason about. The possibly biased behavior of a service is hard to detect and handle if the AI service is merely being used and not developed from scratch, since the training data set is not available. In this situation, we envisage a 3rd party rating agency that is independent of the API producer or consumer and has its own set of biased and unbiased data, with customizable distributions. We propose a 2-step rating approach that generates bias ratings signifying whether the AI service is unbiased compensating, data-sensitive biased, or biased. The approach also works on composite services. We implement it in the context of text translation and report interesting results.


Loreggia

AAAI Conferences

If we want AI systems to make decisions, or to support humans in making them, we need to make sure they are aware of the ethical principles that are involved in such decisions, so they can guide towards decisions that are conform to the ethical principles. Complex decisions that we make on a daily basis are based on our own subjective preferences over the possible options. In this respect, the CP-net formalism is a convenient and expressive way to model preferences over decisions with multiple features. However, often the subjective preferences of the decision makers may need to be checked against exogenous priorities such as those provided by ethical principles, feasibility constraints, or safety regulations. Hence, it is essential to have principled ways to evaluate if preferences are compatible with such priorities. To do this, we describe also such priorities via CP-nets and we define a notion of distance between the ordering induced by two CPnets. We also provide tractable approximation algorithms for computing the distance and we define a procedure that uses the distance to check if the preferences are close enough to the ethical principles. We then provide an experimental evaluation showing that the quality of the decision with respect to the subjective preferences does not significantly degrade when conforming to the ethical principles.


Preferences and Ethical Principles in Decision Making

AAAI Conferences

If we want AI systems to make decisions, or to support humans in making them, we need to make sure they are aware of the ethical principles that are involved in such decisions, so they can guide towards decisions that are conform to the ethical principles. Complex decisions that we make on a daily basis are based on our own subjective preferences over the possible options. In this respect, the CP-net formalism is a convenient and expressive way to model preferences over decisions with multiple features. However, often the subjective preferences of the decision makers may need to be checked against exogenous priorities such as those provided by ethical principles, feasibility constraints, or safety regulations. Hence, it is essential to have principled ways to evaluate if preferences are compatible with such priorities. To do this, we describe also such priorities via CP-nets and we define a notion of distance between the ordering induced by two CPnets. We also provide tractable approximation algorithms for computing the distance and we define a procedure that uses the distance to check if the preferences are close enough to the ethical principles. We then provide an experimental evaluation showing that the quality of the decision with respect to the subjective preferences does not significantly degrade when conforming to the ethical principles.


Loreggia

AAAI Conferences

Recently a large attention has been devoted to the ethical issues arising around the design and the implementation of artificial agents. This is due to the fact that humans and machines more and more often need to collaborate to decide on actions to take or decisions to make. Such decisions should be not only correct and optimal from the point of view of the overall goal to be reached, but should also agree to some form of moral values which are aligned to the human ones. Examples of such scenarios can be seen in autonomous vehicles, medical diagnosis support systems, and many other domains, where humans and artificial intelligent systems cooperate. One of the main issues arising in this context regards ways to model and reason with moral values. In this paper we discuss the possible use of AI compact preference models as a promising approach to model, reason, and embed moral values in decision support systems.