If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
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.
Burton, Emanuelle (University of Kentucky) | Goldsmith, Judy (University of Kentucky) | Koenig, Sven (University of Southern California) | Kuipers, Benjamin (University of Michigan) | Mattei, Nicholas (IBM Research) | Walsh, Toby (University of New South Wales and Data61)