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The more AI agents are deployed in scenarios with possibly unexpected situations, the more they need to be flexible, adaptive, and creative in achieving their goals. Thus, a certain level of freedom to choose the best path to a specific goal is necessary in making AI robust and flexible enough to be deployed successfully in real-life scenarios.
This is especially true when AI systems tackle difficult problems whose solution cannot be accurately defined by a traditional rule-based approach but require the data-driven and/or learning approaches increasingly being used in AI. Indeed, data-driven AI systems, such as those using machine learning, are very successful in terms of accuracy and flexibility, and they can be very "creative" in solving a problem, finding solutions that could positively surprise humans and teach them innovative ways to resolve a challenge.
Creativity - Freedom - Boundaries - Actions - AI
However, creativity and freedom without boundaries can sometimes lead to undesired actions: the AI system could achieve its goal in ways that are not considered acceptable according to values and norms of the impacted community. Thus, there is a growing need to understand how to constrain the actions of an AI system by providing boundaries within which the system must operate. This is usually referred to as the "value alignment" problem, since such boundaries should model values and principles required for the specific AI application scenario.
At IBM Research, we have studied and assessed two ways to align AI systems to ethical principles:
Formalism - Preferences - Service - Personalization - Priorities
The first uses the same formalism to model and combine subjective preferences (to achieve service personalization) and ethical priorities (to achieve value alignment). A notion of distance between preferences and ethical priorities is used to decide if actions can be determined just by the preferences or if we need to consider additional ethical priorities, when the preferences are too...
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