Building Ethically Aligned AI Systems - IBM Research Blog

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. 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.