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The European Union Artificial Intelligence (AI) Act proposes to ban AI systems that ”manipulate persons through subliminal techniques or exploit the fragility of vulnerable individuals, and could potentially harm the manipulated individual or third person”. This article takes the perspective of cognitive psychology to analyze and understand what algorithmic manipulation consists of, who vulnerable individuals may be, and what is considered as harm. Subliminal techniques are expanded with concepts from behavioral science and the study of preference change. Individual psychometric differences which can be exploited are used to expand the concept of vulnerable individuals. The concept of harm is explored beyond physical and psychological harm to consider harm to one's time and right to an un-manipulated opinion. The paper offers policy recommendations that extend from the paper's analyses.

As artificial intelligence becomes more powerful and a ubiquitous presence in daily life, it is imperative to understand and manage the impact of AI systems on our lives and decisions. Modern ML systems often change user behavior (e.g. personalized recommender systems learn user preferences to deliver recommendations that change online behavior). An externality of behavior change is preference change. This article argues for the establishment of a multidisciplinary endeavor focused on understanding how AI systems change preference: Preference Science. We operationalize preference to incorporate concepts from various disciplines, outlining the importance of meta-preferences and preference-change preferences, and proposing a preliminary framework for how preferences change. We draw a distinction between preference change, permissible preference change, and outright preference manipulation. A diversity of disciplines contribute unique insights to this framework.

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Intent modifies an actor's culpability of many types wrongdoing. Autonomous Algorithmic Agents have the capability of causing harm, and whilst their current lack of legal personhood precludes them from committing crimes, it is useful for a number of parties to understand under what type of intentional mode an algorithm might transgress. From the perspective of the creator or owner they would like ensure that their algorithms never intend to cause harm by doing things that would otherwise be labelled criminal if committed by a legal person. Prosecutors might have an interest in understanding whether the actions of an algorithm were internally intended according to a transparent definition of the concept. The presence or absence of intention in the algorithmic agent might inform the court as to the complicity of its owner. This article introduces definitions for direct, oblique (or indirect) and ulterior intent which can be used to test for intent in an algorithmic actor.