In the amendments adopted by the European Parliament on 14 June 2023 on the Artificial Intelligence Act, the EU's regulatory stance on AI Manipulation is outlined as such: "(a) the placing on the market, putting into service or use of an AI system that deploys subliminal techniques beyond a person's consciousness or purposefully manipulative or deceptive techniques, with the objective to or the effect of materially distorting a person's or a group of persons' behaviour by appreciably impairing the person's ability to make an informed decision, thereby causing the person to take a decision that that person would not have otherwise taken in a manner that causes or is likely to cause that person, another person or group of persons significant harm; The prohibition of AI system that deploys subliminal techniques referred to in the first sub-paragraph shall not apply to AI systems intended to be used for approved therapeutical purposes on the basis of specific informed consent of the individuals that are exposed to them or, where applicable, of their legal guardian; (b) the placing on the market, putting into service or use of an AI system that exploits any of the vulnerabilities of a person or a specific group of persons, including characteristics of such person's or such group's known or predicted personality traits or social or economic situation, age, physical or mental ability with the objective or to the effect of materially distorting the behaviour of that person or a person pertaining to that group in a manner that causes or is likely to cause that person or another person significant harm [1]" We argue that in the current regulatory framing, there is a lack of clarity of core concepts in the present amendments. For example, "personality traits" are mentioned six times in the latest amendments, and yet are not defined at any point in the document, or in the draft of the Act [2, 1].

Recently computer scientists at USC Institute of Technologies (ICT) set out to assess under what conditions humans would employ deceptive negotiating tactics. Through a series of studies, they found that whether humans would embrace a range of deceptive and sneaky techniques was dependent both on the humans' prior negotiating experience in negotiating as well as whether virtual agents where employed to negotiate on their behalf. The findings stand in contrast to prior studies and show that when humans use intermediaries in the form of virtual agents, they feel more comfortable employing more deceptive techniques than they would normally use when negotiating for themselves. Lead author of the paper on these studies, Johnathan Mell, says, "We want to understand the conditions under which people act deceptively, in some cases purely by giving them an artificial intelligence agent that can do their dirty work for them." Nowadays, virtual agents are employed nearly everywhere, from automated bidders on sites like eBay to virtual assistants on smart phones.

Negotiation is the complex social process by which multiple parties come to mutual agreement over a series of issues. As such, it has proven to be a key challenge problem for designing adequately social AIs that can effectively navigate this space. Artificial AI agents that are capable of negotiating must be capable of realizing policies and strategies that govern offer acceptances, offer generation, preference elicitation, and more. But the next generation of agents must also adapt to reflect their users’ experiences.      The best human negotiators tend to have honed their craft through hours of practice and experience. But, not all negotiators agree on which strategic tactics to use, and endorsement of deceptive tactics in particular is a controversial topic for many negotiators. We examine the ways in which deceptive tactics are used and endorsed in non-repeated human negotiation and show that prior experience plays a key role in governing what tactics are seen as acceptable or useful in negotiation. Previous work has indicated that people that negotiate through artificial agent representatives may be more inclined to fairness than those people that negotiate directly. We present a series of three user studies that challenge this initial assumption and expand on this picture by examining the role of past experience.      This work constructs a new scale for measuring endorsement of manipulative negotiation tactics and introduces its use to artificial intelligence research. It continues by presenting the results of a series of three studies that examine how negotiating experience can change what negotiation tactics and strategies human endorse. Study #1 looks at human endorsement of deceptive techniques based on prior negotiating experience as well as representative effects. Study #2 further characterizes the negativity of prior experience in relation to endorsement of deceptive techniques. Finally, in Study #3, we show that the lessons learned from the empirical observations in Study #1 and #2 can in fact be induced—by designing agents that provide a specific type of negative experience, human endorsement of deception can be predictably manipulated.