Tempering Transparency in Human-Robot Interaction

In recent years, particular interest has been taken by researchers and governments in examining and regulating aspects of transparency and explainability within artificially intelligent (AI) system. An AI system is “transparent” if humans can understand the mechanisms behind its behavior and use this understanding to make predictions about future behavior while the goal of explainable AI is to clarify an AI system's actions in a way that humans can understand. With this increased interest, research has presented conflicting views on the benefits of algorithmic transparency and explanations [1]. Moreover, research has also highlighted flaws within policy implementations of algorithmic transparency which generally remain too vague and often results in deficient adoption [2]. Even with these pitfalls of transparency, it seems as if the default view of many societies is that AI systems should be made more transparent and explainable; however, we argue that there needs to exist added skepticism of this position. In particular, we believe it is a useful exercise to consider exploring, as a counternarrative, an emerging area within computing that necessitates a lack of transparency-deceptive AI. The newly evolving area of research pertains to the creation (intentionally or not) of AI agents that learn to deceive humans and other AI agents. Here we define deception as “the process by which actions are chosen to manipulate beliefs so as to take advantage of the erroneous inferences” [3] and we use this interchangeably with “lying”. While there may be physically designed aspects of deception in embodied agents, such as the anthropomorphism and zoomorphism of robots [4], [5], here we wish to focus on deception related to utterances and actions of AI agents. On its surface, the idea of deceptive AI agents may not readily seem beneficial; however, there exists added effort to create AI agents that are able to be integrated socially within our societies. Seeing as deception is a foundational part of many human and animal groups, some argue that giving AI agents the ability to learn to deceive is necessary and inevitable for them to truly interact effectively [6], [7]. In fact, it has been found that deception can be an emergent behavior when training systems on human data [8]-thus strengthening the notion that behaving deceptively is a part of what it means to interact with humans. Moreover, prior research has shown that AI deception, rather than transparent truthfulness, can lead to better outcomes in human-robot interactions [9]–[11]. However, deception does of course have obvious drawbacks including an erosion of trust [12]–[15] and decreasing desired reutilization [12], [15]. Because of these negative aspects, and the clear possibly of malicious usage, some suggest the need for entirely truthful agents [16]. However, due to the infancy and lack of knowledge of the effects (short and long term) of deception within human-AI agent interaction, it is currently not possible to definitively determine the lasting implications of either encouraging or banning the practice. Given that transparency and explainability are in contention with deception, while also neither of the ideas are entirely beneficial nor detrimental, this presents important nuance when determining ethical and regulatory considerations of how AI agents should behave [17]. As such, the goal of this work is to present AI deception as a counternarrative to balance transparency and explainability with other considerations to spur discussions on how to be proactive, rather than reactive, to unforeseen consequences of our choices when designing AI systems that interact with humans.