Projects

Deceptive AI

Autonomous agents might develop or be endowed with the ability to deceive. Deceptive machines first appear, more or less, as subtle concepts in Turing’s famous Imitation Game. In this game, their role is to trick humans into assigning them the property of intelligence (and perhaps even the property of being phenomenally conscious?). Events that revolve around fake news indicate that humans are more susceptible than ever to mental manipulation by powerful technological tools. My concern is that, given future advancements in AI, these tools may become fully autonomous. This threat made me think that there might be several reasons for which we might consider modelling such agents. Now, the big question that follows from this is “How do we model these artificial agents in a manner such that we increase our understanding of them, instead of increasing the risks they might pose?”. With this question in mind, in Stefan Sarkadi’s PhD thesis , he gives the first full computational treatment to deception in AI. However, if you’re not into reading PhD theses, you can have a look at his paper in IEEE Technology and Society to get a brief overview and history of the concept of deceptive AI

To anyone interested in delving deeper into this topic, HIDE Lab recommend having a look at some of the symposia and workshops on deceptive AI (some of which we have co-organised): the 1st International Workshop on Deceptive AI @ECAI2020 and the 2nd International Workshop on Deceptive AI @IJCAI2021 , the 2015 AAAI Fall Symposium on Deceptive and Counter-Deceptive Machines, and the 2017 Deceptive Machines Workshop @NeurIPS. Don’t forget to check out the Deceptive AI Springer book containing the joint proceedings of the two International Workshops on Deceptive AI.

Deception Modelling and Analysis

Deception is becoming an increasingly complex socio-cognitive phenomenon that is difficult to detect and reason about. The HIDE Lab’s main research tackles the integration of techniques from AI and Intelligence Analysis to understand deception in hybrid human-machine societies. One avenue of our research is to explore the generation of narratives about interactions in complex and adaptive multi-agent systems in order to help intelligence analysts perform inference to the best explanation. We have recently been awarded fellowship grants by the Royal Academy of Engineering through the UK IC Postdoctoral Research Fellowship scheme for the project entitled Enhancing deception analysis with storytelling AI.

Governing Knowledge-Sharing in Hybrid Societies

We are now in the age of deceptive AI ecosystems where knowledge exchange has a significant role in how humans and machines adapt to each other. How do we ensure that hybrid societies, where humans and machines interact as agents will exchange knowledge in an honest, ethical, and sustainable manner?

To begin to answer this question, we must understand not only the ethics of deceptive AI, but also how deception evolves in human-machine societies, and how societies govern themselves to become resilient in the face of deception.

Moreover, we must also look at how properties that were initially considered technical, such as the interoperability of Web agents, are actually influenced by external evolutionary pressures of society such as financial incentives and businesses strategies at large. Subsequently, the evolution of human-machine relation might have ripple effects into the adoption of technology, putting various businesses at risk, such as agrirobotics.

Reflective and Meta-Cognitive AI

Reflection, done right, could allow machines to reason and model the consequences of their actions in complex environments and together with the ability of using Theory of Mind, it enables them to model and reason about other agents' minds in these environments. Some of the scientific literature on this topic shows that Theory of Mind could increase the performance of artificial agents, making them more efficient than artificial agents that lack this ability. This includes making them more effective at deceiving. However, modelling others agents' minds is a difficult task, given that it involves many factors of uncertainty such as the uncertainty of the communication channel, the uncertainty of reading other agents correctly, and the uncertainty of trust in other agents. Together with Peter Lewis's Trustworthy AI Lab at Ontario tech, we are highly engaged this research topic, especially in the modelling of how artificial agents can cause changes in the beliefs of other agents through communication and how they reflect on their own mental processes and selves. However, we must cautiously tread this path, as we could risk ending in an arms race in Theory of Mind between machines that deceive and machines that detect deception.

Computational Argumentation

How do machines to explain and justify their reasoning and decision making? Argumentation in AI is seeing an increased interest due to its potential in shedding light onto issues like Explainable AI. The HIDE Lab is actively doing research on how machines can generate meaningful arguments during social interactions. Members and collaborators of the HIDE Lab have previously founded the Online Handbook for Argumentation in AI. The purpose of this handbook is to provide an open access and curated anthology for the argumentation research community. OHAAI will act as a research hub to keep track of the latest and upcoming topics and applications of argumentation in AI. The handbook mainly aims to present argumentation research conducted by current PhD students and early-career researchers in all areas where argumentation can be applied to AI. The handbook’s ulterior goal is to encourage collaboration and knowledge discovery between members of the argumentation community. As of 2022, OHAAI has become part of the COMMA conference. Students who submit extended abstracts to OHAAI usually have the opportunity to present their work at the COMMA Summer School on Argumentation.