The self as agent-environment nexus: crossing disciplinary boundaries to help human selves and anticipate artificial selves

Both human and artificial agents learn from and potentially change their environments, but also align and adapt to them. Examples abound, from humans unconsciously tapping feet to the rhythm of music to a cleaning robot keeping a house dust-free. In humans, however, brain-world alignment is also closely linked with mental features like perception or self and mental health disorders lead to profound disruptions in how one considers 'the self'.

As AI agents acquire human and superhuman skill levels and Artificial General Intelligence looms closer, ethical considerations about how an agent will interact with its environment become paramount. What is it about an AI agent that will ensure it can co-exist peacefully in human society and transfer skills seamlessly from one setting to another?

Let us consider a real-world example of an adolescent who is watching television (on their laptop) via a streaming service. Suppose that individual has displayed anti-social behaviours at school or in their home and suppose that he has streamed several violent movies on his laptop over the past few days. Should the recommender system of the streaming service suggest a 'movie that you might like' that, again, contains strong scenes of violence? Or should the recommender system suggest, instead, a documentary on polar bears? This example illustrates three open questions and challenges in AI research. First, how would the recommender system infer the state of the young man? Second, how would a recommender system adapt its recommendations based upon this state, particularly given that the system itself has its own internal goals - i.e. to motivate people to consume more content? And third, is it ethical to probe the human at the human-computer interface in the first place?

The current proposal aims to converge both "free energy" and "dynamic temporo-spatial" approaches to mental features to develop a mathematical and computational framework for environmental alignment and adaptation of intelligent agents.

The primary, direct goal of this approach is to help human agents suffering from abnormal changes in their perception and self, as in psychiatric conditions like schizophrenia. This not only carries major ethical implications for understanding of ourselves, but will also inform the second, broader, aim of this project: paving the way for an understanding and mathematical theory of artificial selves.

Our proposal and study exert strong impact in three ways, that is, economic, clinical, and social impact.

Economic impact: Developing a novel mathematical-computational model of agent-environment interaction carries major implications. Such model will furnish an artificial agent that can respond dynamically to - and decide upon - events in an uncertain environmental context. Our model will thus provide the basis for widespread application and development of agent-environment interaction AI in both public and private settings that are characterized by uncertainty and context sensitivity. As our team members, especially in Canada (M. Fraser, J. Griffith, and Panangaden) show strong association and connection to various AI companies like Google and Facebook in Montreal/Toronto, there is a direct pathway for commercialization of our model. That could not only result in novel patents but also partnerships with some of the giants like Google and Facebook as well as the establishment of spin-off and start-up companies in both UK (London, Oxford) and Canada (Toronto, Montreal, Ottawa). To facilitate transfer from knowledge to application - as well as commercialization - we, due to the extensive connections of our team members (especially in Canada), will, from the very beginning, include the demands on AI as prioritized and in developed by companies lie Facebook and Google.


Clinical impact: Yet another economic impact will be in the field of mental health. Better diagnostic classification of psychiatric disorders like schizophrenia and depression will lead to earlier and more appropriate treatment. That, in turn, will significantly reduce economic burden of especially depression which is the most prevalent disorder (including both medical and psychiatric) according to the World Health Organization. That reduction will be first manifest in UK and Canada as we will implement first our mathematical-computational model of agent-environment interaction in specific psychiatric clinics (Royal Ottawa Mental Health Centre in Ottawa), Douglass/McGill in Montreal. If reducing economic burden, the model will be applied globally as for instance in Taiwan (Taipeh), Italy (Milano), China (Hangzhou), and Japan (Toyio) where the Canadian PI (G. Northoff) has extensive long-standing psychiatric affiliation and connections.


Social impact: Our model of agent-environment interaction and its application to psychiatric disorders carries major social impact. This is so as it will re-define what it is to be a human self and how it converges and is distinct from an artificial self. This will be channeled in our ethical debate. To disseminate our ethical knowledge and its impact, we will develop guidelines and criteria for the human self in its encounter with the artificial self, that is, what it means to be humans. This is especially supported by the fact that the Canadian PI, G. Northoff, has extensive expertise and background in neuroethics. He will work with the current team to discuss the ethical implications and, together also with other specialists in ethics, will develop the guidelines and criteria. It shall also be mentioned that G. Northoff is part of the Human Brain Project of the European Union in project No. 11 that, led by K. Evers and JP. Changeux, focuses on philosophical and ethical implications of neuroscience and AI. Hence, this will facilitate knowledge dissemination and enhance the world-wide impact of the ethical part of the project.