AI & Society Roundtable organised by Humane AI Net

The RPA Human(e) AI would like to bring your attention to the following event, organised by Humane AI Net:



A collective intelligence exercise towards shaping the research questions of Social AI, driven by societal challenges. It is implemented through a structured conversation among inter-disciplinary scientists, looking at the relationship between AI and society from multiple perspectives.

For human-AI scientists and social scientists, the challenge is how to achieve better understanding of how AI technologies could support or affect emerging social challenges, and how to design human-centered AI ecosystems that help mitigate harms and foster beneficial outcomes oriented at the social good.

Social Artificial Intelligence

As increasingly complex socio-technical systems emerge, made of people and intelligent machines, the social dimension of AI becomes evident. Examples range from urban mobility, with travellers helped by smart assistants to fulfill their agendas, to the public discourse and the markets, where diffusion of opinions as well as economic and financial decisions are shaped by personalized recommendation systems. In principle, AI could empower communities to face complex societal challenges. Or it can create further vulnerabilities and exacerbate problems, such as bias, inequalities, polarization, and depletion of social goods.

The point is that a crowd of (interacting) intelligent individuals is not necessarily an intelligent crowd. On the contrary, it can be stupid in many cases, due to network effects: the sum of many individually “optimal” choices is often not collectively beneficial, because individual choices interact and influence each other, on top of common resources. Navigation systems suggest directions that make sense from an individual perspective, but may create a mess if too many drivers are directed on the same route. Personalized recommendations on social media often make sense to the user, but may artificially amplify polarization, echo-cambers, filter bubbles, and radicalization. Profiling and targeted advertising may further increase inequality and monopolies, with harms of perpetuating and amplifying biases, discriminations and “tragedies of the commons”.

The network effects of AI and their impact on society are not sufficiently addressed by AI research, first of all because they require a step ahead in the trans-disciplinary integration of AI, data science, network science and complex systems with the social sciences. How to understand and mitigate the harmful outcomes? How to design “social AI” mechanisms that help towards agreed collective outcomes, such as sustainable mobility in cities, diversity and pluralism in the public debate, fair distribution of resources?


Please register here