Skip to main content
FairLens: Auditing Black-box Clinical Decision Support Systems
Exploratory: Social Impact of AI and Explainable ML
Does immigration make Europeans less supportive of redistribution?
Exploratory: Sustainable Cities for Citizens
A contribution on basic questions regarding AI in law

In September 2020, Oxford University Press published The Oxford Handbook of Ethics and AI. This work comprises 44 different contributions divided in five sections and is edited by Markus D. Dubber, Professor of Law & Criminology and Director of the Centre for Ethics, University of Toronto, Frank Pasquale, Piper & Marbury Professor of Law, University of Maryland, and Sunit Das, Associate Professor in the Department of Surgery, University of Toronto.

Falling Walls Circle Table: Understanding the Scientific Method in the 21st Century

Against the background of the Covid-19 pandemic, which proves to provide fertile ground to intensify the ‘information disorder’ characterised by conspiracy theories and ‘alternative facts’, it is vital to underline the relevance of science and the value of the scientific method that lies at its core and has been developed over centuries.

Dynamics of Scientific Collaboration Networks in Academic Migrations

Mobility is an important aspect of a researcher's life, affecting the career of the scientist in many ways: through the change of host institutions, new career opportunities are chased, positions with higher prestige acquired, stronger collaborations can be created, and novel projects are started. As a result, the collaboration network, the productivity, and the research impact of the studies of the researcher are possibly affected every time a researcher changes their host institution.

Responsible AI: from principles to action

From 6th to 9th October 2020, the 12th International Conference on Social Informatics (SocInfo 2020), took place in a digital format.

Network Medicine: Disease Genes Prioritization Problem
Exploratory: Network Medicine
Crash Prediction and Risk Assessment with Individual Mobility Network

The massive and increasing availability of mobility data enables the study and the prediction of human mobility behavior and activities at various levels. This month we address the problem of building a data-driven model for predicting car drivers’ risk of experiencing a crash in the mid-term future.