Ethical and Societal Challenges of Machine Learning | (smr 3749)
Starts 7 Nov 2022
Ends 11 Nov 2022
Central European Time
An ICTP Virtual Meeting
From helping farmers adapt to climate change to predicting disease outbreaks, scientists in developing countries have begun turning to ML for more effective solutions. With this potential, however, comes the possibility for abuse, misuse, and unintended consequences.
Embedding Ethics Education in Machine Learning: case studies from various parts of the world that demonstrate the need for a wider perspective on ML ethical challenges.
Big data, privacy and democracy: ethical questions linked with big data exploitation, privacy and the dangers for democracy.
Machine Learning, bias and fairness: problems of ML amplified bias, and some of the possible solutions.
Diversity in ML: causes and impact of this lack of diversity, and some of the success stories that demonstrate how diverse teams are more successful.
Impact of new technologies: impacts of novel technologies like TinyML and global satellite-enabled internet services, especially on people in least developed countries.
P. AHRWEILER, Johannes Gutenberg University Mainz, Germany
C. HEITZ, ZHAW School of Engineering, Switzerland
D. HUYSKES, Privacy Network, Italy
S. KENNEDY, Santa Clara University, USA
A. LAWRENCE, Institute for Astronomy, UK
P. PALMA, UNESCO, France
M. ROVERI, Politecnico di Milano, Italy
T. SCANTAMBURLO, University of Venice, Italy
V. SCHIAFFONATI, Politecnico di Milano, Italy
R. TROTTA, SISSA, Italy