New Course Modules: “A Cybernetics Approach to Ethical AI Design” explores the relationship between cybernetics and AI ethics, and looks at how cybernetics can be leveraged to reframe how we think about and how we undertake ethical AI design. This module, by Ellen Broad, Associate Professor and Associate Director at the Australian National University’s School of Cybernetics, is divided into three sections, beginning with an introduction to cybernetics. Following that, we explore different ways of thinking about AI ethics, before concluding by bringing the two concepts together to understand a new approach to ethical AI design.
How should organizations put AI ethics and responsible AI into practice? Is the answer AI ethics principles and AI ethics boards or should everyone developing AI systems become experts in ethics? In “An Ethics Model for Innovation: The PiE (Puzzle-solving in Ethics) Model”, Cansu Canca, Founder and Director of the AI Ethics Lab, presents the model developed and employed at AI Ethics Lab: The Puzzle-solving in Ethics (PiE) Model. The PiE Model is a comprehensive and structured practice framework for organizations to integrate ethics into their operations as they develop and deploy AI systems. The PiE Model aims to make ethics a robust and integral part of innovation and enhance innovation through ethical puzzle-solving.
Nuria Oliver, Co-Founder and Scientific Director of the ELLIS Alicante Unit, presents “Data Science against COVID-19: The Valencian Experience”. In this module, we explore the ELLIS Alicante Foundation’s Data-Science for COVID-19 team’s work in the Valencian region of Spain. The team was founded in response to the pandemic in March 2020 to assist policymakers in making informed, evidence-based decisions. The team tackles four different work areas: modeling human mobility, building computational epidemiological models, predictive models on the prevalence of the disease, and operating one of the largest online citizen surveys related to COVID-19 in the world. This lecture explains the four work streams and shares lessons learned from their work at the intersection between data, AI, and the pandemic…(More)”.