Each year when fall comes, I teach finance ethics to bright new postgraduate students in finance. After introducing ethical investing – i.e. the practice of integrating ethical criteria such as environmental, social, and governance performance (ESG) in investment decisions – I ask them a question: “Who believes that ESG investing generates higher financial returns?”
Author: Thomas Ferretti
I am a Lecturer in Ethics and Sustainable Business at the University of Greenwich (UK). Before that, I taught for five years at the London School of Economics. My work specialises in moral and political philosophy, business ethics, and AI ethics. I hold a Ph.D. in Philosophy from UCLouvain (BEL, 2016). Read more: https://www.thomasferretti.com/
Artificial intelligence (AI) and machine learning (ML) have seen impressive developments in the last decades. Think about Google’s DeepMind defeating Lee Sedol, the best human player of Go, with their program AlphaGo in 2015. The latest version, AlphaZero, is remarkable because it relied on deep reinforcement learning to learn how to play Go entirely by itself from scratch: with only the rules of the game, through trial and error, and playing millions of games against itself. Machine learning algorithms have a range of other practical applications, from image recognition in medical diagnostics to energy management.
The rapid growth of the ‘sharing’ or ‘platform’ economy, with the rise of well-known brands such as Zipcar, Uber, Airbnb, or CouchSurfing, has generated enthusiasm and concerns about precarious work. In my new article in the Journal of Social Philosophy, I investigate, from a broadly liberal egalitarian perspective, how public administrations should regulate these new kinds of economic organizations in a way that respects principles of justice and maximizes the prospects of the least advantaged. In particular, I argue that preventing unfair inequalities could require changing the kind of organizations running these platforms.