Redesigning AIDaron Acemoglu A look at how new technologies can be put to use in the creation of a more just society. Artificial Intelligence (AI) is not likely to make humans redundant. Nor will it create superintelligence anytime soon. But it will make huge advances in the next two decades, revolutionize medicine, entertainment, and transport, transform jobs and markets, and vastly increase the amount of information that governments and companies have about individuals. AI for Good leads off with economist and best-selling author Daron Acemoglu, who argues that there are reasons to be concerned about these developments. AI research today pays too much attention to the technological hurtles ahead without enough attention to its disruptive effects on the fabric of society: displacing workers while failing to create new opportunities for them and threatening to undermine democratic governance itself. But the direction of AI development is not preordained. Acemoglu argues for its potential to create shared prosperity and bolster democratic freedoms. But directing it to that task will take great effort: It will require new funding and regulation, new norms and priorities for developers themselves, and regulations over new technologies and their applications. At the intersection of technology and economic justice, this book will bring together experts--economists, legal scholars, policy makers, and developers--to debate these challenges and consider what steps tech companies can do take to ensure the advancement of AI does not further diminish economic prospects of the most vulnerable groups of population. |
Contents
A WORLD WITH LESS WORK | |
THE PANDEMIC BOLSTERED SUPPORT | |
DECOLONIZING | |
BEYOND THE AUTOMATIONONLY APPROACH | |
THE FRONTIER OF AI SCIENCE SHOULD BE | |
IT IS NOT TOO LATE | |
STOP BUILDING BAD | |
WORKPLACE TRAINING IN THE AGE OF | |
MEDICINES MACHINE LEARNING PROBLEM | |
Other editions - View all
Common terms and phrases
advances AI-as-engineering algorithmic already Amazon approach artificial intelligence associate's degree automation benefits better biases challenges cognitive labor cognitive networks collaboration computational corporations create critical crucial Daron Acemoglu datasets decades decision-making deep learning democracy democratic oversight deployed development and deployment disruptive dystopian economic efforts employment Erik Brynjolfsson ethical example face Facebook facial recognition facial recognition systems firms focused frontline funding future gendered GOFAI harms human labor human workers impact increase industry inequality innovation institutions investment Kate Crawford labor market low-wage workers machine learning Maddie mass surveillance minimum wage monitoring non-deployment norms opportunities patients percent political potential predict problem profit question recent recognize redirect regulation risk roboticists robots robust role shared prosperity skill development social social media society surveillance tasks tech companies technological change things transformation workplace