Artificial Intelligence and Automation

Computer-based systems today can outperform people in more and more tasks once considered to be within the exclusive competence of humans. Automation has historically produced long-term growth and full employment, despite initial job losses. And the introduction of computer technology into the workplace has followed this pattern over the last thirty years. But some suggest that really smart AI-based machines have the potential to create the sustained technological unemployment that John Maynard Keynes warned against in the 1930s. In the short, term policymakers should provide additional resources to educational and training programs to provide the U.S. workforce with 21st Century skills. In the medium term, policymakers should consider funding initiatives to develop efficient human-machine collaborations for workplace applications, and human capital investment in the skills humans will need to work alongside increasingly capable machines. In the long term, policymakers should ramp up human capital investments for a less labor-intensive economy and give some thought to support mechanisms such as a basic universal income.

New Economic and Policy Research on AI and the Future of Work (2017)
In the year since SIIA released its issue brief on AI and the Future of Jobs in December 2016, economists, political scientists, educators and policymakers have provided new insights into how AI systems might change the nature of work.  New policy proposals related to the growth of AI-systems in the workplace have been advanced and old ones refurbished.  Congress has been active this year as well.  This SIIA update reviews these developments to provide a current snapshot of the economic, technical and policy discussion related to AI and the future of work.

Ethical Principles for Artificial Intelligence and Data Analytics (2017)
The application of big data analytics has already improved lives in innumerable ways. It has improved the way teachers instruct students, doctors diagnose and treat patients, lenders find creditworthy customers, financial service companies control money laundering and terrorist financing, and governments deliver services. But the use of big data analytics also poses ethical risks involving fairness, accountability, and transparency. The bounty of big data analytics is not always widely available and too often its distribution fails to be equitable.

Artificial Intelligence and the Future of Work (2016)
While it is true that robots have replaces workers in many manufacturing jobs, embracing automation has the potential to be beneficial to workers and to the economy.  The government needs to place a greater focus on STEM education.  We need to increase the number of skilled software engineers and researchers in artificial intelligence who will enable us to maintain our lead in these fields. There also needs to be better training and retraining programs to fill the needed open positions.  As some workers are displaced, many more jobs will be created.

Algorithmic Fairness (2016)
Discussions of algorithmic fairness have increased in the last several years, even though the underlying issues of disparate impact on protected classes have been around for decades. They appear primed to move into the mainstream of policy discussions in the new Congress and Administration. This renewed discussion derives in part from changes in computer technology that have driven increased use of powerful data analytics tools in more and more areas of social and economic life.