Responding to the COVID-19 Unemployment Crisis and Meeting the Future of Work Challenge
Summary
Due to technology’s disruptive force in society and on the labor force, voices representing business and state governments have recently emphasized the need to revisit the social contract among firms, employees, governments, and citizens. This need has only intensified with the COVID-19 pandemic. The economic emergency associated with the pandemic has left 21.5 million workers unemployed and an additional 11.5 million workers with reduced pay to date. Today’s unemployment numbers are far worse than during the 2008 Great Recession. Underscoring the racial disparity seen in this economic crisis, Black and Latinx workers are currently experiencing higher rates of unemployment than white workers.
The next president should immediately sign two Executive Orders (EOs) to address the current crisis in work and the urgent economic emergency that has left Americans evicted, unable to pay bills, make rent, or put food on the table. The first EO would modernize unemployment insurance nationwide by boosting state unemployment insurance programs. The second would establish a U.S. Future of Work Commission tasked with developing a new model of work that addresses the key challenges the Fourth Industrial Revolution presents to American workers today.
AI’s high-stakes outputs directly shape American economic, industrial, and technological outcomes. They must not be accepted entirely on the basis of institutional faith rather than verifiable evidence.
The clearinghouse will also neutrally mediate the assignment of auditors to audits—strengthening independence, trust, and ethical governance across the AI ecosystem.
Soft law was never meant to be a permanent solution. Treating it as one, and letting the sandcastle stand in for the skyscraper indefinitely, is how we end up with a decade of voluntary commitments and no enforceable accountability to show for it.
Jobseekers, employees at smaller organizations, and local public workers need more than a weekend crash course in AI. They need practice choosing useful tasks, protecting data, testing outputs, and explaining where human judgment remains necessary.