By Joao Guerreiro

Joao Guerreiro

The short stories in Isaac Asimov’s “I, Robot” discussed some of the dilemmas associated with artificial intelligence (AI). New developments in AI technology have brought these concerns from the pages of science-fiction stories to the forefront of policy discussions. In May 2023, a global AI consortium declared AI risks a priority, similar to pandemics and nuclear war. The G7 started the Hiroshima AI Process for global AI regulation. Both the European Union and the U.S. are discussing regulatory frameworks. Ideas include mandatory testing, holding developers accountable, and classifying AI into risk tiers. The critical issue is the uncertainty surrounding AI’s societal costs and benefits.

How should AI be regulated in the presence of uncertainty regarding the AI’s potential adverse external effects? Professor Joao Guerreiro, with Sergio Rebelo (Kellogg School of Management) and Pedro Teles (Banco de Portugal and Catolica-Lisbon School of Business and Economics), tackles this question in a recent paper: “Regulating Artificial Intelligence.” The paper evaluates regulatory approaches using a normative analysis. The authors explore two settings. In the first scenario, uncertainty is only resolved after the release of the AI. In the second scenario, it is possible to beta-test the algorithm to assess external effects.

In the absence of beta testing, there’s a mismatch between the optimal AI novelty level for society and what naturally arises in an unregulated market. The paper argues that the social optimum, or the ideal balance of AI novelty and safety, is generally more conservative than what the market would select without regulation. With beta testing, developers can learn the external effects of the AI by testing before release. This approach helps resolve uncertainties regarding potential negative externalities. Still, the social optimum requires a higher degree of conservativism both in testing and the algorithm’s release.

The authors evaluate three regulatory frameworks. First, they show that subjecting algorithms to regulatory approval is insufficient to implement the social optimum– since developers still have the incentive to go for too-risky algorithms. Second, simply holding developers liable for the external effects of the algorithms is also insufficient to implement the social optimum in the presence of limited liability. Finally, they show that mandating beta testing to assess the externalities and holding developers liable for the adverse effects of the algorithm can achieve the social optimum, even in the presence of limited liability.

Overall, the paper’s findings highlight the complexity of AI regulation and the need for nuanced approaches that balance innovation and safety. By considering various scenarios and regulatory frameworks, it provides valuable insights for policymakers and stakeholders in the AI field.

By Jacob Kohlhepp

Full article available here.

Jacob Kohlhepp

Anyone who has shopped at a Costco and a corner convenience store knows that firms selling the same products assign tasks to workers differently and have dramatically different levels of profitability. Economists have long recognized via case studies and theoretical work that some of these differences are due to differences in organizational capability. For example, some companies use sophisticated workforce management software while others use a clipboard. But are these individual examples reflective of a larger reality? And if so, how does incorporating organizational capabilities change our understanding of economics?

In his paper “The Inner Beauty of Firms,” Professor Jacob Kohlhepp (UNC Chapel Hill and UCLA graduate) answers these questions using millions of task assignments across hundreds of hair salons. He finds that salons using the same management software assign work very differently. At some salons, workers essentially operate as miniature salons with little task specialization. At others, workers play a specific role as part of a task specialized team. Further, task specialization is positively associated with firm performance. Specialized salons earn more revenue per minute, are larger, and set higher prices. At least for hair salons, there is evidence that organizational capability plays a large role in economic outcomes.

He then builds a new model where competing firms with different organizational capabilities choose both who to hire and how to assign tasks. Firms strategically design the jobs of each employee based on individual skills, prevailing wages, consumer demand for quality, and importantly, a firm-specific organizational cost. He develops a procedure which uses machine learning to link the theory with the data, and estimate the firm’s organizational costs, worker skills and worker wages.

Using the estimated model, Professor Kohlhepp reexamines classic economic policies, using Manhattan’s hair salon industry as a laboratory. He starts with a minimum wage. When the minimum wage is increased from $15 to $20, firms which initially employ many minimum wage workers see a cost increase. All workers initially at these firms, minimum-wage or not, are disadvantaged. Internally, firms layoff minimum wage workers and the remaining workers pick up the slack by performing the leftover tasks. In this way, the minimum wage spills over onto non-minimum wage workers, generating wage increases for some and wage decreases for others that depend on the context.

He also considers a sales tax cut. When the sales tax decreases from 4.5% to 0%, workers flow towards more organizationally capable salons increasing productivity. In addition, cutting the sales tax makes producing a high quality product more profitable. This induces firms of all capabilities to increase specialization, which raises worker productivity across the market.

Both the minimum wage and sales tax cut tell a similar story: even firms which look similar on paper can be quite different in terms of their organization capabilities, and accounting for this changes what we predict even from classic economic policies.