Abstract
The deployment of machine learning models is expected to bring several benefits. Nevertheless, as a result of the complexity of the ecosystem in which models are generally trained and deployed, this technology also raises concerns regarding its (1) interpretability, (2) fairness, (3) safety, and (4) privacy. These issues can have substantial economic implications because they may hinder the development and mass adoption of machine learning. In light of this, the purpose of this paper was to determine, from a positive economics point of view, whether the free use of machine learning models maximizes aggregate social welfare or, alternatively, regulations are required. In cases in which restrictions should be enacted, policies are proposed. The adaptation of current tort and anti-discrimination laws is found to guarantee an optimal level of interpretability and fairness. Additionally, existing market solutions appear to incentivize machine learning operators to equip models with a degree of security and privacy that maximizes aggregate social welfare. These findings are expected to be valuable to inform the design of efficient public policies.
Highlights
The unforeseen economic implications of the four issues mentioned above can hinder the maximization of aggregate social welfare that machine learning has the potential to bring
In the context of this paper, we define social welfare as the total utility obtained from a given socioeconomic arrangement or, in other words, as the difference between the benefits and the costs accrued by all the economic agents
We provide an overview of the reasons behind the mass adoption of machine learning by both companies and public administrations, and we introduce the four main challenges that this technology faces today
Summary
Even though the technical sources and solutions to these challenges are being intensely studied [20,21,22,23,24,25,26,27,28,29], their economic implications have not been sufficiently examined. This may constitute a problem because these issues can become a barrier to the development and mass adoption of machine learning and, limit the value that this technology can bring to society. In the context of this paper, we define social welfare as the total utility obtained from a given socioeconomic arrangement or, in other words, as the difference between the benefits and the costs accrued by all the economic agents
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