Regulating AI: do we need new tools?

TL;DR

This paper examines the implicit data exchange in AI-driven digital services, revealing market inefficiencies and proposing the need for new regulatory tools beyond current transparency policies.

Contribution

It provides empirical evidence on how implicit data exchanges affect market dynamics and argues for innovative regulatory approaches to address market failures.

Findings

Consumers and providers are aware of data exchanges but it doesn't affect demand or prices.

Market indicators are ineffective due to the implicit nature of data exchange.

Current transparency policies may be biased and insufficient.

Abstract

The Artificial Intelligence paradigm (hereinafter referred to as "AI") builds on the analysis of data able, among other things, to snap pictures of the individuals' behaviors and preferences. Such data represent the most valuable currency in the digital ecosystem, where their value derives from their being a fundamental asset in order to train machines with a view to developing AI applications. In this environment, online providers attract users by offering them services for free and getting in exchange data generated right through the usage of such services. This swap, characterized by an implicit nature, constitutes the focus of the present paper, in the light of the disequilibria, as well as market failures, that it may bring about. We use mobile apps and the related permission system as an ideal environment to explore, via econometric tools, those issues. The results, stemming from a dataset of over one million observations, show that both buyers and sellers are aware that access to digital services implicitly implies an exchange of data, although this does not have a considerable impact neither on the level of downloads (demand), nor on the level of the prices (supply). In other words, the implicit nature of this exchange does not allow market indicators to work efficiently. We conclude that current policies (e.g. transparency rules) may be inherently biased and we put forward suggestions for a new approach.