AI-driven systems have broad social and economic impacts and demonstrably exacerbate structural discrimination and inequalities. For the most part, regulators have responded by narrowly focusing on the technocentric solution of debiasing algorithms and datasets. By doing so, regulators risk creating a bigger problem for both AI governance and democracy because this narrow approach squeezes complex socio-technical problems into the domain of design and thus into the hands of technology companies. By largely ignoring the costly production environments that machine learning requires, regulators condone an expansionist model of computational infrastructures (clouds, mobile phones, and sensor networks) driven by Big Tech. Effective solutions require bold regulations that target the root of power imbalances inherent to the pervasive deployment of AI- driven systems.
Description:
AI-driven systems have broad social and economic impacts and demonstrably exacerbate structural discrimination and inequalities. For the most part, regulators have responded by narrowly focusing on the technocentric solution of debiasing
algorithms and datasets. By doing so, regulators risk creating a bigger problem for both AI governance and democracy because this narrow approach squeezes complex socio-technical problems into the domain of design and thus into the hands of technology companies. By largely ignoring the costly production environments that machine learning requires, regulators condone an expansionist model of computational infrastructures (clouds, mobile phones, and sensor networks) driven by Big Tech. Effective solutions require bold regulations that target the root of power imbalances inherent to the pervasive deployment of AI-
driven systems.