A Decentralized Approach towards Responsible AI in Social Ecosystems

TL;DR

This paper advocates for a sociotechnical, decentralized infrastructure to promote Responsible AI by integrating social context and technical solutions, addressing structural issues beyond traditional AI research.

Contribution

It introduces a novel sociotechnical framework utilizing decentralized infrastructure to align AI development with social responsibility goals.

Findings

Decentralized infrastructure can enhance Responsible AI deployment.

Human agency and regulation are key intervention mechanisms.

The approach addresses structural issues beyond narrow AI technical solutions.

Abstract

For AI technology to fulfill its full promises, we must have effective means to ensure Responsible AI behavior and curtail potential irresponsible use, e.g., in areas of privacy protection, human autonomy, robustness, and prevention of biases and discrimination in automated decision making. Recent literature in the field has identified serious shortcomings of narrow technology focused and formalism-oriented research and has proposed an interdisciplinary approach that brings the social context into the scope of study. In this paper, we take a sociotechnical approach to propose a more expansive framework of thinking about the Responsible AI challenges in both technical and social context. Effective solutions need to bridge the gap between a technical system with the social system that it will be deployed to. To this end, we propose human agency and regulation as main mechanisms of intervention and propose a decentralized computational infrastructure, or a set of public utilities, as the computational means to bridge this gap. A decentralized infrastructure is uniquely suited for meeting this challenge and enable technical solutions and social institutions in a mutually reinforcing dynamic to achieve Responsible AI goals. Our approach is novel in its sociotechnical approach and its aim in tackling the structural issues that cannot be solved within the narrow confines of AI technical research. We then explore possible features of the proposed infrastructure and discuss how it may help solve example problems recently studied in the field.