Algorithmic Governance for Explainability: A Comparative Overview of Progress and Trends

TL;DR

This paper provides a comparative overview of progress and trends in algorithmic governance for explainability, emphasizing its importance, current challenges, and the need for multi-stakeholder collaboration to develop effective regulatory frameworks.

Contribution

It offers a comprehensive overview of the current state, challenges, and future directions of explainability in AI within the context of algorithmic governance.

Findings

Explainability is crucial for trust and security in AI systems.

Current efforts involve multiple stakeholders including public sector and industry.

XAI remains in early development with ongoing need for collaborative regulation.

Abstract

The explainability of AI has transformed from a purely technical issue to a complex issue closely related to algorithmic governance and algorithmic security. The lack of explainable AI (XAI) brings adverse effects that can cross all economic classes and national borders. Despite efforts in governance, technical, and policy exchange have been made in XAI by multiple stakeholders, including the public sector, enterprises, and international organizations, respectively. XAI is still in its infancy. Future applications and corresponding regulatory instruments are still dependent on the collaborative engagement of all parties.