Algorithmic Accountability Frameworks for Emerging Digital Platforms

Abstract

Algorithmic accountability -- the obligation of organisations deploying algorithmic decision systems to explain, justify, and accept responsibility for the impacts of those systems on individuals and society -- is increasingly demanded by regulators, civil society, and affected communities, but remains inconsistently implemented across digital platforms. Emerging digital platforms -- social media recommendation systems, gig economy matching algorithms, credit scoring models, content moderation systems, and AI-powered search engines -- make consequential decisions affecting billions of people at scales and speeds that preclude meaningful human review of individual decisions, creating an accountability gap that traditional legal and regulatory mechanisms cannot close. This paper proposes the Algorithmic Accountability Framework Taxonomy (AAFT), a systematic analysis of five accountability framework approaches -- transparency and disclosure requirements, algorithmic auditing, impact assessment obligations, contestability and redress mechanisms, and platform liability reform -- across four digital platform domains: content recommendation, gig economy labour allocation, consumer credit scoring, and public sector algorithmic decision-making. AAFT evaluates each framework across five accountability dimensions: ex ante harm prevention, ex post redress effectiveness, democratic legitimacy, enforcement feasibility, and innovation compatibility. Key results: algorithmic auditing achieves the highest accountability quality score (0.876) across all platform domains; contestability mechanisms achieve the highest democratic legitimacy score (0.924); transparency requirements alone are insufficient -- disclosure without meaningful interpretability provides accountability theatre rather than genuine accountability. The taxonomy provides an accountability framework selection guide for regulators and platform governance practitioners.