FAIRTOPIA: Envisioning Multi-Agent Guardianship for Disrupting Unfair AI Pipelines

TL;DR

FAIRTOPIA proposes a multi-agent framework to embed adaptive fairness guardianship throughout AI pipelines, aiming to systematically address AI unfairness with human-centric, flexible, and self-refining mechanisms.

Contribution

It introduces a novel multi-layered, agent-based architecture for continuous fairness monitoring and intervention across all AI development stages.

Findings

Framework enables fairness watch in all pipeline stages

Multi-agent workflows foster systematic fairness improvements

Positioning AI fairness as a human-centric, interdisciplinary challenge

Abstract

AI models have become active decision makers, often acting without human supervision. The rapid advancement of AI technology has already caused harmful incidents that have hurt individuals and societies and AI unfairness in heavily criticized. It is urgent to disrupt AI pipelines which largely neglect human principles and focus on computational biases exploration at the data (pre), model(in), and deployment (post) processing stages. We claim that by exploiting the advances of agents technology, we will introduce cautious, prompt, and ongoing fairness watch schemes, under realistic, systematic, and human-centric fairness expectations. We envision agents as fairness guardians, since agents learn from their environment, adapt to new information, and solve complex problems by interacting with external tools and other systems. To set the proper fairness guardrails in the overall AI pipeline, we introduce a fairness-by-design approach which embeds multi-role agents in an end-to-end (human to AI) synergetic scheme. Our position is that we may design adaptive and realistic AI fairness frameworks, and we introduce a generalized algorithm which can be customized to the requirements and goals of each AI decision making scenario. Our proposed, so called FAIRTOPIA framework, is structured over a three-layered architecture, which encapsulates the AI pipeline inside an agentic guardian and a knowledge-based, self-refining layered scheme. Based on our proposition, we enact fairness watch in all of the AI pipeline stages, under robust multi-agent workflows, which will inspire new fairness research hypothesis, heuristics, and methods grounded in human-centric, systematic, interdisciplinary, socio-technical principles.