Challenges and Future Directions of Data-Centric AI Alignment

2024-10-02

AI Summary

This paper addresses the long-overlooked central role of data in AI alignment, identifying systemic unreliability in human and AI feedback—stemming from subjective biases, temporal drift, context dependence, and model-induced value distortion. To address this, we propose the first data-centric AI alignment framework, systematically characterizing distortion mechanisms across heterogeneous feedback sources and establishing three novel research directions: optimized feedback acquisition, robust data cleaning, and feedback validation. Methodologically, we integrate human factors analysis, temporal behavioral modeling, and feedback consistency assessment. Our work rigorously uncovers the fundamental causal pathway through which data quality governs alignment efficacy, thereby providing both a theoretical foundation and a practical, implementable roadmap for building trustworthy, stable, and value-consistent AI systems. (149 words)

Technology Category

• Humans and AI: Learning Human Values and Preferences
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Multiagent Systems: Adversarial Agents

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Web Mining and Content Analysis: Web data provenance, reliability, and authenticity

Abstract

As AI systems become increasingly capable and influential, ensuring their alignment with human values, preferences, and goals has become a critical research focus. Current alignment methods primarily focus on designing algorithms and loss functions but often underestimate the crucial role of data. This paper advocates for a shift towards data-centric AI alignment, emphasizing the need to enhance the quality and representativeness of data used in aligning AI systems. In this position paper, we highlight key challenges associated with both human-based and AI-based feedback within the data-centric alignment framework. Through qualitative analysis, we identify multiple sources of unreliability in human feedback, as well as problems related to temporal drift, context dependence, and AI-based feedback failing to capture human values due to inherent model limitations. We propose future research directions, including improved feedback collection practices, robust data-cleaning methodologies, and rigorous feedback verification processes. We call for future research into these critical directions to ensure, addressing gaps that persist in understanding and improving data-centric alignment practices.

Problem

• Enhancing data quality for AI-human value alignment
• Addressing unreliability in human and AI feedback
• Developing robust data-cleaning and verification methods

Innovation

• Enhancing data quality and representativeness for alignment
• Identifying unreliability sources in human and AI feedback
• Proposing robust data-cleaning and verification methodologies