• AI
• Tutorials
• AUTHOR: codevision AI engine
• DZone AI/ML

This article explores the ethical challenges of self-healing data pipelines and strategies to address them. Naveen Kolli explains how multi-agent generative AI frameworks can automate error correction while maintaining transparency and fairness. The piece highlights the risks of opaque systems, hidden biases, and autonomous conflicts in data workflows. Readers will learn to implement human checkpoints, audit trails, and governance agents to ensure ethical compliance. By integrating these practices, developers can build reliable and accountable AI-driven data systems.

Key facts

• Self-healing data pipelines automate error detection and correction but raise ethical concerns about transparency and accountability.
• Multi-agent AI frameworks use LLMs to optimize data workflows, yet lack explainability can lead to untraceable data alterations.
• Hidden biases in AI systems risk distorting data processing, such as unfairly excluding rural regions from sales analysis.
• Governance agents and version control tools like Delta Lake help enforce ethical policies and track data changes.
• Human oversight mechanisms, including manual checkpoints and audit trails, are critical for maintaining trust in automated systems.