Zero Knowledge Proofs Enabling Ethical AI and Privacy-Preserving Verification in Distributed Systems

Abstract

The evolving technology in AI and distributed systems requires ethical concepts of how sensitive data can be verified without breach of privacy. Conventional AI systems present the following critical concerns: exposure of data, breach of privacy, and ethical issues concerning transparent but confidential computation. This chapter is a full-fledged cryptographic proof, Zero-Knowledge Proofs (ZKPs), which makes it possible to deploy AI ethically by verifying privacy. The framework is supported by mathematical underpinnings to enable model validation and training verification, as well as federated learning without the underlying datasets or parameters of the models. The chapter shows that ZKPs can be used to meet ethical AI without compromising privacy. It can be used in healthcare, finance, and voting systems where ethical concerns require verification and confidentiality. This chapter offers a new method of dealing with core ethical dilemmas in AI systems and safeguarding privacy and security in algorithmic decision-making exercises.