chapter six

6 Ethical and Trustworthy GenAI

 

This chapter covers

  • Why trust in GenAI requires more than privacy and security safeguards.
  • Common risks such as hallucinations, bias, and overreliance
  • What transparency and explainability look like in black-box models.
  • Techniques to mitigate safety, toxicity, and offensive outputs.
  • Intellectual property concerns across usage, training, and vendor relationships.
  • How agentic and multi agent deployments amplify these risks and introduce new ones.

We have spent the previous chapters working through security and privacy: how to protect data, how to control vendors, and how to keep sensitive information from leaking into places it does not belong. This chapter shows their limit: a GenAI system can be well secured, fully privacy preserving, and still cause harm.

6.1 Accuracy & Reliability

6.1.1 Truthfulness & Hallucinations

6.1.2 Robustness

6.1.3 Consistency of Answers

6.2 Bias and Fairness

6.2.1 Real-world incidents

6.2.2 What “fair” actually means

6.2.3 A practical bias-testing loop for LLM-based systems

6.2.4 Bias controls

6.3 Overreliance

6.3.1 Overreliance Controls

6.4 Transparency and Explainability

6.4.1 Transparency controls

6.5 Safety and Toxicity

6.5.1 Safety Controls

6.5.2 Red Teaming in Practice

6.6 IP and Copyrighted Material

6.6.1 Provenance & license Risk

6.6.2 During Use: Sharing IP and Legal Protections

6.6.3 After Release: Outputs, Leakage, and Distilled Models

6.7 Ethics in Agentic and Multi-Agent Deployments

6.7.1 Agentic AI controls

6.8 Summary