Chapter 01. Trustworthy AI system¶
Artificial intelligence doesn’t just compute, it governs. It decides who gets hired, flagged, diagnosed, or ignored. As these systems move deeper into high-stakes environments, the question of trust becomes central: not just can we build intelligent machines, but can we trust what they decide, and why?
Trustworthy AI is about ensuring that AI systems are aligned with human values, accountable under law, and resilient in the real world. These are not soft aspirations, they are design requirements, ethical, legal, and technical, that determine whether AI earns public trust or triggers public harm.
In this chapter, we establish the foundation for what it means to build and evaluate AI that deserves our trust. We organize the discussion across five key sections:
- Section 1.1 asks the uncomfortable but necessary question: When AI causes harm, who is responsible?
- Section 1.2 introduces the three pillars of trustworthiness, ethical, legal, and stable, and how they must work together, not in isolation.
- Section 1.3 connects these pillars to global frameworks like the OECD AI Principles and the EU AI Act, which attempt to formalize trust into enforceable practice.
- Section 1.4 grounds trust in process, showing how it must be embedded at every phase of the AI development lifecycle.
- Section 1.5 offers structured case studies to analyze where trust fails, and what systemic breakdowns lead to real-world consequences.
The most catastrophic failures in AI are rarely caused by one bad model. They happen when fairness, legality, and stability are treated as afterthoughts.