From MNA Press · A professional reference for AI Assurance

AI × HI: The Age of AI Assurance

Name: AI × HI: The Age of AI Assurance
Author: Chenyi Ang
ISBN: 9786299306702

An evidence-centred approach to understanding when institutional reliance on artificial intelligence is justified.

By Chenyi Ang

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Hardcover · USD 36.99 · ISBN 978-629-93067-0-2

Front cover of AI × HI: The Age of AI Assurance by Chenyi Ang, featuring the approved blue-and-bronze Intersection sculpture

The Age of AI Assurance · MNA Press

01 / The discipline

What is AI Assurance?

AI Assurance is the evidence-centred discipline concerned with whether confidence in an AI system is justified for a defined use, context and period.

Organisations already have governance policies, controls, standards, testing and risk frameworks. Those are essential foundations, but they do not automatically answer whether an AI system can be relied upon.

The assurance question is sharper: What evidence is sufficient to justify reliance on an AI system in a particular context? The book develops that question into a structured professional discipline.

Why AI × HI?

The multiplication sign expresses a relationship of dependence between Artificial Intelligence and Human Intelligence.

AI can increasingly generate, challenge and synthesise evidence. Yet evidence does not authorise its own institutional use. Human Intelligence brings the competent judgement, critical scrutiny and accountability needed to determine what that evidence justifies.

AI × HI examines how these capabilities contribute to AI Assurance, where confidence must be supported by relevant evidence, interpreted responsibly and continually reassessed as circumstances change.

02 / Distinct, complementary roles

Why governance alone is not assurance.

Governance

States expectations.

Policies, accountabilities, control objectives, standards and obligations give institutions a basis for responsible AI oversight.

Assurance

Examines the evidence.

Assurance asks what evidence justifies confidence that relevant expectations are being met in the specific context of reliance.

AI Assurance works alongside governance, risk management, safety testing, auditing, compliance and regulation. It does not displace them.

03 / Inside the book

From principles to justified institutional reliance.

AI × HI develops a connected body of concepts, drawing on established assurance traditions while addressing the particular challenges posed by AI.

AI Assurance

The evidence-centred discipline concerned with whether reliance on an AI system is justified.

AIAF

A structured assurance methodology—not a replacement for governance, regulation or established audit traditions.

Evidence & judgement

Assurance subjects, materiality, evidence quality and the professional judgement needed to form defensible opinions.

Assurance over time

Continuous assurance, Assurance Drift, Evidence Decay, Assurance Debt and the need to maintain assurance as systems change.

Agentic systems

Behavioural early-warning signals, autonomous systems and the challenges of justifying reliance in adaptive environments.

Institutional reliance

The roles of human intelligence, boards, oversight, technology risk and the emerging assurance economy.

04 / Readers

Written for people who have to decide.

Boards & executives

Clarify what evidence should support decisions to deploy, govern and rely on AI.

Risk, audit & assurance

Connect control expectations with evidence, assurance judgement and ongoing reliance.

AI & technology leaders

Consider assurance across the lifecycle, including model change and agentic behaviour.

Regulators & policymakers

Examine how assurance methodology can work alongside jurisdiction-specific criteria.

Academia & professional education

Engage with an emerging discipline at the intersection of technology, risk and assurance.

For those who must know not only whether AI works, but whether it can be responsibly relied upon.

05 / The central proposition

Evidence before confidence.

“Capability does not create confidence. Evidence does.”

06 / The author

Chenyi Ang

Chenyi Ang is a technology entrepreneur and practitioner in AI assurance, AI governance and enterprise intelligence. He is the founder of MNA Ventures, Chairman of the Chartered Software Developer Association (CSDA), and developer of the AI Assurance Framework (AIAF). His cross-disciplinary background spans economics, law and professional risk management.

He is the named inventor on an international patent application published through the World Intellectual Property Organization (WIPO) relating to autonomous AI red teaming using generative adversarial networks and reinforcement learning. His work focuses on the evidence required for institutions to rely responsibly on artificial intelligence.

Amazon author page

07 / Publication