The AI Fairness Diagnostic Kit

From Principle to Practice in No-Code AI Fairness Auditing
1. Auflage 2026
ISBN: 979-8-8688-2884-3
Verlag: APRESS L.P.

Buch, Englisch, 378 Seiten, Format (B × H): 155 mm x 235 mm

This book is a comprehensive, six-part guide that turns fairness in artificial intelligence from theory into an operational discipline. Drawing on validated metrics and seven domain modules like education, finance and housing, this book provides clear explanations, decision frameworks, professional workflows, and governance artefacts that enable non-technical stakeholders to interpret fairness risk, document accountability, and meet emerging regulatory expectations. Case studies—including hiring filters, and public-service eligibility systems—demonstrate real-world, high-stakes consequences of unfair AI and how accessible auditing can prevent them.

Written for a cross-disciplinary readership, this work connects public rights, professional responsibility, and regulatory mandates into a unified reference for fairness diagnostics. It offers a rigorous, repeatable framework for testing and improving AI accountability—supporting practitioners and affected communities alike in ensuring that intelligence remains both powerful and just. The book explains why fairness diagnostics are necessary, and maps ethical principles, human risks, and domain failures that demand practical testing. It introduces the diagnostic blueprint, fairness metric families and selection, governance and accountability roles, and the system architecture—including a universal baseline, sector-specific modules, and a no-code interface. We apply these methods across seven domains: Justice, Employment, Education, Finance, Health Settings, Business Services, and Public Governance—showing how to assess risks and run fairness audits in each high-impact context. It demonstrates clinical implementation, including end-to-end workflow examples, healthcare scenarios, and a complete case application. It explores scaling the toolkit across geographies and regulatory environments, aligning with MLOps/LLMOps ecosystems, meeting professional standards, and democratising fairness literacy. It also provides step-by-step audit workflows, interpretable reporting, fairness API libraries, case studies, exercises, self-assessment tools, and comprehensive reference tables for 89 fairness metrics and their domain applicability.

WHAT READERS WILL LEARN

• Understand where and how algorithmic unfairness emerges across sectors and populations.
• Translate ethical fairness principles into measurable and repeatable audit criteria.
• Select and apply appropriate fairness metrics from 89 validated measures, including group, individual, and causal methods.
• Run complete fairness audits through a no-code workflow, without programming or statistical expertise.
• Produce transparent governance artefacts, including audit reports, evidence packs, and escalation pathways..
• Support regulatory compliance and alignment with policies such as GDPR, the EU AI Act, and global standards frameworks.
• Build fairness literacy within organizations and among affected groups to democratize oversight.
• Embed fairness evaluation into procurement, deployment, and lifecycle governance of AI systems.

WHO THIS BOOK IS FOR

This book is for anyone affected by AI-driven decisions and anyone responsible for ensuring those decisions are fair. It is written for non-technical readers with no computer science background, including students, patients, consumers, job applicants, and citizens interacting with AI systems in everyday life.

Zielgruppe

Professional/practitioner

Autoren/Hrsg.

Tavakoli, Hamid

Fachgebiete

• Mathematik | Informatik EDV | Informatik Computerkommunikation & -vernetzung Netzwerksicherheit
• Mathematik | Informatik EDV | Informatik Technische Informatik Computersicherheit
• Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Software Engineering

Inhaltsverzeichnis

Part 1: Foundations.- Chapter 1: Why AI Fairness Needs a Diagnostic Toolkit.- Chapter 2: The Limits of Awareness: From Principles to Practice.- Chapter 3: The Human Stakes: Fairness Across Domains and Populations.- Chapter 4: Linking Principles to Practice: What FDK™ Adds.- Part 2: Designing the Fairness Diagnostic Kit (FDK™).- Chapter 5: Blueprint for a Diagnostic Approach to AI Fairness.- Chapter 6: Metrics and Measures: Translating Ethics into Testable Criteria.- Chapter 7: Fairness Metrics in Depth.- Chapter 8: Governance and Accountability: Who Tests, Who Decides - Chapter 9 FDK™ Architecture: Baseline, Domain Modules.- Part 3: Fairness Diagnostic Toolkit - Chapter 10: Justice Systems: Fairness in Policing and Sentencing Algorithms.- Chapter 11: Employment and Hiring: Transparency in Automated Decisions.- Chapter 12: Education and Access: Fairness in Learning Technologies.- Chapter 13:

Financial Services and Housing: Tackling Structural Inequities.- Chapter 14: Health Settings: Fairness in Clinical AI and Diagnostic Systems.- Chapter 15: Business Services: Fairness in Customer Analytics and Decision Support.- Chapter 16: Governance and Public Policy: Fairness in Decision Infrastructure.- Part 4: FDK In Practice.- Chapter 17: Building the Healthcare FDK™ Module.- Chapter 18: Clinical Case Scenarios.- Chapter 19: Clinical Application of FDK in Glaucoma.- Chapter 20: From Bias Detection to Bias Correction in Healthcare AI.- Part 5: Future Directions and Global Impact.- Chapter 21: Scaling FDK™ Across Domains and Geographies.- Chapter 22: Integrating FDK™ with Emerging AI Ecosystems.- Chapter 23: Policy, Regulation, and Professional Standards.- Chapter 24: Education, Literacy, and the Democratization of AI Fairness.- Chapter 25: Sustainability and the Road Ahead.- Part 6: Practical Implementation and Resources.- Chapter 26: Getting Started with the FDK™ API.- Chapter 27: The Metric Foundation of FDK™.- Chapter 28: Running and Interpreting Fairness Audits.- Chapter 29: Fairness API Libraries and Custom Queries.- Chapter 30: Case Studies, Exercises, and Self-Assessment. – Part 7: BiasClean™: Practical Bias Detection and Mitigation in Real-World Data. – Chapter 31: From Fairness Diagnosis to Data Remediation. - Chapter 32: What BiasClean™ Does. – Chapter 33: The BiasClean™ Pipeline Explained. – Chapter 34: Domain-Specific Weighting and Customization. – Chapter 35: Running BiasClean™ in Practice. – Chapter 36: Case Study: Cleaning Bias in the COMPAS Dataset. – Chapter 37: Ethical Boundaries, Risks, and Trade-offs. – Chapter 38: Integrating BiasClean™ into Organizational Workflows. – Chapter 39: Exercises and Applied Scenarios. – Chapter 40: From Tools to Stewardship: Completing the Journey Toward Fairer Decisions.

Über Autor(innen)

Hamid Tavakoli is a London-based Artificial Intelligence researcher specialising in algorithmic fairness, governance, and clinical decision systems. As Managing Director of Optics AI Ltd, he leads applied research on the impact of AI in healthcare and public-facing services, ensuring transparent, human-centred implementation.

His professional background spans AI development, data ethics, and regulatory-aligned diagnostics across health, education, housing, and employment sectors. He works closely with domain experts — clinicians, HR specialists, educators, and auditors — to ensure fairness becomes a measurable and operational aspect of AI adoption.

Hamid is also the author of the forthcoming Apress title  which equips non-specialists to leverage modern AI through practical natural-language design. His cross-disciplinary experience uniquely positions him to translate fairness theory into accessible, no-code practice for global professional communities.