Book Image

Responsible AI in the Enterprise

By : Adnan Masood, Heather Dawe
5 (1)
Book Image

Responsible AI in the Enterprise

5 (1)
By: Adnan Masood, Heather Dawe

Overview of this book

Responsible AI in the Enterprise is a comprehensive guide to implementing ethical, transparent, and compliant AI systems in an organization. With a focus on understanding key concepts of machine learning models, this book equips you with techniques and algorithms to tackle complex issues such as bias, fairness, and model governance. Throughout the book, you’ll gain an understanding of FairLearn and InterpretML, along with Google What-If Tool, ML Fairness Gym, IBM AI 360 Fairness tool, and Aequitas. You’ll uncover various aspects of responsible AI, including model interpretability, monitoring and management of model drift, and compliance recommendations. You’ll gain practical insights into using AI governance tools to ensure fairness, bias mitigation, explainability, privacy compliance, and privacy in an enterprise setting. Additionally, you’ll explore interpretability toolkits and fairness measures offered by major cloud AI providers like IBM, Amazon, Google, and Microsoft, while discovering how to use FairLearn for fairness assessment and bias mitigation. You’ll also learn to build explainable models using global and local feature summary, local surrogate model, Shapley values, anchors, and counterfactual explanations. By the end of this book, you’ll be well-equipped with tools and techniques to create transparent and accountable machine learning models.
Table of Contents (16 chapters)
1
Part 1: Bigot in the Machine – A Primer
4
Part 2: Enterprise Risk Observability Model Governance
9
Part 3: Explainable AI in Action

References and further reading

  1. Crafting acronyms is a linguistic diversion, where we contort and stretch language to its limits, squeezing out meaning while ensuring it fits snugly into a preconceived mold. Like a lexical gymnast, we bend and twist phrases until they submit to our desired abbreviation, allowing us to create memorable, yet sometimes tortured, linguistic souvenirs.
  2. AI STEPS FORWARD framework: rationale.ai
  3. https://github.com/heather-dawe/AI-STEPS-FORWARD-Common-Data-Model
  4. New York Regulator Probes UnitedHealth Algorithm for Racial Bias: https://www.wsj.com/articles/new-york-regulator-probes-unitedhealth-algorithm-for-racial-bias-11572087601
  5. Discrimination in Online Ad Delivery: https://arxiv.org/ftp/arxiv/papers/1301/1301.6822.pdf
  6. Why Your Board Needs a Plan for AI Oversight - MIT Sloan: https://sloanreview.mit.edu/article/why-your-board-needs-a-plan-for-ai-oversight/MIT
  7. Board Responsibility for Artificial Intelligence Oversight - Harvard...