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)
Part 1: Bigot in the Machine – A Primer
Part 2: Enterprise Risk Observability Model Governance
Part 3: Explainable AI in Action

Policing and surveillance

Predictive policing is an object of major concern where police departments can predict hotspots for future crime, Minority Report-style. The consequences? Over-policing the neighborhoods of people of color, essentially exacerbating the existing situation.33

There is no narrative of AI gone bad that can be complete without mentioning Correctional Offender Management Profiling for Alternative Sanctions (COMPAS). A ProPublica report34 analyzed the risk assessment algorithm, which predicts the risk of recidivism, and found it to be biased against black people35. There has been a lot written about automated decision-making in predictive policing and sentencing since COMPAS, but using computer vision-style surveillance approaches to determine criminality36 continues in one form or another. The Department of Homeland Security is using37 a terrorist-predicting algorithm that utilizes features such as age, address, destination and/or transit airports, trip information...