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

AWS Sagemaker Clarify

Amazon Web Services (AWS) offers a comprehensive AI platform, which has evolved from a simple data science notebook offering into a competitive solution on the mainstream AI platform market, alongside its cloud rivals, Azure and GCP. AWS offers a comprehensive suite of AI offerings that enable developers to build, train, and deploy ML models quickly and easily. These offerings include Amazon SageMaker, a fully managed service that provides end-to-end ML workflows, Amazon Rekognition, a deep-learning-based image and video analysis service, Amazon Comprehend, a natural language processing service, and Amazon Lex, a service for building conversational interfaces using voice and text. AWS also provides several pre-built AI services, including text-to-speech, speech-to-text, language translation, and sentiment analysis.

Amazon SageMaker Clarify4 is part of this comprehensive suite of services and helps ensure the accuracy, transparency, and fairness of ML models...