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

Enterprise use of foundation models and bias remediation

It is crucial for companies to avoid using biased models in various industries, as this can result in harm, unethical decisions, and legal and reputational risks. Biases can be introduced in AI models at different stages of the ML pipeline, including data collection, preprocessing, model training, and evaluation. To ensure that foundation models are suitable for enterprise use, companies can take various measures, such as implementing fair data practices, including diverse representation in data, regular model monitoring, and audit trails. Fine-tuning and customizing foundation models to specific use cases through prompt engineering can reduce bias and improve accuracy. Moreover, companies should adopt responsible AI principles and undergo ongoing education and training to ensure the ethical and unbiased deployment of AI systems.

To reduce bias in foundation models, there are several techniques and approaches that can be used...