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

Fairness metrics

Fairness metrics are critical tools for ensuring that machine learning models are fair and unbiased. These measures allow for the evaluation of classification models and provide insights into whether certain groups are being unfairly favored or discriminated against. Demographic parity and equalized odds are two of the most widely used fairness metrics, both with their own unique approach to measuring fairness. By using these metrics, organizations can better understand how their models perform and take steps to address any biases that may exist.

Demographic parity

Demographic parity is a fairness metric that compares the predictions made between different groups, ignoring the actual true values. This metric is useful in cases where the input data is known to contain biases and the goal is to measure fairness. However, it is important to note that demographic parity only uses the predicted values and discards the information about the true values. It also uses...