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

Bias in large language models (LLMS)

In a sensational revelation8 that sent shockwaves through the AI community, ChatGPT, powered by OpenAI’s formidable GPT-3 model, found itself embroiled in controversy for producing incorrect, biased, or downright inappropriate responses. This discovery ignited concerns about the model’s safety and the potential propagation of misinformation by an AI system intended to revolutionize human-machine interaction.

Natural language processing is a critical area in AI and machine learning, and studies show that LLMs can mimic subconscious human bias even when they are not actively presenting it. Word embedding is a popular natural language technique used to represent text data as vectors, which has been used in many machine learning and natural language processing tasks. The seminal paper Man is to Computer Programmer as Woman is to Homemaker?9 discusses debiasing word embeddings, and shows how natural language processing techniques can...