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-related harms

There are a number of types of harm that can be caused by AI systems when their creators have failed to take fairness into account. Fairness-related harms refer to the various types of negative impacts that result from AI systems when fairness considerations are not properly addressed during their design and development. These can include unequal distribution of benefits and drawbacks, unequal QoS, and perpetuation of harmful stereotypes and biases. Also, AI harms overlap because systems often cause multiple forms of harm simultaneously.

Let’s now discuss some key negative consequences that may occur when fairness is not considered during the design and development of AI systems:

  • Allocation harms refer to the negative consequences that occur when AI systems provide or restrict access to opportunities, resources, or information. This leads to unequal treatment and prejudice against certain demographic groups, negatively impacting their ability...