Measure group fairness, individual fairness and choose the right metric for different scenarios
Explain model’s logic using different explanation techniques
Mitigate bias at different stages of the machine learning pipeline
Description
As we incorporate the next wave of AI-enabled products in high-stakes decisions, we need some level of assurance of the safety that we have come to expect from everyday products. Continuing the progress of using AI in high-stakes decisions requires trusting AI-enabled solutions to deliver their promised benefits while protecting the public from harm. Questions about the security, safety, privacy, and fairness of AI-enabled decisions need to be answered as a condition for deploying AI solutions at scale. This book is a guide that will introduce you to key concepts, use cases, tools, and techniques of the emerging field of Responsible AI. We will cover hands-on coding techniques to identify and measure bias. Measuring bias is not enough: we also need to explain and fix our models. This book outlines how to do this throughout the machine learning pipeline. By the end of this book, you will have mastered Python coding techniques of explaining machine learning models’ logic, measuring their fairness at the individual and group levels and monitor them in production environments to detect degradation in their accuracy or fairness.
What you will learn
Explain the fundamental concepts of Responsible AI
Audit models machine learning models to ascertain their group and individual fairness outcomes
Apply explanatory techniques to gain insight into the inner logic of complex machine learning models
Alter the development of machine learning models using pre-processing, in-processing, and post-processing techniques to mitigate biased outcomes
Monitor machine learning models in production to identify drift and manage adverse impacts drift
Describe emerging trends in Responsible AI
Apply mitigation techniques to models, so that identified biases in models are remediated
Monitor models’ post-production launch degradation to ensure accuracy and fairness objectives are maintained over time
Serg Masís has been at the confluence of the internet, application development, and analytics for the last two decades. Currently, he's a Climate and Agronomic Data Scientist at Syngenta, a leading agribusiness company with a mission to improve global food security. Before that role, he co-founded a startup, incubated by Harvard Innovation Labs, that combined the power of cloud computing and machine learning with principles in decision-making science to expose users to new places and events. Whether it pertains to leisure activities, plant diseases, or customer lifetime value, Serg is passionate about providing the often-missing link between data and decision-making — and machine learning interpretation helps bridge this gap more robustly.
Dr. Ali El-Sharif is a professor teaching Data Analytics and Cybersecurity. Dr. El-Sharif earned his Ph.D. in Information Systems at Nova Southeastern University, focusing on Explainable Artificial Intelligence. He also received Information Security from Nova Southeastern University, a Master of Business Administration (MBA), and a Bachelor of Computer Engineering from Wright State University. Dr. El-Sharif transitioned to academia after 20 years of being an industry practitioner, holding roles including software developer, solution architect, project manager, and management consultant.
Nicholas Schmidt started working in the field of responsible AI through his twenty years of experience working at the intersection of statistics, law, and regulation. He is currently the CEO of SolasAI and the AI Practice Leader at BLDS, LLC. Before that, Nicholas studied at the University of Chicago. In his work at SolasAI, he leads a team of data scientists and engineers building software that assesses whether there is evidence of bias or discrimination in an algorithm, and then provides alternative models that are both fairer and highly predictive. Nicholas believes that algorithms can be used as a force for good, but that responsible governance is necessary for that to happen
A subscription provides you with full access to view all Packt and licnesed content online, this includes exclusive access to Early Access titles. Depending on the tier chosen you can also earn credits and discounts to use for owning content
How can I cancel my subscription?
To cancel your subscription with us simply go to the account page - found in the top right of the page or at https://subscription.packtpub.com/my-account/subscription - From here you will see the ‘cancel subscription’ button in the grey box with your subscription information in.
What are credits?
Credits can be earned from reading 40 section of any title within the payment cycle - a month starting from the day of subscription payment. You also earn a Credit every month if you subscribe to our annual or 18 month plans. Credits can be used to buy books DRM free, the same way that you would pay for a book. Your credits can be found in the subscription homepage - subscription.packtpub.com - clicking on ‘the my’ library dropdown and selecting ‘credits’.
What happens if an Early Access Course is cancelled?
Projects are rarely cancelled, but sometimes it's unavoidable. If an Early Access course is cancelled or excessively delayed, you can exchange your purchase for another course. For further details, please contact us here.
Where can I send feedback about an Early Access title?
If you have any feedback about the product you're reading, or Early Access in general, then please fill out a contact form here and we'll make sure the feedback gets to the right team.
Can I download the code files for Early Access titles?
We try to ensure that all books in Early Access have code available to use, download, and fork on GitHub. This helps us be more agile in the development of the book, and helps keep the often changing code base of new versions and new technologies as up to date as possible. Unfortunately, however, there will be rare cases when it is not possible for us to have downloadable code samples available until publication.
When we publish the book, the code files will also be available to download from the Packt website.
How accurate is the publication date?
The publication date is as accurate as we can be at any point in the project. Unfortunately, delays can happen. Often those delays are out of our control, such as changes to the technology code base or delays in the tech release. We do our best to give you an accurate estimate of the publication date at any given time, and as more chapters are delivered, the more accurate the delivery date will become.
How will I know when new chapters are ready?
We'll let you know every time there has been an update to a course that you've bought in Early Access. You'll get an email to let you know there has been a new chapter, or a change to a previous chapter. The new chapters are automatically added to your account, so you can also check back there any time you're ready and download or read them online.
I am a Packt subscriber, do I get Early Access?
Yes, all Early Access content is fully available through your subscription. You will need to have a paid for or active trial subscription in order to access all titles.
How is Early Access delivered?
Early Access is currently only available as a PDF or through our online reader. As we make changes or add new chapters, the files in your Packt account will be updated so you can download them again or view them online immediately.
How do I buy Early Access content?
Early Access is a way of us getting our content to you quicker, but the method of buying the Early Access course is still the same. Just find the course you want to buy, go through the check-out steps, and you’ll get a confirmation email from us with information and a link to the relevant Early Access courses.
What is Early Access?
Keeping up to date with the latest technology is difficult; new versions, new frameworks, new techniques. This feature gives you a head-start to our content, as it's being created. With Early Access you'll receive each chapter as it's written, and get regular updates throughout the product's development, as well as the final course as soon as it's ready.We created Early Access as a means of giving you the information you need, as soon as it's available. As we go through the process of developing a course, 99% of it can be ready but we can't publish until that last 1% falls in to place. Early Access helps to unlock the potential of our content early, to help you start your learning when you need it most. You not only get access to every chapter as it's delivered, edited, and updated, but you'll also get the finalized, DRM-free product to download in any format you want when it's published. As a member of Packt, you'll also be eligible for our exclusive offers, including a free course every day, and discounts on new and popular titles.
United States
United Kingdom
India
Germany
France
Canada
Russia
Spain
Brazil
Australia
Argentina
Austria
Belgium
Bulgaria
Chile
Colombia
Cyprus
Czechia
Denmark
Ecuador
Egypt
Estonia
Finland
Greece
Hungary
Indonesia
Ireland
Italy
Japan
Latvia
Lithuania
Luxembourg
Malaysia
Malta
Mexico
Netherlands
New Zealand
Norway
Philippines
Poland
Portugal
Romania
Singapore
Slovakia
Slovenia
South Africa
South Korea
Sweden
Switzerland
Taiwan
Thailand
Turkey
Ukraine
