Home > Computer & Internet > Computer programming / software development > Algorithms & data structures > Uncovering Bias in Machine Learning: A Guide to Im plementing Interpretable Models: A Guide to Implementing Interpretable Models
29%
Uncovering Bias in Machine Learning: A Guide to Im plementing Interpretable Models: A Guide to Implementing Interpretable Models

Uncovering Bias in Machine Learning: A Guide to Im plementing Interpretable Models: A Guide to Implementing Interpretable Models

          
5
4
3
2
1

Out of Stock


Premium quality
Premium quality
Bookswagon upholds the quality by delivering untarnished books. Quality, services and satisfaction are everything for us!
Easy Return
Easy return
Not satisfied with this product! Keep it in original condition and packaging to avail easy return policy.
Certified product
Certified product
First impression is the last impression! Address the book’s certification page, ISBN, publisher’s name, copyright page and print quality.
Secure Checkout
Secure checkout
Security at its finest! Login, browse, purchase and pay, every step is safe and secured.
Money back guarantee
Money-back guarantee:
It’s all about customers! For any kind of bad experience with the product, get your actual amount back after returning the product.
On time delivery
On-time delivery
At your doorstep on time! Get this book delivered without any delay.
Notify me when this book is in stock
Add to Wishlist

About the Book

With machine learning systems becoming more ubiquitous in automated decision making, it is crucial that we make these systems sensitive to the type of bias that results in discrimination, especially discrimination on illegal grounds. Machine learning is already being used to make or assist decisions in the following domains of Recruiting (Screening job applicants), Banking (Credit ratings/Loan approvals), Judiciary (Recidivism risk assessments), Welfare (Welfare Benefit Eligibility), Journalism (News Recommender Systems) etc. Given the scale and impact of these industries, it is crucial that we take measures to prevent unfair discrimination in them via legal as well as technical means.

This book will give data scientists and Machine learning engineers insight on how building machine learning models and algorithms can negatively impact users. The book will also provide tools and code examples to help document, identify, and mitigate different types of machine bias. The audience are Data Scientists, Machine Learning Engineers, and Researchers who implement and productionalize machine learning models. This book has been needed for decades because it not only helps the reader understand how human bias slips into models but gives them code and techniques to analyze the models they've already built. This book will also give engineers the tools to push back on demands from management that result in harmful models.

While this book will focus on machine learning that is used to predict data about users that can be impactful on their lives. Thousands of consumer products use machine learning and these algorithms can cause major damage if influenced by biased data. Google has already classified black people as "gorillas" in Google Photos. Some facial recognition doesn't even pick up darker toned skin. In terms of trends, ML and AI are by far the hottest fields in computing. The problem with this high-paying, high-growth area is that few practitioners are actually skilled in reducing and mitigating harm caused to users. This book will allow Data Scientists, Machine Learning Engineers, Software Developers, and Researchers alike to apply these explainability steps to their system.


Best Sellers



Product Details
  • ISBN-13: 9781119763147
  • Publisher: Wiley
  • Publisher Imprint: Wiley
  • Height: 0 mm
  • No of Pages: 300
  • Spine Width: 0 mm
  • Width: 0 mm
  • ISBN-10: 1119763142
  • Publisher Date: 05 Oct 2021
  • Binding: Paperback
  • Language: English
  • Returnable: Y
  • Weight: 110 gr


Similar Products

How would you rate your experience shopping for books on Bookswagon?

Add Photo
Add Photo

Customer Reviews

REVIEWS           
Click Here To Be The First to Review this Product
Uncovering Bias in Machine Learning: A Guide to Im plementing Interpretable Models: A Guide to Implementing Interpretable Models
Wiley -
Uncovering Bias in Machine Learning: A Guide to Im plementing Interpretable Models: A Guide to Implementing Interpretable Models
Writing guidlines
We want to publish your review, so please:
  • keep your review on the product. Review's that defame author's character will be rejected.
  • Keep your review focused on the product.
  • Avoid writing about customer service. contact us instead if you have issue requiring immediate attention.
  • Refrain from mentioning competitors or the specific price you paid for the product.
  • Do not include any personally identifiable information, such as full names.

Uncovering Bias in Machine Learning: A Guide to Im plementing Interpretable Models: A Guide to Implementing Interpretable Models

Required fields are marked with *

Review Title*
Review
    Add Photo Add up to 6 photos
    Would you recommend this product to a friend?
    Tag this Book
    Read more
    Does your review contain spoilers?
    What type of reader best describes you?
    I agree to the terms & conditions
    You may receive emails regarding this submission. Any emails will include the ability to opt-out of future communications.

    CUSTOMER RATINGS AND REVIEWS AND QUESTIONS AND ANSWERS TERMS OF USE

    These Terms of Use govern your conduct associated with the Customer Ratings and Reviews and/or Questions and Answers service offered by Bookswagon (the "CRR Service").


    By submitting any content to Bookswagon, you guarantee that:
    • You are the sole author and owner of the intellectual property rights in the content;
    • All "moral rights" that you may have in such content have been voluntarily waived by you;
    • All content that you post is accurate;
    • You are at least 13 years old;
    • Use of the content you supply does not violate these Terms of Use and will not cause injury to any person or entity.
    You further agree that you may not submit any content:
    • That is known by you to be false, inaccurate or misleading;
    • That infringes any third party's copyright, patent, trademark, trade secret or other proprietary rights or rights of publicity or privacy;
    • That violates any law, statute, ordinance or regulation (including, but not limited to, those governing, consumer protection, unfair competition, anti-discrimination or false advertising);
    • That is, or may reasonably be considered to be, defamatory, libelous, hateful, racially or religiously biased or offensive, unlawfully threatening or unlawfully harassing to any individual, partnership or corporation;
    • For which you were compensated or granted any consideration by any unapproved third party;
    • That includes any information that references other websites, addresses, email addresses, contact information or phone numbers;
    • That contains any computer viruses, worms or other potentially damaging computer programs or files.
    You agree to indemnify and hold Bookswagon (and its officers, directors, agents, subsidiaries, joint ventures, employees and third-party service providers, including but not limited to Bazaarvoice, Inc.), harmless from all claims, demands, and damages (actual and consequential) of every kind and nature, known and unknown including reasonable attorneys' fees, arising out of a breach of your representations and warranties set forth above, or your violation of any law or the rights of a third party.


    For any content that you submit, you grant Bookswagon a perpetual, irrevocable, royalty-free, transferable right and license to use, copy, modify, delete in its entirety, adapt, publish, translate, create derivative works from and/or sell, transfer, and/or distribute such content and/or incorporate such content into any form, medium or technology throughout the world without compensation to you. Additionally,  Bookswagon may transfer or share any personal information that you submit with its third-party service providers, including but not limited to Bazaarvoice, Inc. in accordance with  Privacy Policy


    All content that you submit may be used at Bookswagon's sole discretion. Bookswagon reserves the right to change, condense, withhold publication, remove or delete any content on Bookswagon's website that Bookswagon deems, in its sole discretion, to violate the content guidelines or any other provision of these Terms of Use.  Bookswagon does not guarantee that you will have any recourse through Bookswagon to edit or delete any content you have submitted. Ratings and written comments are generally posted within two to four business days. However, Bookswagon reserves the right to remove or to refuse to post any submission to the extent authorized by law. You acknowledge that you, not Bookswagon, are responsible for the contents of your submission. None of the content that you submit shall be subject to any obligation of confidence on the part of Bookswagon, its agents, subsidiaries, affiliates, partners or third party service providers (including but not limited to Bazaarvoice, Inc.)and their respective directors, officers and employees.

    Accept

    New Arrivals



    Inspired by your browsing history


    Your review has been submitted!

    You've already reviewed this product!