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Make Work Fair: Data-Driven Design for Real Results

Make Work Fair: Data-Driven Design for Real Results

by Iris Bohnet (Author), Siri Chilazi (Author) Format: Kindle Edition
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Two leading gender experts and Harvard researchers reveal a new paradigm for fairness at work and offer professionals at every level, in any kind of organization, immediate, proven, and evidence-based ways to do their everyday work better and smarter—and more fairly.To make organizations more fair, many well-meaning individuals and companies invest their time and resources in diversity, equity, and inclusion (DEI) initiatives. But because inequity is built into the structures, processes, and environments of our workplaces, adding these programs has been ineffective and often becomes a burden passed off to the individuals they are meant to help.InMake Work Fair, behavioral scientist and author of What Works Iris Bohnet and gender expert Siri Chilazi offer data-backed, actionable solutions that build fairness into the very fabric of the workplace. Their methods—tested at many organizations, and grounded in data proven to work in the real world—help us make fairer, and simply better, decisions. Using their three-part framework, employees at all levels can embed fairness into their everyday practices.Believing in equal opportunity is essential—but it isn’t enough. Offering an evidence-based blueprint, Make Work Fair shows you how to make it a reality, no matter your role, seniority, responsibilities, or where you are in the world. Read more

Product Information

ASINB0D3CBQSN7
PublisherHarper Business
AccessibilityLearn more
Publication dateJanuary 28, 2025
LanguageEnglish
File size3.0 MB
Screen ReaderSupported
Enhanced typesettingEnabled
X-RayEnabled
Word WiseEnabled
Print length382 pages
ISBN-13978-0063374409
Page FlipEnabled
Best Sellers Rank#123,585 in Kindle Store (See Top 100 in Kindle Store) #10 in Business Ethics (Kindle Store) #11 in Organizational Learning #25 in Business Ethics (Books)
Customer Reviews4.3 4.3 out of 5 stars 19 ratings

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