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Make Work Fair: Data-Driven Design for Real Results – The Harvard Experts' Blueprint: Actionable Practices for Equity and Inclusion in Organizations
Harvard Kennedy School behavioral economist and author of What Works joins forces with a Harvard Kennedy School researcher to offer professionals at every level, in any kind of organization, immediate, proven, and evidence-based ways to create and sustain equity in their everyday business practices.
To make their organizations more equitable, 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.
In Make Work Fair, Iris Bohnet and 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 execute and embed equity 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.
To make their organizations more equitable, 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.
In Make Work Fair, Iris Bohnet and 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 execute and embed equity 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.
382 pages, Kindle Edition
Published January 28, 2025
42 people are currently reading
2457 people want to read
About the author
Iris Bohnet
7 books43 followersIris Bohnet, Professor of Public Policy, is a behavioral economist at Harvard Kennedy School, combining insights from economics and psychology to improve decision-making in organizations and society, often with a gender or cross-cultural perspective. She is the author of What Works: Gender Equality by Design, published by Harvard University Press in 2016. Her most recent research examines behavioral design to de-bias how we live, learn and work. Professor Bohnet served as the academic dean of the Kennedy School, is the director of its Women and Public Policy Program, the co-chair (with Max Bazerman) of the Behavioral Insights Group, an associate director of the Harvard Decision Science Laboratory, and the faculty chair of the executive program “Global Leadership and Public Policy for the 21st Century” for the World Economic Forum’s Young Global Leaders. She serves on the boards of directors of Credit Suisse Group and University of Lucerne, as well as the advisory boards of the Vienna University of Economics and Business, EDGE and Applied, as well as numerous academic journals. She is a member of the Global Agenda Council on Behavior of the World Economic Forum.
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Displaying 1 - 12 of 12 reviews
March 21, 2025
Despite the book description, I found much of this book’s advice and insights to be lacking applicability “at all levels”. Instead, there are definitely data-driven research findings that decision-makers and corporate leaders can (and should) follow.
It was also difficult to remain bought-in to the concepts being conveyed with companies like JPMorgan and McKinsey as the examples. I’m not of a mind that these should’ve been replaced by role models in the space, but it’s a tough sell for me to look at companies so distinctly known for their prioritization of profits over people and buy into whatever they’re doing to bring fairness and equity to the workplace.
It was well-cited and -researched, though, and made use of some other salient examples where it could. Probably better suited for middle- and upper-management at mid- to large-size companies.
(Edited to correct a typo)
It was also difficult to remain bought-in to the concepts being conveyed with companies like JPMorgan and McKinsey as the examples. I’m not of a mind that these should’ve been replaced by role models in the space, but it’s a tough sell for me to look at companies so distinctly known for their prioritization of profits over people and buy into whatever they’re doing to bring fairness and equity to the workplace.
It was well-cited and -researched, though, and made use of some other salient examples where it could. Probably better suited for middle- and upper-management at mid- to large-size companies.
(Edited to correct a typo)
Read
January 31, 2026"Make Work Fair: Data-Driven Design for Real Results" by Iris Bohnet and Siri Chilazi examines why so many workplaces remain unfair despite good intentions, and why lasting change depends less on changing people’s attitudes and more on redesigning the systems they work within. The book begins by showing how inequality often emerges not from malice, but from narrow assumptions baked into everyday design decisions. Technologies, policies, and norms are frequently created around a limited idea of what is 'normal,' which quietly excludes large portions of the population. By focusing on data, design, and accountability rather than awareness alone, Bohnet and Chilazi argue that fairness can become a reliable outcome of how work is structured, not a fragile aspiration dependent on individual goodwill.
Many examples reveal how unfairness is often invisible to those who benefit from existing systems. Early voice recognition software struggled to understand women, people with nonstandard accents, and people of color because it was trained mainly on white male voices. Car safety systems long relied on crash test dummies modeled after an average male body, leading to higher injury and death rates for women and children. These outcomes were not the result of hostile intent, but of designers failing to question whose bodies, voices, and experiences were treated as the default. The same pattern appears in workplaces, where career paths assume uninterrupted availability, long hours, and physical presence, advantages that favor certain workers while disadvantaging caregivers, people with disabilities, and those with different working styles. Systems feel neutral until you are the one running into their limits.
The book emphasizes that organizations often misunderstand the problem of fairness by framing it as an issue of individual bias. As a result, they rely heavily on diversity training, awareness campaigns, or moral appeals, hoping that people will think and behave differently. Research shows that these approaches have limited and often temporary effects. Human biases are deeply ingrained and difficult to override in fast-moving, high-pressure environments. Even well-intentioned people fall back on shortcuts and assumptions. Instead of asking people to become less biased, Bohnet and Chilazi propose a more effective strategy: redesign the environment so that fair outcomes are easier and unfair ones are harder.
Data plays a central role in this approach. Organizations frequently claim that fairness matters, yet fail to measure it with the same rigor applied to financial performance or productivity. When fairness is not tracked, leaders rely on assumptions that are often wrong. The book illustrates how even simple measurement can expose gaps between perception and reality. When a media team counted how many women and men appeared as experts on their program, they discovered a significant imbalance they had never noticed. Simply making the numbers visible led to rapid improvement. Measurement creates accountability, and accountability drives change.
Importantly, the authors stress that collecting data is not about blame but about learning. Aggregate data helps organizations identify where barriers exist and which groups are affected. More detailed analysis often reveals that what looks like a broad problem has a specific cause. In one case, higher turnover among women was traced not to gender in general, but to new mothers facing insufficient parental leave. Addressing that single factor closed the retention gap. Without data, organizations risk solving the wrong problem or reinforcing ineffective solutions. Even individuals without formal authority can begin tracking patterns within their own teams, such as who speaks in meetings, who gets high-visibility assignments, or who receives mentorship.
Another key idea in the book is that fairness must be embedded into everyday work rather than treated as a separate initiative. Many organizations respond to inequity by adding programs or committees on top of existing processes, which can make fairness feel like extra work or someone else’s responsibility. In contrast, system redesign involves changing how routine tasks are done. Small adjustments can have outsized effects. For example, altering resume formats to focus on skills and experience rather than uninterrupted timelines reduces penalties for career breaks. Removing unnecessary degree requirements from job postings expands applicant pools without lowering performance standards. These changes work because they eliminate barriers directly, rather than hoping people will consciously ignore them.
This approach also helps address the uneven burden of fairness work. Traditionally, responsibility for improving inclusion often falls on women and underrepresented groups, who are asked to serve on committees or educate others while still doing their regular jobs. Embedding fairness into core processes makes it everyone’s responsibility and part of normal operations. Designing fair meetings, evaluations, and hiring practices becomes as routine as managing budgets or deadlines. Fairness stops being an optional add-on and starts being a basic feature of how work is done.
Culture reinforces these systems. While policies matter, what people see modeled around them shapes what feels normal and possible. Visible role models in diverse roles help redefine what success looks like and who belongs. Seeing people who do not fit traditional stereotypes succeed provides concrete evidence that alternative paths are valued. Transparency also plays a powerful role. When data about representation, participation, or advancement is shared openly, it creates social accountability. People tend to act more thoughtfully when they know their decisions are visible to others.
The book highlights the importance of timely reminders that bring fairness into focus at the moment decisions are made. Simple prompts before hiring or evaluation decisions can reduce bias by making fairness salient when it matters most. Over time, repeated actions, shared data, and visible examples shape organizational culture. Fairness becomes less about intention and more about expectation, something people assume should be present rather than something they need to be persuaded to support.
Beyond moral considerations, the authors make a strong case that fairness improves organizational performance. Diverse teams make better decisions by challenging assumptions and reducing blind spots. Inclusive processes help organizations access a wider range of talent and perspectives. Removing unnecessary constraints allows more people to do their best work, which can increase productivity and innovation. Flexible work arrangements, for example, often improve output rather than diminish it by allowing employees to work in ways that suit their circumstances. Fair systems also build trust, reducing time and energy spent navigating politics or uncertainty, and increasing engagement and retention.
The overall message is that fairness and effectiveness are not opposing goals. Systems designed to work well for a broader range of people tend to work better overall. Data-driven decision-making, whether applied to equity or efficiency, leads to stronger results. When organizations rely on evidence rather than assumptions, they make smarter choices and waste fewer resources. Fairness, in this view, is not a constraint on performance but a driver of it.
In conclusion, "Make Work Fair: Data-Driven Design for Real Results" by Iris Bohnet and Siri Chilazi argues that creating fair workplaces requires a shift in focus from changing minds to changing systems. By measuring what matters, redesigning everyday processes to remove barriers, and making fairness visible through data, accountability, and role models, organizations can produce more equitable outcomes without relying on constant moral effort. These changes not only advance fairness but also lead to better decisions, higher productivity, and stronger performance. The book shows that when fairness is built into the design of work itself, it becomes sustainable, scalable, and beneficial for everyone involved.
Many examples reveal how unfairness is often invisible to those who benefit from existing systems. Early voice recognition software struggled to understand women, people with nonstandard accents, and people of color because it was trained mainly on white male voices. Car safety systems long relied on crash test dummies modeled after an average male body, leading to higher injury and death rates for women and children. These outcomes were not the result of hostile intent, but of designers failing to question whose bodies, voices, and experiences were treated as the default. The same pattern appears in workplaces, where career paths assume uninterrupted availability, long hours, and physical presence, advantages that favor certain workers while disadvantaging caregivers, people with disabilities, and those with different working styles. Systems feel neutral until you are the one running into their limits.
The book emphasizes that organizations often misunderstand the problem of fairness by framing it as an issue of individual bias. As a result, they rely heavily on diversity training, awareness campaigns, or moral appeals, hoping that people will think and behave differently. Research shows that these approaches have limited and often temporary effects. Human biases are deeply ingrained and difficult to override in fast-moving, high-pressure environments. Even well-intentioned people fall back on shortcuts and assumptions. Instead of asking people to become less biased, Bohnet and Chilazi propose a more effective strategy: redesign the environment so that fair outcomes are easier and unfair ones are harder.
Data plays a central role in this approach. Organizations frequently claim that fairness matters, yet fail to measure it with the same rigor applied to financial performance or productivity. When fairness is not tracked, leaders rely on assumptions that are often wrong. The book illustrates how even simple measurement can expose gaps between perception and reality. When a media team counted how many women and men appeared as experts on their program, they discovered a significant imbalance they had never noticed. Simply making the numbers visible led to rapid improvement. Measurement creates accountability, and accountability drives change.
Importantly, the authors stress that collecting data is not about blame but about learning. Aggregate data helps organizations identify where barriers exist and which groups are affected. More detailed analysis often reveals that what looks like a broad problem has a specific cause. In one case, higher turnover among women was traced not to gender in general, but to new mothers facing insufficient parental leave. Addressing that single factor closed the retention gap. Without data, organizations risk solving the wrong problem or reinforcing ineffective solutions. Even individuals without formal authority can begin tracking patterns within their own teams, such as who speaks in meetings, who gets high-visibility assignments, or who receives mentorship.
Another key idea in the book is that fairness must be embedded into everyday work rather than treated as a separate initiative. Many organizations respond to inequity by adding programs or committees on top of existing processes, which can make fairness feel like extra work or someone else’s responsibility. In contrast, system redesign involves changing how routine tasks are done. Small adjustments can have outsized effects. For example, altering resume formats to focus on skills and experience rather than uninterrupted timelines reduces penalties for career breaks. Removing unnecessary degree requirements from job postings expands applicant pools without lowering performance standards. These changes work because they eliminate barriers directly, rather than hoping people will consciously ignore them.
This approach also helps address the uneven burden of fairness work. Traditionally, responsibility for improving inclusion often falls on women and underrepresented groups, who are asked to serve on committees or educate others while still doing their regular jobs. Embedding fairness into core processes makes it everyone’s responsibility and part of normal operations. Designing fair meetings, evaluations, and hiring practices becomes as routine as managing budgets or deadlines. Fairness stops being an optional add-on and starts being a basic feature of how work is done.
Culture reinforces these systems. While policies matter, what people see modeled around them shapes what feels normal and possible. Visible role models in diverse roles help redefine what success looks like and who belongs. Seeing people who do not fit traditional stereotypes succeed provides concrete evidence that alternative paths are valued. Transparency also plays a powerful role. When data about representation, participation, or advancement is shared openly, it creates social accountability. People tend to act more thoughtfully when they know their decisions are visible to others.
The book highlights the importance of timely reminders that bring fairness into focus at the moment decisions are made. Simple prompts before hiring or evaluation decisions can reduce bias by making fairness salient when it matters most. Over time, repeated actions, shared data, and visible examples shape organizational culture. Fairness becomes less about intention and more about expectation, something people assume should be present rather than something they need to be persuaded to support.
Beyond moral considerations, the authors make a strong case that fairness improves organizational performance. Diverse teams make better decisions by challenging assumptions and reducing blind spots. Inclusive processes help organizations access a wider range of talent and perspectives. Removing unnecessary constraints allows more people to do their best work, which can increase productivity and innovation. Flexible work arrangements, for example, often improve output rather than diminish it by allowing employees to work in ways that suit their circumstances. Fair systems also build trust, reducing time and energy spent navigating politics or uncertainty, and increasing engagement and retention.
The overall message is that fairness and effectiveness are not opposing goals. Systems designed to work well for a broader range of people tend to work better overall. Data-driven decision-making, whether applied to equity or efficiency, leads to stronger results. When organizations rely on evidence rather than assumptions, they make smarter choices and waste fewer resources. Fairness, in this view, is not a constraint on performance but a driver of it.
In conclusion, "Make Work Fair: Data-Driven Design for Real Results" by Iris Bohnet and Siri Chilazi argues that creating fair workplaces requires a shift in focus from changing minds to changing systems. By measuring what matters, redesigning everyday processes to remove barriers, and making fairness visible through data, accountability, and role models, organizations can produce more equitable outcomes without relying on constant moral effort. These changes not only advance fairness but also lead to better decisions, higher productivity, and stronger performance. The book shows that when fairness is built into the design of work itself, it becomes sustainable, scalable, and beneficial for everyone involved.
Read
February 3, 2025'A timely guide to the latest research, aimed, the authors write, at “the activist working to advance DEI — as well as the activist working to limit DEI”'
My review for the Financial Times:
https://on.ft.com/4hqPnSA
My review for the Financial Times:
https://on.ft.com/4hqPnSA
April 24, 2025
Make Work Fair me pareció una lectura muy recomendable para quienes quieren pasar de la conversación a la acción en temas de equidad en el trabajo. Para mí, el libro se puede dividir en tres grandes aportes:
- Primero, cuestiona con muy buenos argumentos varios mitos sobre la equidad (como que basta con capacitar o cambiar actitudes individuales) y propone enfocarse en rediseñar procesos y sistemas organizacionales.
- Después, entrega herramientas concretas como el uso de datos, objetivos, incentivos y transparencia para hacer que la equidad no sea solo un ideal, sino parte de cómo se toman decisiones en el día a día.
- Finalmente, explora cómo aplicar este enfoque en prácticas como contratación, evaluación de desempeño, trabajo flexible o cultura organizacional, siempre con una mirada basada en evidencia.
Me gustó porque es práctico, está muy bien escrito, y no se queda solo en lo teórico. Propone acciones pequeñas pero efectivas que cualquier persona puede empezar a implementar, sin importar su rol.
Lo recomiendo no solo a personas que estén relacionadas a RRHH, sino a cualquiera que le interese el tema, porque tal como dicen las autoras, todos y todas podemos aportar desde nuestro campo de acción a hacer que el trabajo sea más justo.
- Primero, cuestiona con muy buenos argumentos varios mitos sobre la equidad (como que basta con capacitar o cambiar actitudes individuales) y propone enfocarse en rediseñar procesos y sistemas organizacionales.
- Después, entrega herramientas concretas como el uso de datos, objetivos, incentivos y transparencia para hacer que la equidad no sea solo un ideal, sino parte de cómo se toman decisiones en el día a día.
- Finalmente, explora cómo aplicar este enfoque en prácticas como contratación, evaluación de desempeño, trabajo flexible o cultura organizacional, siempre con una mirada basada en evidencia.
Me gustó porque es práctico, está muy bien escrito, y no se queda solo en lo teórico. Propone acciones pequeñas pero efectivas que cualquier persona puede empezar a implementar, sin importar su rol.
Lo recomiendo no solo a personas que estén relacionadas a RRHH, sino a cualquiera que le interese el tema, porque tal como dicen las autoras, todos y todas podemos aportar desde nuestro campo de acción a hacer que el trabajo sea más justo.
February 23, 2025
Make Work Fair by Iris Bohnet and Siri Chilazi is an impressively researched and well written book on an important topic. This is a timely read with DEI programs currently being evaluated across the US. While I tend to agree that many DEI initiatives should be dismantled, it doesn’t underscore the fact that diversity, equity, and inclusion in all aspects of humanity are vital.
This is a sticky topic because, to quote the authors, “Very few people are against fairness but we don't all agree on what it entails.” The real life examples in this book made the materials interesting and easy to digest, but I found certain action items to be idealistic. Businesses can’t operate exclusively through a DEI lens, and when it comes to certain accommodations that are suggested, there seems to be a lack of awareness about the decrease in productivity that would accompany them.
Thank you to NetGalley and Harper Business for providing an advanced copy of this book in exchange for an honest review.
This is a sticky topic because, to quote the authors, “Very few people are against fairness but we don't all agree on what it entails.” The real life examples in this book made the materials interesting and easy to digest, but I found certain action items to be idealistic. Businesses can’t operate exclusively through a DEI lens, and when it comes to certain accommodations that are suggested, there seems to be a lack of awareness about the decrease in productivity that would accompany them.
Thank you to NetGalley and Harper Business for providing an advanced copy of this book in exchange for an honest review.
May 10, 2025
Make Work Fair is an extremely timely and well-researched book. Iris Bohnet brings clarity and evidence to the conversation around workplace fairness, offering practical insights backed by data and behavioral science.
The frustrating part is how relevant this all is right now—especially as DEI programs face resistance and rollback across the country. The real challenge isn’t knowing what works; it’s figuring out how to apply these insights to drive lasting, structural change in organizations.
An important read for leaders, policymakers, and anyone who still believes fairness at work is worth fighting for.
The frustrating part is how relevant this all is right now—especially as DEI programs face resistance and rollback across the country. The real challenge isn’t knowing what works; it’s figuring out how to apply these insights to drive lasting, structural change in organizations.
An important read for leaders, policymakers, and anyone who still believes fairness at work is worth fighting for.
January 30, 2025
I loved this book!! It is not only accessible, but it is also brilliant and timely. As a non-profit founder working in gender equity and girls empowerment, this book not only leans into talking about gender injustice and inequity in the work place, but also gives stories, strategies, and tools to dismantle them. This is a book you return to over and over - not only when you are frustrated with the system, but when you are ready to work together to make workplaces more inclusive, equitable, and safe for all!
December 17, 2024
For real?
What is a "gender expert"?
And anyone who is remotely connected to the real world knows that the purpose of a business is to earn as much money as possible. Being "fair" doesn't even enter into the equation.
If it did, then companies should give out all their goods and intellectual property for free to those who can't afford to pay for it. That would be fair.
So much for the business creating valuable goods and services though.🙄
🚮
What is a "gender expert"?
And anyone who is remotely connected to the real world knows that the purpose of a business is to earn as much money as possible. Being "fair" doesn't even enter into the equation.
If it did, then companies should give out all their goods and intellectual property for free to those who can't afford to pay for it. That would be fair.
So much for the business creating valuable goods and services though.🙄
🚮
This entire review has been hidden because of spoilers.
February 1, 2026
Blinkist app book of the day!
Make Work Fair (2025) offers a data-driven alternative to ineffective diversity training by showing how to redesign workplace systems themselves. It demonstrates how measuring patterns, removing structural barriers, and building accountability into daily work creates organizations that are both fairer and more effective.
Make Work Fair (2025) offers a data-driven alternative to ineffective diversity training by showing how to redesign workplace systems themselves. It demonstrates how measuring patterns, removing structural barriers, and building accountability into daily work creates organizations that are both fairer and more effective.
Read
March 25, 2025Lots of accessible, impactful ideas and research/data to back them up.
July 19, 2025
Read as part of research for my dissertation. Helped to form my how.
November 10, 2025
This book is full of extremely helpful case studies and data organized around chapters like Incentives, Hiring the Best Person for the Job, Flexible Work Arrangements, etc. The index is very detailed if you want to look up something specific. It’s a really well researched book with a ton of practical advice.
Displaying 1 - 12 of 12 reviews