As technology plays an increasingly bigger role in working toward a more equitable world, data and analytics are becoming more prominent in the push for equality.
In the enterprise and government sectors, analytics was viewed as a luxury as recently as just a few years ago.
The COVID-19 pandemic, however, forced organizations to realize the crucial function of analytics amid rapidly changing conditions, whether they are trying to keep people healthy and safe or fighting for the survival of their businesses.
Now, as data and analytics become more necessity than luxury, their use is spreading.
Just as analytics is becoming more mainstream in the government and private sectors, the use of data and analytics is becoming more widespread in the nonprofit sector, where organizations are working toward equality on many fronts, such as racial and gender equality, environmental justice and equality in the criminal justice system.
Afua Bruce is a trained computer engineer, and over the past decade has worked in the private sector as a software engineer at IBM, the government sector as data strategy lead at the FBI and the nonprofit sector as chief program officer at DataKind, among other places.
In addition, she was recently a member of President Joe Biden's transition team.
Now, she and Amy Sample Ward have co-authored a book scheduled for release on March 22 titled The Tech That Comes Next: How changemakers, philanthropists, and technologists can build an equitable world.
Recently, Bruce took time to talk about the role data and analytics can play in the fight for equality, including how modern technology can be more inclusive.
In addition, she spoke about how nonprofits have increasingly used analytics just in the past couple of years, how recent graduates are playing a role in the growing use of data and analytics to work toward greater equality, and what comes next as activists use technology to build a more equitable world.
The title of your book refers to building an equitable world. So to start, what does an equitable world look like?
Afua Bruce: When I think of an equitable world, I think of one which is built around inclusion and not exclusion. When it comes to a lot of stories and coverage about how technology affects historically overlooked populations, there are still examples of technologies that have ignored the existence of Black women, or has not accounted for transgender people, or something like that. It's really thinking about what it means to design and deploy technology that is centered around inclusion as opposed to exclusion.
How does a technology exclude or include someone?
Bruce: What many people want to believe, and what has often been told, is that technology is neutral, that it is unbiased and that it removes the worst of humans from the process. But actually, technology is created by humans and is therefore built with the different biases that humans have. I think one thing we can do in designing technology is to ask questions about who we're serving -- how are we designing for them, how have we brought communities into the development process, how do we help fund the right activities, how do we help ensure that the technology that's being used is really serving the needs of people that need to use it?
What are some of the existing technologies that are helping build an equitable world and what are some that are on the horizon?
Bruce: One of the things I found really exciting about writing this book was talking to people who were working on all sorts of technology, talking to people who are using data science, machine learning and [augmented intelligence], people who are developing websites or [customer relationship management] solutions or web apps, even talking to people who have developed and deployed [electric] lights in developing countries, just bringing light to people.
Afua BruceCo-author, 'The Tech That Comes Next: How changemakers, philanthropists, and technologists can build an equitable world'
Any technology can be used to help build a more equitable world. We just have to think about how we've designed it and how we deploy it.
How can data and analytics be used in the many fights for equality?
Bruce: I did a lot of this work during my time at DataKind, which is a nonprofit that exists to do data science and machine learning in service of humanity. DataKind is one of a number of organizations trying to bring a strong analytical approach, strong data science and strong machine learning techniques to the social impact sector. One of the examples we talk about in the book is a collaboration DataKind did with John Jay College in New York City.
They had a lot of programs targeted toward freshmen and making sure freshmen complete their first year and continue on with their education, but what they realized was that they had a large number of students who were completing three-quarters of the credits needed to graduate but not graduating. They worked with DataKind to test a number of models -- DataKind developed about two dozen models -- and then whittled that down to the two that were most effective.
What was the result?
Bruce: Two years later, the John Jay College staff used those models to help identify students [in danger of dropping out], recommend some interventions and work with the staff to do those interventions. In a two-year period, 900 additional students graduated. That's 900 more families that have a graduate and have people who can get presumably better-paying jobs and more. That, to me, is one example of the power of analytics for building a more equitable world and giving more people opportunities to complete their degrees, giving more people opportunities to have access to different and better-paying jobs than they used to have, giving more families the opportunity to do more with more income coming into the family.
Additionally, what was exciting about the John Jay College and DataKind partnership was how John Jay College, as an institution, was strengthened as well. Now they knew how to work with technologists and have those conversations with the people designing the algorithms, and how to ask the right technical questions of future vendors.
There are scores of different ways organizations are working toward equality -- are most using data and analytics?
Bruce: What's exciting to me is that there is a real push to encourage more organizations to [use analytics] and to equip them to be able to do so. There are a number of different philanthropic institutions around criminal justice reform, environmental justice and healthcare where there is a lot of investment to see how to better use data, better use analytics, to improve the function of these organizations and the effectiveness of them.
In the social impact sector you do have organizations on a wide spectrum of data maturity. There are organizations that want to do great things but need that investment and support in building out the right databases and having access to analytical software, and then you have organizations that are large multinational nonprofits that have entire data teams.
What is some of the data that's key to enabling organizations to work toward equality -- in the fight for a more equitable criminal justice system, for example, what data is important?
Bruce: It's just so wide that I don't think I can pick a specific data type. Some interesting work I'm seeing in criminal justice has to do with translations and making sure that people who need to engage with the criminal justice system can communicate. There's a lot of research going on in ways to prevent recidivism. There is some interesting work going on with respect to youth diversion programs. Work with young people while they're still minors to keep them out of the system and provide them the support them they need so they don't get caught up in the adult system.
We spoke back in 2020 about analytics in the social justice movement. Since then, how has the use the use of analytics in working toward more equality evolved?
Bruce: Here, what's exciting is that you have more organizations that have been spun up directly for [working toward equality]. These are standalone organizations dedicated to such things as data science and Black and brown populations. That's really exciting. What's also exciting to me is the excitement at the university level. There are a lot of data science programs that are spinning up at universities at both the undergraduate and graduate levels, and then as students look for projects to do, they're looking for projects in the social justice space.
They're really excited about defining problems and assigning data to help answer those questions they have.
How effectively are the mostly nonprofit organizations that are dedicated to making the world more equitable using analytics compared to for-profit organizations?
Bruce: I would say the nonprofit sector is catching up. Within the nonprofit sector, you have a diversity in the size of organizations. There are many that are very small and designed for neighborhood and community support. They may not have even a business case for analytics. That's an important difference if we're looking to compare. But organizations like DataKind partnered with organizations like Data.org, MasterCard and the Rockefeller Foundation in 2020 to do a big data science for good challenge and encourage nonprofits throughout the world to develop more use cases. They've given both financial and technical support to a lot of organizations, and they've received applications from more than 1,000 organizations around the world that are thinking about this kind of work but need a little extra support to implement it.
I think the nonprofit sector is catching up to the private sector from a capability standpoint. I think there's a slow and steady shift happening. There's so much interest, and with the continuation of the pandemic more people are figuring out how to best leverage technology, whether it's video conferencing or what analytics you might need to do.
Are there equality movements -- such as for racial equality and gender equality and environmental justice -- that are making better use of data and analytics than others?
Bruce: I don't know if I have a way to differentiate along those lines. For me, the differentiation is often related to the size and data maturity of the organization rather than the issue they work on.
So are there perhaps issues that have more large organizations working on them, or does each have both its small and large organizations?
Bruce: I think they all have their big and they all have their small.
What are the next steps in the use of analytics in the fight for equality?
Bruce: One of the things we talk about in the book is how technologists themselves can be more prepared for the future.
Some things that are exciting are how we train technologists to think about the importance of understanding the cultural context when they're doing analytics, understanding the organizations or issues they're working with as they're doing the analytics and not focusing solely on the data. I also think [it's important] to support social impact organization leaders who are thinking differently about how to use technology so they can extend their mission.
Editor's note: This Q&A has been edited for clarity and conciseness.