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Melissa Harris-Perry: You're listening to The Takeaway with Melissa Harris-Perry from WNYC and PRX, in collaboration with WGBH Radio in Boston.

Alondra Nelson: When we have the ability to alleviate suffering but we don't, it's not a failure of science, it's a failure of imagination.

Melissa Harris-Perry: Dr. Alondra Nelson, head of the White House Office of Science and Technology Policy, has placed the intersection of equity and data at the center of her work with the White House. On April 6th, also known as Data Science Day, she delivered a keynote address about the importance of data collection, to assess whether government policies and programs are truly equitable.

Alondra Nelson: When we can break the bonds of oppression, we can expand civil rights and civil liberties and choose not to, that's not a failure of engineering, but a failure of conscience.

Melissa Harris-Perry: Last week, the Biden Administration released a report from the Equitable Data Working Group, co-chaired by Dr. Nelson, and she's here with me now to discuss the findings. Welcome to The Takeaway, Dr. Alondra Nelson.

Alondra Nelson: Thank you very much, Melissa, for having me.

Melissa Harris-Perry: Alondra, let's start first with the formulation that you've given us because I do love this language. How are science and imagination, engineering and conscience connected?

Alondra Nelson: These are the connections or principles that really guide a lot of the work of the White House Office of Science and Technology Policy, and indeed, the Biden-Harris Administration more generally. This is an administration that came into office saying that we need to prioritize science over fiction. One can have an imagination about how we do science and how we do data science in particular, and that imagination can allow us to think about how we can use these tools to advance equity.

That's to say, it's not a status quo way of thinking about we how we do and use data, or computational social science or statistical tools, but really opening the apertures of our imagination for policy and for good government to think about how science and data can be used to improve the lives of the American public.

Melissa Harris-Perry: That notion of using science and data to improve the lives of the American public, that is at the core of President Biden's day one executive order on racial equity. Tell us a little bit about it.

Alondra Nelson: Yes. This is not only the day one executive order, it is the first executive order that President Biden signs on day one and/or issues on day one. It is really the cornerstone of so much of the work of the Biden-Harris Administration. We might call it the equity EO, the equity executive order, is really far-reaching and forward-thinking. The far-reaching is that it is a whole of government approach to advancing equity.

I know members of the administration say a whole of government a lot, and just to unpack that a little bit for you and your listeners, it means that every agency, every department in government is being tasked with, and it's a call to action for every agency and department to move forward on a particular policy or priority that the president is setting forth. In this case, it meant that all of the government was really to turn its attention to advancing equity and to better serving all of the American public, but in particular, underserved and under-reached communities. That's the far-reaching.

One of the forward-thinking pieces of this equity EO was, a portion of it that established the Equitable Data Working Group. The first in government ever attempt to have all of the government body, the job of which was to think about how we can use data, in particular disaggregated data. Data that gives us insight into not only race and ethnicity, but sexual orientation, gender identity, disability, region, to be able to identify disparities and use policy and programs to remedy them.

Melissa Harris-Perry: Let's dig in a little bit more on that highly nerdy word, disaggregated and disaggregated data. Give us an example of what would constitute disaggregated data?

Alondra Nelson: Sure. I think that we're very familiar with getting survey data that's about large swaths of the population. 45% of Americans think this, for example. Disaggregated data is data that can be broken down and analyzed by other categories, not just Americans, not just Americans in the South, but you can think about rural communities and those can be all over the United States. You can think about persons with disabilities, you can think about veterans' status and other variables.

What this allows us to do is really understand who's missing from the numbers and the stories and the experiences we collect. Disaggregated data can help us understand how people who belong to multiple underserved or underrepresented or intersecting populations experience life are being served or not served adequately by the government. This might include a married Black woman, a transgender immigrant living in a rural community. These are intersecting identities and populations that often experience compounded discrimination or compounded disadvantage with regard to accessing services, benefits, and programs.

Melissa Harris-Perry: I'm going to ask you to go deep into one more word or phrase that I think can sound like government-speak, but I want you to make it real for us. What does it mean to advance equity?

Alondra Nelson: To advance equity means to start from a place that says we want to be a nation in which everyone has equal opportunities, but the way that things are currently set up for many people, they're not starting from a place of equality. To advance equity means to start from the place that says everyone doesn't have the same opportunity or the same access to opportunities and that part of the job of the government is to identify and lift up those folks who need help, who need assistance, and starting from that equal place, is one way of thinking about that.

Melissa Harris-Perry: The White House itself, certainly makes some policy, but so much of what you're doing is presumably in connection with other partners in the federal government. Who are some of the colleagues that you've been working with in this equitable data working group?

Alondra Nelson: The working group which I co-chaired with a colleague named Margo Schwab, from the Office of Management and Budget, had participants from NASA, from FEMA, from NOAA, this is the alphabet soup, but from the Department of Labor, from Health and Human Services. Departments and agencies that work in the spaces that are important for governments work to advance equity. These included also the Department of Transportation, the USDA that does a lot of work with rural communities, the Department of Energy, and many others besides.

The work of the working group proceeded through a series of case studies or drilling down into particular questions. Questions like, are there programs across government that are addressing climate-related crises, recent hurricanes, where people and communities often need acute services from the government? Have we been successful in reaching the people that need that support the most? Who are those programs reaching? Who hasn't been reached by those benefits? How can we do better going forward?

In that smaller working group, we had colleagues from FEMA as well as from the USDA, and from NASA which has a lot of spatial data that's important for understanding climate and climate change. We also had colleagues from the Department of the Treasury and the Department of Commerce working on thinking about the equitable distribution of employment impact payments, and thinking about using their data together in a privacy-preserving way to understand who's getting access to those benefits, and who we still need to serve with those benefits.

Melissa Harris-Perry: That's an interesting phrase I don't think I've heard before, privacy-preserving way, but it's exactly where I was going next. On the one hand, it's exciting to hear this work around data and some of these big-think structural issues, even in the midst of having to manage day-to-day concerns and crises. On the other hand, you'd say to me, the government is really thinking about our data, and I get nervous. Talk to me about privacy.

Alondra Nelson: Part of what is in this report, which is called The Vision For Equitable Data that came out last week is, a list of all of the communities and researchers, impacted communities, advocates, and experts that we talked to over the last year in the course of doing this work. Part of how we were engaged with the stakeholders was really around this question. On the one hand, particular communities like the Asian-American, Native Hawaiian, and Pacific Islander communities really understand the importance of needing this disaggregated data.

That the experience of Asian-Americans or Asian-America is really shaped and contoured by immigrant experience by generation, by access to resources, by various kinds of occupational locations and the like. It is not a monolithic experience. The importance of having disaggregated data for understanding that was really important to many communities and to the AA, NH, and PI community in particular.

Melissa Harris-Perry: Great. Alondra, we're going to take a quick break. We will be right back with more right here on The Takeaway.

[music] We're back with Dr. Alondra Nelson, head of the White House Office of Science and Technology Policy. We've been nerding out just a bit over data. Let's get to where these data are going, what its goals are. How do we know if a program, a policy, and effort by the federal government is in fact creating a more equitable world?

Alondra Nelson: Yes. It starts with having the data and having a baseline. We might think about a particular program like unemployment insurance and in the first instance just disaggregate the data. Collect data in a way that allows us to ask questions about equity and then go back to that data having established a kind of baseline, "These are the variables we've disaggregated." One can then go into the data and say, "Was it the case that immigrant populations in this community receive this data? Did communities that we know needed most get access to the resources that government was making available to things like the American rescue plan for example?"

In a broad swap those are ways that we can do it. I think that part of this is really about establishing practices to collect and report data across many variables in government which doesn't actually yet exist as a norm across all of government and we're moving towards that. Part of this vision for equitable data is to do that. That means collecting data by more variables. It also will mean-- Importantly, one of the recommendations is revising standards for how we collect and report data on race and ethnicity and ways that will allow us to have more information about Asian-American communities, for example, that can answer some of these equity questions.

Melissa Harris-Perry: Okay. I want to shift just a little and make a reality check here. This is all super exciting. It also seems complex. We know that whatever else happens, someday, there will be a new administration in the White House. When that happens, what happens to all these efforts to collect and disaggregate data?

Alondra Nelson: I think the short answer to your question is that one of the recommendations of the report is to make disaggregated data the norm for the work of government while robustly protecting privacy. That means that we will be making investments and infrastructure in the government so everything from having more people with statistical and data science capacity working in government, and we encourage any listeners who have these skill sets to consider doing a tour of duty in government.

It also means trying to build more collaboration across agencies and departments in government that can really leverage and build disaggregated data sets by working more closely together like some of the collaborations I mentioned between FEMA and NASA or between the treasury and commerce, for example.

Melissa Harris-Perry: Let me shift to one other project that you've worked on during your time in the White House, what is the bill of rights for an automated society?

Alondra Nelson: The bill of rights for an automated society is a policy planning process that we started last fall and that we are winding down. It's a real effort to go back to first principles with regards to thinking about technology policy and automated systems, AI in particular. I think it's often the case that we think when new emergent technologies come on the line that we have to wholesale remake democracy. We have to have new principles, new policies, and new regulations.

Sometimes things emerge in the space of science and technology that are so new, and that we've seen nothing like them before that perhaps that might be the case. What this bill of rights attempts to do is really go back to first principles, what are the things that all Americans should be able to expect with regards to their civil rights and their civil liberties, for example with regards to first amendment rights privacy protection in the space of automated technologies even if they're new, even if they're just coming out of the imagination of a computer scientist or an engineer. That there are these enduring rights that we have that don't change even as new innovations come online.

That's really the foundational principle of it.

Melissa Harris-Perry: Alondra Nelson is head of the White House Office of Science and Technology Policy. Dr. Nelson, thank you for joining us.

Alondra Nelson: Thank you for having me, Melissa.

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