Are Analytics the key to solving inequity in Busing?

Some lessons are worth saying twice… maybe even three times!
A few hours before the Data for Black Lives Conference official began 50 plus education activists from around the country and across generations discussed the ways big data has impacted public education in the black communities. Below is a synthesis of their analysis. 

Rather than typing a direct transcript of everyone's statements I've written a series of post discussing the primary themes that came about during my facilitation, following my presentation on Big Data for Education Justice (View Deck).

The structure of each post:

  • Observations – A synthesis of notes from a discussion on the current structures that exist in education and how big data has impacted education.
  • Recommendation – Based on existing interventions brought up during our discussion and frameworks being implemented by activist in the room.
  • Implications – Broader ideas of how we see big data’s role in public education should evolve.

Data types we discussed:

Quick definitions
  • Administrative Datainformation captured at the school level that has to do with the schools ecosystem. This data includes metrics that measure achievement, demographics, and engagement.
  • Resource Data – Any information that relates to funding, budget and how money is allocated.
  • Click Stream Data – the capturing of mouse movements and keystrokes, used to learn patterns about human behavior.

Observations, a Tale of Two Cities’ Attempt to address Structural Racism

Primary Data Type | Resource Data

Boston Public Schools’ failure to implement more equitable bus routes resonated with many activists who understand the racial politics of busing.

We discussed how BPS’s partnership with MIT flopped, due to its lack of parent support. Similar to my post on predictive analytics in schools, this project never established a collaborative partnership with parents, who saw the 85% change in school start times as untenable.

Our Boston based participates cited this failure as a trend. One mentioning A plan in Boston in 2009  attempted to bring bus rapid transit to the 28 line, a project known as “28X.” The state failed to win support from leaders, activists, and merchants in neighborhoods.

In October, recently elected Congresswoman Ayanna Pressley spoke at the Dukakis Center Convenes Forum on Public Transportation Inequality in Boston, stating “that African American people who use the Massachusetts Bay Transportation Authority system have longer commute times than white riders, and spend 66 more hours per year waiting for and riding buses than white riders.”

Pressley, like many at our workshop understood that riding the bus is often the only option for low-income minorities. She ends with a familiar point.

“People close to the pain are the ones that need to be involved in creating the solution,” 

Sound familiar?

Recommendations:

1) The communities that will most be impacted by data-driven interventions need to be part of the design and implementation process.

* Note: this lesson is so important it deserves two post.*

The work in NYC led to the “Select Bus service in the Bronx spurred by advocacy among local groups and coordination between the Metropolitan Transportation Authority, which runs buses and trains, and the city Department of Transportation, which controls streets, curbs, and traffic lights. 

While this program has not solved all the transportation equity issues in New York it has been around for nearly a decade and has made things better for those who rely on public transit.

Implication for Officials:

There’s no algorithm to build trust. You can’t build consensus with billboards and op-eds. I can’t speak for what citizens in Boston are asking for, but I can cite the creative work of the team behind Envision Cambridge. This team spent 2016 researching and LISTENING TO COMMUNITY MEMBERS asking their opinion on new city planning. Transportation journalist Rachel Kaufman wrote about their efforts. While it’s not exactly the same challenge the lesson are translatable. Article linked below:

Cambridge Is Working on an Inclusive City Road Map

The Best Outcome with Top-down Predictive Policies in Schools is Community Action!

A lesson for those implementing predictive analytics in schools; community voice can’t be ignored and is often your best asset.
A few hours before the Data for Black Lives Conference official began 50 plus education activists from around the country and across generations discussed the ways big data has impacted public education in the black communities. Below is a synthesis of their analysis. 

Rather than typing a direct transcript of everyone's statements I've written a series of post discussing the primary themes that came about during my facilitation, following my presentation on Big Data for Education Justice (View Deck).

The structure of each post:

  • Observations – A synthesis of notes from a discussion on the current structures that exist in education and how big data has impacted education.
  • Recommendation – Based on existing interventions brought up during our discussion and frameworks being implemented by activist in the room.
  • Implications – Broader ideas of how we see big data’s role in public education should evolve.

Data types we discussed:

Quick definition
Azeem translates to GREAT in Arabic
  • Administrative Datainformation captured at the school level that has to do with the schools ecosystem. This data includes metrics that measure achievement, demographics, and engagement.
  • Resource Data – Any information that relates to funding, budget and how money is allocated.
  • Click Stream Data – the capturing of mouse movements and keystrokes, used to learn patterns about human behavior.

Observations about Data-Driven Schools & the use of Risk Scores

Primary Data Type | Administrative Data

We found that data-driven schools and school systems are using information riddled with implicit bias to determine student outcomes.  The perceptions of school officials is codified into “objective” data; districts are measured almost exclusively on their performance on exams.  

Districts are failing to invest adequate resources in gathering insight from the community and the families they serve and instead invest in consultants and advisors that promise improved schools but continue to push out interventions that lack community feedback.  

An example of this is the use of risk scores, which have been found to be racial bias and can force students down a path to incarceration and underachievement.

While a risk score within itself is tool the ways it can be implemented are what makes it oppressive.  Most risk scores are designed using legacy (old) databases that have bias “baked in”. They’re also designed in a way that lacks input from the people who will be most impacted by their implementation.

Recommendations:

The communities that will most be impacted by data-driven interventions need to be part of the design and implementation process.

What does this look like, you ask?

There is no one way to structure an effective coalition between grass-root leaders and local government. But below is a great example of what happens when those most impacted are left out.

Marika Pfefferkorn, leader of the Coalition to Stop the Cradle to Prison Algorithm, recently won a victory with the dissolution of problematic data-sharing agreement; the “agreement” and corresponding predictive technology had been created to predict youth “at risk” of future delinquency. This “faulty process” and legal agreement lacked any formal community partnerships or any advanced investments in youth development.

Above image of Marika CPA Coalition Co-founder and Twin Cities Innovation Alliance (TCIA) Director
The alliance recently won their campaign to dissolve a joint powers agreement that would have brought risk score based predictive analytics with no community input.

Marika is quoted, “We know that predictive technologies cannot be detached from human bias and error. And while data can be a tool for positive change, it is also clear that there are many risks that we need to unpack in relationship to the JPA and Big Data, Predictive Analytics and Algorithms and their potential to amplify racial and ethnic disparities in the education and the juvenile justice systems.”

You can learn more about strategies used by community leaders by exploring the Toolkit on Organizing to Combat the School-to-Prison Pipeline.

Marika was part of our discussions and championed shared values of the activists in the room:

  • Data-driven education policy, like all public policy can not be equitable if there aren’t formal partnerships between impacted communities and government.
  • It is also important to use explicit language at the forefront when communicating with parents and advocates.

Implication for Officials:

Government efforts function better when agencies partner with the community to gain support and offer feedback.  Data-driven intervention should be implemented with community stakeholders having input from design to implementation and the work needs to be iterative.  Transformative education success is only possible when impacted communities are seen as community experts and are given access to the data they themselves generate.

The Origin of a Blog and the Death of an Imposter

Original Post: 1/25/18

Disclaimer: The same way people hate to hear the sound of their voice in a voicemail I’ve hated sharing my writing with others.  But I love building tools that solve problems.  For over 2 years I’ve researched the intersection of big data, Education, and racial equity.  In my research I’ve found that the lack of data literacy in the education communities is a roadblock, this deficit limits the strategies available to education advocates.  This blog is meant primarily to be a tool to increase data literacy among all people, but particularly education leaders, families and education advocates.

Being a tool, this means it is iterative and will change and, in theory, get better!

Below is the story of how after experiencing something extraordinary I decided to do something so singular.  Write a blog.  If you’re looking for my resume/Bio or are interesting in my quant research please click the link to my Github.

All Other artifacts of my work can be found on the Portfolio Page.

On the weekend of January 12th I road a 7 am bus from Port Authority NYC to MIT in Boston to join over 700 people at the Second Inaugural  Data for Black Lives Conference. I encourage anyone with a passion for racial equity, artificial intelligence, and the future to check out the Youtube uploads of the keynote! For those with less time, in short, the D4BL II conference brought together over 500 data scientists, mathematicians, advocates and students doing work to address, or who are simply interested in the ways that data and technology intersect with racial justice and social good.  As a Black data Scientists focused on addressing issues of education equity and mass incarceration this conference felt tailor made for me; and if that wasn’t enough of a motivator I was going as  a speaker!

If you go to the Agenda website you won’t find my session, it was part of pre-conference session for education advocates, never the less I was humbled to have this opportunity (I will post a video on my presentation below). 

As I mentioned before, my primary intersections are issues of race, education, and mass incarceration.   Currently I’m partnering with the NYU Metro Center for Research on Equity.  For the past year we’ve investigated the impact of Big data in education on black children and how it can be used for education equity.  We have had the support to the founders of Data for Black Lives and were invited to speak at a pre-conference session to present our research. 

Perplexing to some but familiar to most, my sense of pride quickly turned to dread and uncertainty. Realistically, I knew this research like I knew the stove in my kitchen, having committed over 100 hours to this work officially, 150 if you include pillow hours. Yet hovering around me was this thought, that I didn’t belong.  Admittedly that feeling did not stop me from booking a ticket, getting up at 5 am to take a bus from New York to Boston, which had an average temperature high of 12 degrees, but it certainly had fried my confidence.   

I won’t speak for all first generation college students, but I got through a number of situations (presentations, alumni networking events, buying beer with a fake ID) by simply pretending. Pretending I had read the book, pretending I knew the team fight song, pretending I was from Wilmington, IL.  So instinctually I resolved to fake it.

Somewhere between watching the episode 4 of the latest season of Voltron and practicing my lines I recognized the unsatisfying reality I had been avoiding. That this conference and the people who made the commitment to attend deserve better than your best impression, they deserve your best; if for no other reason than out of respect for the spirit of why you’re going and the opportunity to just be in an environment that felt designed for you.

Knowing that truth I took to exercise these feelings.

So like any millennial, 1st gen. college grad experiencing imposter syndrome I and reached out to my community via our black alumni group-chats and text threads, explaining to them this out of body experience. I told them of my fear and my fear was met with love and validation.

I spoke (see video below) and only through the strength of prayer, practice and affirmation from friends and family did I do my part so we could achieve our goals; sharing our research and knowledge, and garnering insight from a community of activist. 

I’m sure if you watch the video of my presentation you see again something ordinary.  When I watch it I see the most ordinary thing in the world, me.  But that’s the point; what’s actually extraordinary about that video is what you don’t see.  You don’t see in imposter, you see an ordinary guy with extraordinary goals choosing to operate from a place of authenticity over fear. 

The weekend itself was far more spectacular than this story (I warned you). I plan to write several post articulating the lessons learned throughout the weekend.   Including one addressing the 3 most inspiring things I saw.