“What happened in Minneapolis and Louisville and Chicago and countlessother cities across the United States is unconscionable (and to be clear, racist). But what makes me the maddest is how easy this problem is to solve, just by the police deciding it’s a thing they want to solve.” - Allison Weil on Medium Before Allison Weil became an investor and Senior Associate at Hyde Park Ventures, she was a co-founder at Flag Analytics, an early intervention system for police departments designed to help identify officers at risk of committing harm. Unfortunately, Flag Analytics—as a business—was set up for failure from the start, regardless of its predictive capability. As Allison explains so candidly and openly in her recent Medium article (thanks Allison!), the company had  “poor product-market fit, a poor problem-market fit, and a poor founder-market fit.” The technology was not the problem, and as a result, it did not help them succeed as a business or in producing the desired behavior change because the customers were not ready to act on the insights. Yet, the key takeaways from her team’s research during the design and validation of their product — and the uncomfortable truths they uncovered — are extremely valuable, especially now as we attempt to understand why racial injustice and police brutality continue to persist in law enforcement agencies. As it turns out, simply having the data to support a decision doesn’t mean the decision will be made using the data. This is what Allison found out while in her interactions with several police chiefs and departments, and it’s also what we discussed in this episode. I asked Allison to go deeper into her Medium article, and she agreed. Together, we covered:

How Allison and a group of researchers tried to streamline the identification of urban police officers at risk of misconduct or harm using machine learning.

Allison’s experience of trying to build a company and program to solve a critical societal issue, and dealing with police departments that weren’t ready to take action on the analytical insights her product revealed

How she went about creating a “single pane of glass,” where officers could monitor known problem officers and also discover officers who may be in danger of committing harm.

The barriers that prevented the project from being a success, from financial ones to a general unwillingness among certain departments to take remedial action against officers despite historical or predicted data

The key factors and predictors Allison’s team found in the data set of thousands of officers that correlated highly with poor officer behavior in the future—and how it seemed to fall on deaf ears

How Allison and her team approached the sensitive issue of race in the data, and a [perhaps unexpected] finding they discovered about how prevalent racism seemed to be in departments in general.

Allison’s experience of conducting “ride-alongs” (qualitative 1x1 research) where she went on patrol with officers to observe their work and how the experience influenced how her team designed the product and influenced her perspective while analyzing the police officer data set.

Resources and Links:

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Quotes from Today’s Episode

“The folks at the police departments that we were working with said they were well-intentioned, and said that they wanted to talk through, and fix the problem, but when it came to their actions, it didn't seem like [they were] really willing to make the choices that they needed to make based off of what the data said, and based off of what they knew already.” - Allison “I don't come from a policing background, and neither did any of my co-founders. And that made it really difficult to relate to different officers, and relate to departments. And so the combination of all of those things really didn't set me up for a whole lot of business success in that way.”- Allison “You can take a whole lot of data and do a bunch of analysis, but what I saw wa