Membership Spotlight – Valarie Findlay, Research Fellow, Police Foundation (US)

Coexistence: Evidence-Based Policing and Emerging Technologies

Valarie Findlay – Research Fellow, Police Foundation (US)

Evidence-based policing promises to be a powerful force for change and holding significant value for police organizations, the public and community safety. Often misunderstood, evidenced-based policing promotes the importance of practices based on scientific evidence that demonstrates what works ‘best’.

Plainly stated (sort of), qualitative and quantitative data is gathered from operational practices in a controlled framework that accounts for fluid environmental factors. Later, this data is analysed to fact-based evidence used to improve policies and procedures.

What evidence-based policing is not is the uncontrolled, empirical collection of data or its arbitrary use to implement or change procedural approaches and practices. Properly constructed, it does the opposite.

Allowing the integration of other research methods, such as discourse analysis, can create context where dynamic factors influence responses, such as offender interactions that may differ depending on circumstance, individual, neighborhood or city.

A linkage between research and practice, evidence-based practices hinge on the value of anecdotal and formalised evidence from measurable policing functions – from implementing guidelines to crime prevention. This nexus of controlled theory married with practice outcomes is gaining traction as it allows for faster operationalisation of academic research. From this, the best quality evidence can be used to shape ‘best practices’ through a process of re-testing and measuring the hypothesis.

Looking at both static and dynamic factors, the ones that change analogue and expected outcomes, is valuable to policing since its a human-behaviour driven profession. It’s easy to presume that under a particular scenario that under “normal” circumstances any person would react a certain way or that from the officer’s “experience” a certain outcome is presumed; this is what evidence-based policing stands to clarify.

The field of medicine is a good example. It’s widely accepted by professionals that diagnostics rely on three important areas of ‘evidence’: clinical signs, pathology and anecdotal (patient-view) since how disease presents itself physically, biologically and practically varies by individual. These diagnostic axioms produce irrefutable facts that remain relevant despite the use of technology; noting, centuries ago diagnostics were largely anecdotal and treatment was empirical.

For policing, an evidenced-based approach could look like this: a hypothesis is drawn from anecdotal evidence showing community-based policing programs deter recruitment of youth in street-level gangs. A sample group or area is selected to test the hypothesis by collecting data from practice activities by the subject matter experts (officers). Controlled by research methods that isolate criteria (demographic, circumstantial, geographic, etc) findings can be re-tested with outcomes confirmed, creating procedural or programmatic approaches.

Turning to technologies and how they could assist in evidence-based approaches, I’ll preface this by saying identify the “problem” and organisational requirements and letting those drive the selection of the technology; opposite view of how technology can be used to address all problems only serves the vendors.

Using artificial intelligence (AI) as an example – a hot topic across all sectors – may solve one of policing’s longstanding problems with the persistence of analogue data alongside electronic data and converting it into usable intelligence. AI could play a key role in addressing this problem not necessarily as an approach that automates decisions, actions and responses, but aiding in them.

Clearly, the more integrated public safety data is, whether individually identifying or statistically informing of a group or area, the more informative it is. AI can create generative processes that correlate large amounts of data, replicating ‘human’ thinking through algorithms and digitized heuristics, learning from outcomes. These predictive, adaptive responses can then be extended to similar problems making AI inherently useful to some aspects of policing.

The value to evidenced-based practices could improve data analytics across many programs – crime prevention and interdiction, investigative functions in analysis and intelligence gathering, from tip consolidation to complex field intelligence.

Despite AI’s ability to rapidly correlate and create associations with data, as with any technology, it has limitations and risks. The outputs and conclusions generated by AI processes are only as good as the input data and algorithms – it is truly a matter of ‘garbage in, garbage out’ and AI will not magically improve the quality of input data.

While ethics and privacy issues arise with AI’s ambiguous processes, for evidenced-based policing to explore its value, high-integrity algorithm, validation and verification will be key to establishing trust and confidence for all sides.


About Valarie:

Valarie Findlay is an ASEBP Member and a research fellow for the Police Foundation (USA) with two decades of senior expertise in cyber security and policing initiatives. She has worked extensively on federal cyber initiatives and is a member of the Canadian Association of Chiefs of Police eCrimes Cyber Council, as well as other federal cyber councils in Canada and the US. She holds a Masters in Sociology and a Masters in Terrorism Studies from the University of St. Andrews; her dissertation, “The Impact of Terrorism on the Transformation of Law Enforcement” examined the transformation of law enforcement in Western Nations.


Membership Spotlight – Eva Ruth Moravec, Texas Justice Initiative

Reporting with Confidence

Eva Ruth Moravec, Co-Founder of The Texas Justice Initiative

I love learning, and it’s my belief that it’s unhealthy for one to close off one’s mind to learning new things. Because of this passion for education, journalism – a profession that requires asking questions and understanding the answers – has always come somewhat naturally.

For years, I worked for newspapers, starting with weekly publications in downtown Austin, Oak Hill, Southwest Houston and San Antonio, and eventually landing at the daily San Antonio Express-News. At weekly newspapers, the staff is typically miniscule, and reporters are assigned to cover anything from meetings at city hall to breaking news and features on new restaurants and businesses. So I’d covered a few scenes before my first day on scanners with the Express-News, but nothing could have truly prepared me for the breaking news beat.

I arrived to my first scene – a dog bite – a little too quickly, I’d soon learn. I parked around the block, and as I rounded the corner, I heard two gunshots. The scene was chaotic, and as first responders hurriedly walked in and out of the one-story house, panicked relatives began to arrive. Around me, police officers roped off the crime scene. Inside the home, a 7-month-old had been brutally killed – “dragged around like a ragdoll” ¬– by two pit bulls when the baby’s grandmother left the room to heat a bottle.

How often did that happen? Were pit bulls inherently dangerous, or were all dogs liable to eat small children? Did these dogs show signs of aggression before? I quickly learned that law enforcement tracked these things, and that the dogs had attacked a child before. The woman was indicted for injury to a child and died of natural causes before a trial could take place. I was left wondering: How can I report in a way that helps readers, instead of just shocks and horrifies them?

It has always been important for me to infuse my reporting with context, facts, best practices and some sort of take-away for readers. It remained a priority when I covered the Texas Legislature for The Associated Press, and I watched in excitement as legislators passed a law requiring basic information to be reported on each officer-involved shooting. In a data journalism class for my Master’s degree, I started a database of information from the reports, and sought a way to report on the incidents using both the qualitative and quantitative methods I was learning about. The result was a series on officer-involved shootings that ran in three newspapers, and while I reported and took journalism classes, I also took law enforcement training courses on using force and participated in Austin’s Citizen Police Academy.

Throughout the past decade, I’ve enhanced my journalism skills with those of a budding social scientist, thanks to graduate school, and have been able to look more critically at research, policies, practices and outcomes. I have a lot more to learn, but I know that factual information has the ability to enhance trust and build understanding. These days, I work occasionally as a stringer covering breaking news for The Washington Post, but mostly focus on running my nonprofit, the Texas Justice Initiative, which is focused on increasing transparency and accountability in Texas criminal justice. I am also writing a book about the legality vs. public opinion of officer-involved shootings in Texas for the University of Texas Press. I find myself relying on experts, research and evidence-based practices for nearly everything I do, and being a member of ASEBP allows me to stay at the forefront of new law enforcement research and practices.


About Eva:

Eva Ruth Moravec is a 2018 John Jay/Harry Frank Guggenheim Criminal Justice Reporting fellow, a freelance reporter and the author of a forthcoming book that explores the legality of police shootings in Texas. While in a data journalism class for her master’s at the University of Texas at Austin, Moravec started a database of officer-involved shootings in Texas. She then explored cases in her database through “Point of Impact,” an investigative journalism series that ran in three Texas daily newspapers. She has covered criminal justice in Texas for a decade, including stints at the San Antonio Express-News and The Associated Press.