What It Was Like — "The Man Using Maths to Find Serial Killers"

Host: Julian Morgans
Guest: Thomas Hargrove
Air date: November 28, 2025

Episode Overview

This episode delves into the extraordinary work of Thomas Hargrove, a former investigative journalist who developed a mathematical model to analyze open-source FBI murder data and detect potential serial killers across the United States. Host Julian Morgans explores how Hargrove's algorithm exposed patterns that police often miss—sometimes with devastating consequences. The conversation covers the creation of the model, its successes and failings, the limits of law enforcement cooperation, and the immense power and limitations of data in seeking justice for the victims of serial murder.

Key Discussion Points & Insights

1. Thomas Hargrove’s Background and Motivation

• Hargrove was an investigative journalist for 37 years, beginning as a "cop shop reporter" in Birmingham, Alabama and later working as a White House correspondent.
• His interest in teaching computers to spot serial murders stemmed from observing "linkage blindness" in policing—cases that should be connected, but aren't.
• The Atlanta child murders in the late 1970s highlighted to him how police can miss clear serial patterns.

Notable Quote:
“It’s called linkage blindness ... that linkage frequently goes missed because of how we investigate murder in America. That prompted me to think, well, maybe there’s something that a journalist could do to try to fix what does seem to be pretty broken.”
— Thomas Hargrove (07:53)

2. The Birth of the Algorithm

• The breakthrough came upon seeing the FBI’s Supplemental Homicide Report: a data set containing specifics (victim's age, sex, ethnicity, etc.) for every tracked murder.
• The project aimed to have the computer independently recognize infamous serial killer cases (i.e., the Green River Killer) as success.
• He and intern Liz Lucas used cluster analysis—grouping cases by shared traits—to find previously unrecognized clusters.

Memorable Moment (12:40):
Testing the algorithm in Los Angeles, Hargrove found five unrelated serial killers operating at the same time—information not previously public.

Notable Quote:
“To me, God forgive me, but to me, they were just lines on a spreadsheet... It was a very intellectual exercise... I was trying to develop a mathematical procedure.”
— Thomas Hargrove (15:27)

3. Discovery in Gary, Indiana

• One striking cluster was a series of 15 strangled women in Gary, Indiana, mostly sex workers.
• Hargrove attempted to alert local police and government but faced silence and denial.
• The "linkage blindness" and reluctance by authorities—driven by resource constraints, political hassle, or pride—blocked preemptive action.

Notable Quote:
“In Gary, Indiana, there is a well-documented phenomenon for police to be reluctant to consider the possibility of serial murder... it becomes hellish. It's perfectly understandable why there'd be a reluctance.”
— Thomas Hargrove (20:35)

4. Heartbreak: Lives Lost Despite Warnings

• Hargrove's warnings went unheeded. In 2014, Darren Dion Vaughn was arrested for murdering seven women after Hargrove’s outreach.
• Vaughn later hinted at having killed many more, possibly making him one of America’s most prolific serial killers.
• Hargrove reflects with regret, wondering if there was more he could have done.

Notable Quote:
“Those seven women died after I contacted the Gary Police Department...I had even sent registered letters to the mayor of Gary and the police chief saying, look, we're about to publish a story saying, you've got an active serial killer. Please talk to us. And we got nothing from that. And so these women died after I published a story saying there was an active serial killer in Gary.”
— Thomas Hargrove (02:44 & repeated at 24:59)

Julian’s Reaction (26:09):
"I'm angry at them. They sound like the most incompetent police department in the country."

5. Beyond Gary: The Algorithm in Action

• Hargrove founded the Murder Accountability Project: making the data public so “armchair detectives” could help spot serial patterns.
• Example: A layperson using the site helped uncover a pattern involving a suspected serial killer in upstate New York—despite police never securing a confession.

Memorable Anecdote (34:54):
Police, convinced by cluster data, built a Hollywood-style set to interview John White, a suspected serial killer, but never got a confession before he died of a heart attack.

Notable Quote:
"It proves the idea that if people are given access to data and just play with the data, sometimes very good things can happen."
— Thomas Hargrove (38:19)

6. The Scope: How Many Serial Killers Remain Uncaught?

• Hundreds of suspicious clusters exist in the data; not all are serial killers, but many likely are.
• Hargrove estimates at least 2,000 murders may be the work of serial killers who were never recognized as such.

Notable Quote:
"Famously, I have said that there are probably at least 2000 murders that were not recognized as being serial killers."
— Thomas Hargrove (49:20)

7. Law Enforcement Cooperation: A Mixed Bag

• Hargrove says most homicide detectives are supportive and cooperative when shown the data—exceptions exist, but “homicide detectives are very decent people.”
• Makes presentations at homicide investigators' conventions, where officers actively comb their own data for similar patterns.

8. The Limits of Data: International Barriers

• The model’s success depends on access to detailed public data, which is uniquely available in the US but lacking elsewhere—including Australia.
• Hargrove laments governmental reluctance globally to share detailed homicide data, a barrier to adopting similar tools abroad.

Notable Quote:
“If I were murdered, I would want my murder to be as public as possible. I want the whole world to know... And so why governments do this is a mystery to me...”
— Thomas Hargrove (45:25)

9. Challenges with AI and the Algorithm’s Future

• Hargrove experimented with AI models but found them problematic: they’re black boxes, sometimes non-replicable, and violate scientific transparency.
• Still, AI offers potential to reduce errors, yet its opacity remains a concern.

Notable Quote:
“AI is a black box... some AI systems will produce different results according to how you run it... it is in violation of basic scientific principles.”
— Thomas Hargrove (46:11)

10. Ongoing Patterns & The Power of Playing With Data

• Hargrove encourages anyone, anywhere, to explore the cases at murderdata.org.
• Hopes public engagement will spur similar transparency in other nations.

Final Request:
"We hope every day folk will go in and play with the data and we hope that gets replicated in other nations."
— Thomas Hargrove (49:32)

Notable Quotes & Timestamps

• On the emotional distance of analytics:
  “To me, God forgive me, but to me, they were just lines on a spreadsheet... It was a very intellectual exercise.”
  — Thomas Hargrove, (15:27)

• On public data’s power:
  “This ability for the public to get its hands on individual case information is incredibly empowering. And I’m not aware of its equivalent in any other Western nation.”
  — Thomas Hargrove (44:08)

• On the chilling, missed warning in Gary:
  “Logically, that's an impossible statement to make. ‘There are no unsolved serial murders in Gary, Indiana.’ Immediately, red alerts were going up for me.”
  — Thomas Hargrove (17:56)

Timeline of Important Segments

• 02:44 — Hargrove explains the tragic aftermath when warnings go ignored in Gary, Indiana.
• 06:26 — Early career & the origins of his idea to teach computers to recognize serial killings.
• 12:44–15:10 — Testing and proof: LA serial killer “convention” and algorithmic discovery process.
• 16:16–21:00 — Detailed discussion of the Gary, Indiana cluster and hurdles with local law enforcement.
• 24:59–26:44 — The emotional fallout after the killings and official denials.
• 30:15 — Vaughn’s wider confessions and possible connection to Chicago murder clusters.
• 33:49–38:19 — Creation of the Murder Accountability Project and the role of ordinary users.
• 38:45–43:27 — Scope of U.S. hotspots; international data access and barriers.
• 46:00 — AI’s mixed results and scientific drawbacks.
• 47:36–49:24 — Ongoing suspicious cases, regional patterns, and limits of detection.

Memorable Moments

• LA’s “serial killer convention” shock (12:44).
• Hargrove’s regrets and the “what if” after Vaughn’s arrest (24:59).
• The detailed retelling of the John White investigation and the use of a fake police set to secure a confession (34:54).
• Emotional candor about wishing murder data were public everywhere, including if he himself became a victim (45:25).

Closing Thoughts

This episode paints a stark picture of how data can reveal horrors hidden in plain sight―but only if someone looks. Thomas Hargrove’s story is a stirring call for transparency, vigilance, and the fusion of math with empathy to save lives. His journey is both deeply rational and deeply human, marked by elusive victories, haunting regrets, and enduring hope.

Explore or support the project:
https://murderdata.org
— Donations are welcome to help continue their volunteer-based work.

For listeners:
If you’d like to investigate, contribute, or understand more, visit murderdata.org. The data is public and user-friendly—Hargrove hopes others worldwide will replicate this approach to bring justice and recognition where it’s overdue.