Speaker 1: The views and opinions expressed in this podcast are solely
Speaker 1: those of the authors and participants and do not necessarily
Speaker 1: represent those of iHeart Media, Tenderfoot TV, or their employees.
Speaker 1: This series contains discussions of violence and sexual violence. Listener
Speaker 1: discretion is advised. Last time an algorithm. Lorie Townsend more
Speaker 1: in the death of her daughter, Africa Hardy, don't think
Speaker 1: that you know everything about child, because there's something that
Speaker 1: they're not telling you. After moving to Chicago when she
Speaker 1: was nineteen, Africa started escorting. In October, detectives found her
Speaker 1: strangled in the bathtub of an Indiana motel. I just
Speaker 1: think a lot of it, and I think it could
Speaker 1: have been prevented. Years earlier, journalist Thomas Hargrove had learned
Speaker 1: about the concept of linkage blindness. Most connected murders go unrecognized,
Speaker 1: and I kept that in the back of my mind.
Speaker 1: And then when Hargrove discovered an FBI database that tracked homicides,
Speaker 1: it gave him an idea, could we teach a computer
Speaker 1: to identify connected cases to find serial killings? From my
Speaker 1: Heart Media and Tenderfoot TV, this is algorithm I'm ben
Speaker 1: Keebrick I mentioned in the last episode that Africa's case
Speaker 1: flipped what I knew about crime on its head this
Speaker 1: episode you'll see why. Let's jump back to my conversation
Speaker 1: with Thomas Hargrove. So, at what point in my um
Speaker 1: you were saying that we shouldn't ignore her. Grove told
Speaker 1: me that back in two thousand four, when he was
Speaker 1: working as an investigative reporter in Washington, d C. He
Speaker 1: had come across a database with information about homicides all
Speaker 1: across the US. He wondered whether an algorithm could find
Speaker 1: patterns within that data and to text serial killers. Maybe
Speaker 1: it could even be used to detect active serial killers
Speaker 1: who had not yet been caught. I did not know
Speaker 1: that such an algorithm was possible. I was going on faith.
Speaker 1: I did believe that it was important, and I did
Speaker 1: believe that it could save lives, but I didn't know
Speaker 1: that it would work. I begged my editors, let me
Speaker 1: try this. Let's do a project looking at murder, with
Speaker 1: the understanding that the actual goal is to see if
Speaker 1: we could create a computer program that would identify serial murder.
Speaker 1: But remember this was back in two thousand four, years
Speaker 1: before people were talking about things like machine learning or
Speaker 1: the power of big data. Because Harder was on the
Speaker 1: cutting edge of these ideas, it was sometimes difficult to
Speaker 1: get other people to understand him or to take him serious.
Speaker 1: Lee My editors recognized that this could be something very cool,
Speaker 1: that this could make news, but it's hard to commit
Speaker 1: to a project you don't know upfront whether it's possible. Ultimately,
Speaker 1: his editors told him that trying to design an algorithm
Speaker 1: sounded too risky. It's normal for story pitches, especially ambitious
Speaker 1: ones like this, to get rejected, but what's unusual is
Speaker 1: that Hargrove didn't give up For the next six years.
Speaker 1: He kept pitching the story six years, and he says
Speaker 1: that his editors always considered it but would end up
Speaker 1: assigning him to another safer story. Then, in two thousand ten,
Speaker 1: Hargrove got his big break. He had just published a
Speaker 1: provocative piece called Saving Babies, exposing sudden infant death. Hargrove
Speaker 1: had analyzed data from Corners offices all across the US
Speaker 1: and found that many times when infant deaths were listed
Speaker 1: as sid's sudden infant death syndrome, the evidence actually showed
Speaker 1: that the infants had died of accidental cufifocations. Most of
Speaker 1: the time, babies don't die mysteriously. They die from avoidable
Speaker 1: accidents and unsafe sleeping conditions. They got covered up because
Speaker 1: of what was intended to be a very kindly diagnosis
Speaker 1: called SIDS. That was meant to be a kindness to parents.
Speaker 1: There was a mistake. This project prompted a national conference
Speaker 1: to re examine infant death. It caused the creation of
Speaker 1: a sudden Infant Death monitoring system by the CDC. I mean,
Speaker 1: it was tremendously successful. It had got rave attention. Finally,
Speaker 1: after six years, my stock was high enough in the
Speaker 1: newsroom that I was able to get them to look
Speaker 1: at murder. My editors that, okay, Tom, you've got a year.
Speaker 1: I could do a national reporting project looking at unsolved murder,
Speaker 1: with the understanding that what we were really about was
Speaker 1: to try to develop a statistical means to identify overlooked
Speaker 1: serial murder. Could we teach a computer to find serial killings?
Speaker 1: After years of dreaming about making this algorithm, now Hargrove
Speaker 1: had this chance to try and make it work. Hargrove
Speaker 1: starting to design a computer program, one that could comb
Speaker 1: through the five hundred thousand supplemental homicide reports and find
Speaker 1: patterns between victims, patterns that might suggest the work of
Speaker 1: a serial killer. So what is an algorithm? These days?
Speaker 1: Algorithms dictate much of our digital lives. They determine what
Speaker 1: TV shows get suggested to us, post we see on Facebook,
Speaker 1: what route our GPS takes us on. Often these algorithms
Speaker 1: are black boxes. We're not sure what data is being
Speaker 1: fed into them and how they're deciding what to spit
Speaker 1: back out. But at their core, algorithms aren't mysterious. They're
Speaker 1: not even really that complicated. Algorithms are just sets of
Speaker 1: instructions used to accomplish a specific task. You could say
Speaker 1: a cake recipe is an algorithm for baking a cake.
Speaker 1: You wouldn't say that, but you could. But while following
Speaker 1: an algorithm might be simple, designing a whole new algorithm
Speaker 1: is trickier. It wasn't like Cargroove was just baking a
Speaker 1: cake from scratch. It was more like he was trying
Speaker 1: to invent a brand new dessert. What we were doing
Speaker 1: with the algorithm was the process literally of trial and error.
Speaker 1: The ability to experiment with data was critical. If you're
Speaker 1: trying to come up with a new dessert, might experiment
Speaker 1: with ingredients until you come up with a combination that
Speaker 1: tastes good. Hargrove knew what ingredients he had to play with.
Speaker 1: That was the data from the supplemental homicide report, which
Speaker 1: listed the weapon that was used he was shot with
Speaker 1: a handgun, the time and location of the murder January,
Speaker 1: and the age, race, and sex of the victim. Victim
Speaker 1: is a blackmail eighteen years old. But as he experimented
Speaker 1: with how an algorithm might sort through that data, Hargrove
Speaker 1: needed a way to check if he was on the
Speaker 1: right track. So we decided to test each new prototype
Speaker 1: of his algorithm to see if it could detect a
Speaker 1: known serial killer. We used as our test bed the
Speaker 1: forty eight victims of serial killer Gary Ridgeway, so called
Speaker 1: Green River Killer. He was convicted convicted in a court
Speaker 1: of law of murdering forty eight girls and women in
Speaker 1: the Seattle area. The question was, we'd teach a computer
Speaker 1: a process that would tell us that something god awful
Speaker 1: happened in Seattle. At the time, Gary Ridgeway was thought
Speaker 1: to be the most prolific serial killer in America. Her
Speaker 1: group figured if he could create an algorithm that could
Speaker 1: detect Ridgeway. Maybe it would also detect other serial killers
Speaker 1: that had gone unrecognized. To motivate himself, Hargrove stuck up
Speaker 1: a picture of Gary Ridgeway in his office. It was
Speaker 1: one of his booking photographs. He was glowering and looked
Speaker 1: like it was glowering at me. Under that picture, I
Speaker 1: typed the headline, what do serial victims look like? Statistically?
Speaker 1: For over two decades, Ridgeway had killed women and dumped
Speaker 1: their bodies along the Green River outside Seattle. In most cases,
Speaker 1: by the time anyone had discovered those corpses, they were
Speaker 1: already skeletal. Ridgeway has snuffed out does sense of lives.
Speaker 1: He'd killed women and girls with their own hopes and dreams.
Speaker 1: But Hargrove needed to approach their deaths like the algorithm would,
Speaker 1: looking at their cases by their numbers, using only the
Speaker 1: data from the FBI's supplemental homicide reports. So what did
Speaker 1: ridgeways victims look like? Statistically? And more broadly, what do
Speaker 1: a serial killer's victims look like? To find out, I
Speaker 1: reached out to one of Hargrove's collaborators, Dr Mike A. Mott.
Speaker 1: A Mott is a forensic psychology professor at Radford University,
Speaker 1: and he curates the world's largest database of serial killers
Speaker 1: and their victims. If you look at how serial killers
Speaker 1: are portrayed in movies or on TV, the stereotypes not
Speaker 1: really very consistent with with the back Are these also
Speaker 1: stereotypes that law enforcement might have when they're trying to
Speaker 1: decide could this murder possibly be a serial murder? Well,
Speaker 1: law enforcement certainly has the same stereotypes. When we've done
Speaker 1: presentations to law enforcement groups and even clinical psychologists that
Speaker 1: are police psychologists, they're very surprised at the results. I'm
Speaker 1: talking to Professor Mike al Mott about what serial murders
Speaker 1: look like statistically, because statistical differences between serial killings and
Speaker 1: other homicides are the kind of signatures and algorithm could
Speaker 1: use to detect a serial killer. Professor Amatt has built
Speaker 1: a database with information on thousands of serial killers, and
Speaker 1: he told me that there are many misconceptions about these killers,
Speaker 1: even within law enforcement and the true crime community. The
Speaker 1: stereotypes about the profile of a serial killer or pro
Speaker 1: file of the victims is not really very consistent with
Speaker 1: the facts. If we want to catch serial killers, we
Speaker 1: need to know who they really are in the reality
Speaker 1: of their crimes. I want you to stop for a
Speaker 1: moment and try to conjure up a mental image of
Speaker 1: a typical serial killers victim. How old are they, what
Speaker 1: do they look like? Now, imagine all of the victims
Speaker 1: of a single killer. What would you guess they have
Speaker 1: in common with one another? If we look at the
Speaker 1: victims of serial killers, of the victims or women or
Speaker 1: forty nine or men, so there's not really much of
Speaker 1: a difference there. One of the stereotypes about serial killers
Speaker 1: is that they have a type, you know, kinde of
Speaker 1: victim that they're going to kill. The most consistent profile
Speaker 1: we can have is really in the age category of
Speaker 1: who they're killing. About are going to kill people that
Speaker 1: are in the same age categories, so it's children, or
Speaker 1: its teens, or its adults or elderly. Serial Killers are
Speaker 1: less consistent when it comes to other attributes of their victims.
Speaker 1: For example, only six kill victims there are all male
Speaker 1: or all female. And then if you look at race,
Speaker 1: for example, of serial killers only kill somebody of the
Speaker 1: same race. And while the stereotypical victim of a serial
Speaker 1: killer is white, actually a third of victims or people
Speaker 1: of color. For Gary Ridgeway, the serial killer that Hargrove
Speaker 1: picked as his test case, all his known victims were
Speaker 1: young women, but they were diverse in terms of race.
Speaker 1: In an interview with an FBI agent, Ridgeway says he
Speaker 1: targeted prostitutes out of convenience, and studies show that there's
Speaker 1: actually been a dramatic rise in serial killers targeting sex workers.
Speaker 1: In the seventies, prostitutes are thought to have made up
Speaker 1: around six of the female victims of serial killers. By
Speaker 1: the two thousands, more than two thirds of women murdered
Speaker 1: by serial killers were sex workers. Many serial killings are
Speaker 1: sexual in nature. About one third of serial killers rape
Speaker 1: at least one of their victims. Ridgeway told detectives he'd
Speaker 1: have sex with his victims before he strangled them, and
Speaker 1: when asked why he chose to choke all his victims,
Speaker 1: Ridgeway replied because that was more personal and more rewarding
Speaker 1: than shooting them. And compared to typical murderers, serial killers
Speaker 1: are more likely to use methods that are up close
Speaker 1: and personal, like strangulation or bludgending. So if we're looking
Speaker 1: at victims in the US about were shot, were strangled
Speaker 1: were stabbed and ten percent were blugend and serial killers
Speaker 1: tend to be consistent in the method they used to kill.
Speaker 1: Amont says that two thirds of serial killers use only
Speaker 1: a single means to kill their victims. So what are
Speaker 1: the takeaways from a MOOTS data? Serial killers show some
Speaker 1: but not perfect consistency in terms of their victims age, race,
Speaker 1: and sex, and also in their method of killing. But
Speaker 1: it turns out that there's one more thing that serial
Speaker 1: homicides tend to have in common, a property that would
Speaker 1: be crucial for hard Groves algorithm. We spent the summer
Speaker 1: of two thousand and ten finding at least the hundred
Speaker 1: procedures that crashed and burned. Does the presence of a
Speaker 1: serial killer increase the rated which women are murdered? No?
Speaker 1: Does it increase the rate of which women are murdered
Speaker 1: through unusual means? No? And do you get any indications
Speaker 1: that you're getting closer? Yeah, So as we were progressing
Speaker 1: through the hundred and one things that don't work, we
Speaker 1: were starting to get closer. So um, the last thing
Speaker 1: was to look at what the clearance rate was for
Speaker 1: particular types of weapons. That term clearance rate refers to
Speaker 1: the percentage of cases that police end up arresting someone
Speaker 1: for the crime. Hard Group realized that Ridgeway had gotten
Speaker 1: away with his murders for so long that they've been
Speaker 1: listed as unsolved in the database. So if you looked
Speaker 1: at killings in Seattle that matched his method, the number
Speaker 1: of cases police had cleared was much lower than expected.
Speaker 1: The presence of an active serial killer often destroys the
Speaker 1: batting average for the local police department. They're able to
Speaker 1: solve most murders, but not that type of murder because
Speaker 1: there's a serial killer who's avoiding arrest hert group was
Speaker 1: making progress. He'd picked up the hint of a signal
Speaker 1: from Bridgeway in Seattle, but it wasn't enough to make
Speaker 1: the algorithm useful as a tool. He racked his brain
Speaker 1: trying to come up with a way to improve it.
Speaker 1: One day, near the end of the summer, he looked
Speaker 1: up at Ridgeway's mug shot and asked himself again, what
Speaker 1: do a serial killers victims look like? Statistically? As he
Speaker 1: talked through the project one last time with his research assistant,
Speaker 1: Liz Lucas, an idea struck him. As I was taking
Speaker 1: Liz to the airport because she had to go home
Speaker 1: to defend her master's thesis. I told her that what
Speaker 1: might work, and we're gonna try this next is a
Speaker 1: kind of cluster analysis. Instead of querying all of the data,
Speaker 1: we tried to assemble the data into smaller clusters according
Speaker 1: to the county where the murderer occurred, the gender of
Speaker 1: the victim, the method of killing, and at that time
Speaker 1: age group. Then we calculated what the clearance rate was
Speaker 1: for each cluster, how many of those murders were solved.
Speaker 1: Up until this point, he tried looking at the data
Speaker 1: for all the orders in Seattle. His new idea was
Speaker 1: to further break down the data. Have the algorithm split
Speaker 1: up the homicides for each county into buckets of victims,
Speaker 1: and then rank these different buckets by the percentage of
Speaker 1: cases that were solved. And Hargrove wasn't just doing this
Speaker 1: for Seattle. He was doing this for data all across
Speaker 1: the US, and he was hoping that out of the
Speaker 1: thousands of clusters, Ridgeways Seattle killings would show up somewhere
Speaker 1: near the top of the list. Hargrove ran his new
Speaker 1: algorithm and waited anxiously for the result. When we did that,
Speaker 1: the Green River killings jumped out plane as Day came
Speaker 1: in third place most of the time seventies seven percent
Speaker 1: of the time, and arrest is made when a woman
Speaker 1: is killed in the cluster where the Green River killings
Speaker 1: were grouped. The solution rate was less than and that
Speaker 1: was our key. We're looking for groups of similar murders
Speaker 1: that have very low clearance rates. We had hundreds of
Speaker 1: results all over the country, highly suspicious clusters, and we
Speaker 1: started investigating them. Heart Grove was a static. It seemed
Speaker 1: like his algorithm might be working. It detected Gary Ridgeway,
Speaker 1: the Green River killer, but at the same time, he
Speaker 1: designed the algorithm to detect Ridgeway, and he kept tweaking
Speaker 1: it until he did. So maybe the algorithm had just
Speaker 1: detected Ridgeway by luck and it wouldn't generalize to other killers.
Speaker 1: In statistics, this is a problem called overfitting, and it's
Speaker 1: a common problem when scientists try to make algorithms that
Speaker 1: predict things. So to get around this problem of overfitting,
Speaker 1: people often train their algorithms with one set of data
Speaker 1: and then validate the algorithm by testing it with new data. So,
Speaker 1: since Hargrove had been using homicides in Seattle. To train
Speaker 1: his algorithm, he needed to look at other cities to
Speaker 1: see if it was really working. There were two larger
Speaker 1: suspicious clusters, and in first place was Los Angeles. They
Speaker 1: were a large group of almost entirely African American women
Speaker 1: who were killed by handguns, and those murders had a
Speaker 1: very low solution rate. So I assembled a spreadsheet of
Speaker 1: those murders and emailed them to the public relations department
Speaker 1: of the l a p D. Got one of the
Speaker 1: representatives on the phone, is there a chance that any
Speaker 1: of these could be serial murders? And he spent a
Speaker 1: minute or so looking through the files, and then he
Speaker 1: can back and said, what are you kidding? They're all
Speaker 1: serial killings? I said, what. In fact, in the es
Speaker 1: l ap D established what they hoped would be a
Speaker 1: secret task force. They didn't want to alarm the public,
Speaker 1: but they were exploring the possibility that there could be
Speaker 1: a serial killer active. They called it the South Side
Speaker 1: Slayer Task Force. Well, it turned out that the task
Speaker 1: force was misnamed. It should have been called the South
Speaker 1: Side Slayers Task Force. Because they had five they were
Speaker 1: all quite independent of each other. They didn't know each
Speaker 1: other but they were all killing women over a period
Speaker 1: of twenty years. You heard that right. During the eighties
Speaker 1: and nineties, Los Angeles had at least five different serial
Speaker 1: killers who were shooting, strangling, and sexually assaulting women in
Speaker 1: the area. This was happening at the height of the
Speaker 1: crack epidemic and at a time when homicides across the
Speaker 1: US where peaking. The large number of overall homicides probably
Speaker 1: helped cover up the fact that there were these serial
Speaker 1: killers operating in l A. But it's also likely that
Speaker 1: the police didn't give these murders enough attention due to
Speaker 1: a phenomena that's sometimes called victim discounting, and this is
Speaker 1: the tendency to ignore crimes targeting marginalized groups. But when
Speaker 1: Hargrove found out about these l A serial killers, he
Speaker 1: took this as another sign that the algorithms seemed to
Speaker 1: be working. It had detected a group of confirmed serial
Speaker 1: killers that he hadn't even been aware of. Hargrove continued
Speaker 1: down his list of top clusters that the algorithm had identified.
Speaker 1: He called up the police department in Youngstown, Ohio, and
Speaker 1: left a long message where he tried to explain the
Speaker 1: algorithm and how it had detected a cluster of murders
Speaker 1: in Youngstown. Thirty minutes later, the phone rings the chief
Speaker 1: of detectives and you gotta give him credit, answering a
Speaker 1: voicemail like that, he's a young guy. He called back
Speaker 1: and said, that was the damnedest message I ever had
Speaker 1: on my phone. And so I went back and I
Speaker 1: interviewed my senior detectives and they told me something I
Speaker 1: did not know. We thought we had a serial killer
Speaker 1: in the nineties. We definitely thought we had one, and
Speaker 1: we never got him, and so we started a new investigation.
Speaker 1: He was attempting to locate the rape kids from those cases,
Speaker 1: to try to DNA type all of the rape kits
Speaker 1: they could find. Unfortunately, and this was very embarrassing, the
Speaker 1: rape kits had all been destroyed, the property rooms had
Speaker 1: been cleaned out, and he was not able to get
Speaker 1: any of the kids from his cases or from surrounding jurisdictions.
Speaker 1: There were other similar murders in neighboring jurisdictions, but they
Speaker 1: too did not retain the rape kids. So it was
Speaker 1: it was very sad, but they gave it the old
Speaker 1: college try again. The algorithm had identified a cluster of
Speaker 1: murders that seemed like it was the work of a
Speaker 1: serial killer. Police couldn't prove that the killings were connected,
Speaker 1: but the algorithms findings lined up with what police suspected,
Speaker 1: and the algorithm had inspired a reinvestigation of those cold cases.
Speaker 1: Hargrove felt like he was on the right track and
Speaker 1: that maybe the algorithm could be a useful tool for
Speaker 1: law enforcement. We selected ten major cities that appeared to
Speaker 1: have a suspicious number of algorithm identified murders. Gary was
Speaker 1: one of those ten. Gary, Indiana, the city right next
Speaker 1: door to where Africa Hardy would be strangled. Four years later,
Speaker 1: the algorithm flagged fifteen all unsolved murders in the Gary,
Speaker 1: Indiana area. They were all women who were strangled. Not
Speaker 1: one of the cases were solved, which is unusual. Seventy
Speaker 1: seven percent of female murders get cleared, but not one
Speaker 1: of these fifteen strangulation murder and Gary were cleared. I
Speaker 1: called the public relations officer for the Gary Police Department,
Speaker 1: gave him my name and said what we had found,
Speaker 1: and said, is there a chance that you're dealing with
Speaker 1: a serial killer? The next day the phone rings, it's
Speaker 1: uh as I recall His name was Captain Roberts, who said, UM,
Speaker 1: I've checked with our detectives and I can tell you
Speaker 1: definitively that there are no unsolved serial murders in Gary, Indiana,
Speaker 1: which is by definition an impossible statement to make. Unless
Speaker 1: you have no unsolved murders, you cannot claim you'd be
Speaker 1: definitively certain that there are no unsolved serial murders. Hargrove
Speaker 1: felt like the police were just blowing him off, so
Speaker 1: he started investigating the fifteen murders himself. Hargrove wanted to
Speaker 1: know whether the algorithm was working, and he thought that
Speaker 1: if he found evidence suggesting the murders were connected, maybe
Speaker 1: the police would start take came seriously. But looking up
Speaker 1: the information about the cases wasn't easy. The FBI supplemental
Speaker 1: homicide report didn't include the victim's names or even the
Speaker 1: exact date of their death. It just listed a month
Speaker 1: in a year. So define more details about the cases,
Speaker 1: Hargrove had to meticulously dig through old issues of local papers,
Speaker 1: and as he started to piece things together, he was
Speaker 1: unsettled by what he found. He was disturbed not just
Speaker 1: by how grotesque these murders were, but by the patterns
Speaker 1: that seemed to link them, and he became convinced that
Speaker 1: Northwest Indiana had a serial killer on the loose, a
Speaker 1: strangler who was targeting young women, women just like Africa Hardy.
Speaker 1: He tried to talk to Gary's chief of police, but
Speaker 1: he couldn't get through. I continued to suggest, have you
Speaker 1: really looked at these cases? Soon the police stopped returning
Speaker 1: his calls altogether, and Hargrove needed a response. He wasn't
Speaker 1: just playing arm chair detective. We were about to publish
Speaker 1: a story saying that Gary, Indiana has a serial killer
Speaker 1: and the police would not talk about it. We were
Speaker 1: afraid that the reason they weren't talking to us was
Speaker 1: because they were hot on the heels of solving it.
Speaker 1: That they had a suspect that we're trying to reel
Speaker 1: them in, and we were going to screw that up.
Speaker 1: We needn't have worried, but um, that was our fear.
Speaker 1: I even sent registered letters to the chief of police
Speaker 1: and to the mayor saying what we were about to do,
Speaker 1: and if there's any issue they have, or any conversation
Speaker 1: they want to have, for heaven's sakes, call me. This
Speaker 1: is the letter that I wrote to the chief of
Speaker 1: Police and Mayor in Gary, Indiana, and it goes dear Chief.
Speaker 1: Carter Scripts Hour news service based in Washington, d C
Speaker 1: is conducting a national reporting project looking into the thousand
Speaker 1: unsolved thomicides committed in the United States since night. As
Speaker 1: part of this project, we are investigating whether it's possible
Speaker 1: to spot victims of serial murder among these unsolved killings.
Speaker 1: Using the FBI's Supplementary Homicide Report. Using multivariate analysis, we've
Speaker 1: determined that Gary, Indiana has an elevated number of unsolved
Speaker 1: murders of women who were strangled in recent years. The
Speaker 1: data that your department reported to the FBI are consistent
Speaker 1: with the possibility that multiple victim killers have operated in
Speaker 1: northwestern Indiana. Broadly, we see two possible patterns. In recent years,
Speaker 1: several women have been strangled in their homes. In at
Speaker 1: least two cases, of fire was set after the women
Speaker 1: were killed. Also, starting in the nineteen nineties, we've seen
Speaker 1: several women who were found strangled in or near abandoned buildings.
Speaker 1: We doubt these killings are the result of convicted serial
Speaker 1: killer Eugene V. Brit who admitted to killing eight people.
Speaker 1: Please note the attached list of homicide victims. We'd be
Speaker 1: grateful if your detectives would review these cases to determine
Speaker 1: if any might have a common perpetrator. The US Justice
Speaker 1: Department defines a serial killer simply as anyone who kills
Speaker 1: two or more people in separate incidents. Experience thomicide investigators
Speaker 1: tell us it's extremely difficult to spot a serial murderer.
Speaker 1: There have been enough unsolved killings of women in Gary,
Speaker 1: Indiana that your metropolitan area made our top ten list.
Speaker 1: We are contacting authorities in all ten areas. Police and
Speaker 1: five cities have already confirmed that cases we've cited contained
Speaker 1: proven or suspected victims of serial murder. We are also
Speaker 1: making a similar request to the Lake County Coroner's office.
Speaker 1: Thank you for your time and consideration. Please help us
Speaker 1: explore whether national crime data can assist local law enforcement. Sincerely,
Speaker 1: Thomas Hargrove, Scripts Hour News service, and we had total
Speaker 1: radio silence from those people. The Gary police chief and
Speaker 1: mayor didn't respond to Hargrove's letter or to his follow
Speaker 1: up phone calls, but Hargrove had also sent the letter
Speaker 1: to the county corners corners or the public officials who
Speaker 1: oversee autopsies and determined causes of death. They work with
Speaker 1: the police, but they're an entirely separate entity. When I
Speaker 1: called the Lake County Coroner's Office, identified myself, I'm Tom
Speaker 1: Hargrove calling from Washington, d C. Oh just a minute,
Speaker 1: Mr Hargrove, the senior deputy corner, wants to talk to you.
Speaker 1: He came on and said, Mr Hargrove, we got your
Speaker 1: packet of information. Thank you very much for sending it.
Speaker 1: I'm assigning it to one of our assistant corners, a
Speaker 1: lady named Jackie. We're going to have her look into it.
Speaker 1: An entirely different reception them when I didn't get it Gary,
Speaker 1: they agreed with us that there were too many unsolved murders.
Speaker 1: She added three more cases that she thought belonged on
Speaker 1: that pile we had identified fifteen. She added three, making
Speaker 1: eighteen that she thought were connected and was trying to
Speaker 1: have a conversation with the Carry Police department. She's never
Speaker 1: gone on Mike to talk about this case. For the
Speaker 1: Coroner's office, it is very, very difficult to speak ill
Speaker 1: of a police department. It's considered bad form, and so
Speaker 1: she probably still feels a reluctance to do that. Although
Speaker 1: she has passion about this case, you shouldn't use this
Speaker 1: recording where I named her unless she agrees. I was
Speaker 1: looking up other names of people in that department, you know.
Speaker 1: I think one went down for some kind of corruption charge,
Speaker 1: and then it's kind of hard to find someone who
Speaker 1: is there that can talk about this stuff. Yeah, now
Speaker 1: your your only hope is to get Jackie to talk.
Speaker 1: This is really one of the most frustrating experiences of
Speaker 1: my life. I think the Gary Police Department should be
Speaker 1: looking at some of those old cases. They still may
Speaker 1: have a killer out there. When I finished speaking with
Speaker 1: hard Grove, I tried calling Jackie, but I couldn't get through.
Speaker 1: After a couple of failed attempts, I left her a
Speaker 1: voicemail explaining the podcast how I was trying to look
Speaker 1: into the murders Hargrove had identified to see if they
Speaker 1: were indeed connected to Africa's death. Weeks passed, and I've
Speaker 1: worked on other stories and chased down other leads, and
Speaker 1: after what Hargrove had told me about her reluctance to
Speaker 1: talk about the case, I didn't think she would want
Speaker 1: to speak to me. Then one morning I woke up
Speaker 1: to a missed call from a number I didn't recognize. Hey, Don,
Speaker 1: this is Jackie. You would try to contact me a
Speaker 1: while back in regard to heart Grow story you're doing. Yes,
Speaker 1: you want to give me a call later next week? Um,
Speaker 1: that would be fine. Sorry, had gotten back to you sooner.
Speaker 1: It's just everything is fine, a little different. All right, guys,
Speaker 1: I'll talk to you soon. Maybe that's coming next episode.
Speaker 1: They don't want to talk about it either, I assume
Speaker 1: because they don't want to embarrass their neighboring police agency.
Speaker 1: All right, and said here and they had nothing to do.
Speaker 1: They led to the death your friend. You should try
Speaker 1: to find out. You'd be the first to do that.
Speaker 1: Algorithm is released weekly on Tuesday's Subscribe Now so you
Speaker 1: don't miss the next episode on the I Heart Radio app,
Speaker 1: Apple Podcasts, or wherever you get your favorite shows. This
Speaker 1: episode was written and produced by me ben Key. Brick.
Speaker 1: Algorithm is executive produced by Alex Williams, Donald Albright, and
Speaker 1: Matt Frederick. Production assistance in mixing by Eric Quintana. The
Speaker 1: music is by Makeup and Vanity Set and Blue Dot Sessions.
Speaker 1: Thanks to Christina Dana Miranda Hawkins. Jamie Albright, rema El Kaili,
Speaker 1: Trevor Young, and Josh Thane for their help and notes. Again,
Speaker 1: thanks for listening as it heads up. I'm still working
Speaker 1: on this podcast as we release it, so any feedback
Speaker 1: is appreciated. I think Algorithm is going to address some
Speaker 1: really important issues about policing and how crimes are investigated
Speaker 1: that don't receive enough attention. So if you can, please
Speaker 1: leave a review on Apple Podcasts or tell a friend
Speaker 1: about Algorithm, where brand new show and could really use
Speaker 1: your help.