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Advanced data mining techniques enabled by artificial intelligence are unlocking new leads in cold case investigations. By analyzing disconnected information in case files and external databases, algorithms can spot correlations that human investigators may have overlooked. This provides critical momentum to stalled or abandoned investigations by surfacing promising new directions.
One area where data mining delivers major dividends is linking crime scene evidence to potential suspects. For example, DNA evidence left at a crime scene can now be rapidly compared against criminal databases using AI to identify genetic matches. This instantly prioritizes persons of interest that detectives can pursue. Even partial DNA hits can generate suspects by searching genealogy databases like GEDmatch. This technique was used to crack two cold cases in 2018, leading to the arrests of men suspected of multiple rapes and murders dating back decades.
AI systems can also data mine firearm registries to identify weapons matching ballistic evidence from a shooting. Scanner technology can now detect fingerprints on casings missed in initial analysis. Combining this data provides new suspect leads by linking guns purchased by specific individuals to cached fingerprint records. Similar techniques have matched fibers and materials found at crime scenes to product databases, pointing to suspects whose clothing or tools left evidence behind.
By indexing and cross-referencing vast document troves, algorithms also discover unexpected connections in written case records. Analyzing outdated investigation reports against updated databases frequently surfaces new informational threads to pull. Even handwritten notes can now be parsed by AI using optical character recognition and natural language processing. This has unearthed overlooked tips and witness statements pointing towards potential suspects or accomplices.
The exponential increase in digital data from smartphones, social platforms and internet-connected devices provides a treasure trove of information to mine. AI analytics can pinpoint location patterns, relationships and communications via metadata that situates suspects in time and space. This amplifies the evidentiary potential from both existing and newly acquired case data.
Machine learning is enabling breakthroughs in DNA analysis that are reinvigorating cold case investigations. By training algorithms on large datasets of genetic samples, researchers have developed more accurate and efficient methods for extracting insights from DNA evidence. This expands the possibility of establishing definitive genetic links between crime scene samples and potential suspects.
One area seeing dramatic gains is improving the analysis of degraded or limited DNA samples. Old or damaged blood, semen and skin cells often do not contain intact DNA chains. This restricts the markers detectable with traditional PCR analysis. But AI systems can reconstruct partial genetic profiles by recognizing patterns among carbon bonds still present in degraded strands. This allows conclusive hits from samples previously thought to be unusable.
Researchers at the University of California, Davis demonstrated a machine learning technique that boosted identification rates for degraded DNA samples by up to 70%. Their method uses neural networks to complete fragmented genetic profiles by comparing trillions of sequence patterns. This has been able to yield complete STR profiles from DNA too damaged for normal PCR testing.
Machine learning also shows promise for accurately predicting physical traits from crime scene DNA. Chinese researchers developed an AI system that can determine an individual's ancestry and facial shape just from their genetic code. This "DNA phenotyping" can provide investigators with a basic profile to filter potential suspects. While only providing general biogeographic ancestry at this stage, the technology is rapidly improving.
DNA mixture analysis is another area being advanced by AI. Machine learning algorithms can decipher complex mixtures of multiple individuals' DNA better than traditional methods. Rather than just identifying major contributor profiles, these systems can reconstruct complete genotypes for minor contributors too. This allows key evidence from multiple assailant crimes, like sexual assaults, to be conclusively matched to suspects.
Digging into decades old case files often yields reams of disorganized, handwritten documents full of potentially valuable clues. ButCombing through boxes of notes, letters, and reports to pinpoint that one key detail can be an exercise in futility for time-strapped detectives. This is where natural language processing artificial intelligence can make quick work of cold case document review.
By using algorithms that can read and interpret human language, NLP systems can rapidly analyze huge volumes of unstructured text. This allows them to surface notable names, dates, locations, relationships, events, and other facts that may relate to a crime. While a human would struggle to connect the dots, NLP models can instantly identify patterns and anomalies that deserve a closer look.
Researchers at the University of Lausanne in Switzerland demonstrated how NLP algorithms can expedite cold case investigations. They trained a system on old police reports that learned to extract event timelines about criminal activities. By analyzing document context, the AI could reconstruct sequences of events from fragmented notes across multiple files. This chronological assembly of clues provides crucial insight into the relationships, motives, and opportunities of suspects.
Dutch police have also used NLP algorithms to reexamine decades of files on nearly 1,900 unsolved serious crimes. Uploading all digitized documents, the system looks for similarities that may link crimes and identify crossover suspects. It also translates slang, jargon, nicknames and other language patterns that may have stumped previous investigators. Patterns in speech, dialect, and vocabulary can indicate social circles involved in multiple cases.
Closer analysis of language use can even provide clues about a suspect's background and personality. Analyzing word choice, grammar patterns, and semantic context, NLP algorithms can infer attributes like gender, age, education level, cultural affiliations, and cognitive traits. This allows investigators to refine profiles of persons of interest who may have only been identified by partial DNA or aliases.
With the massive proliferation of digital visual evidence, image recognition technology enabled by artificial intelligence is transforming cold case investigations. Advanced algorithms can meticulously analyze crime scene photos, CCTV footage, and video evidence that overwhelmed original detectives. Machine learning models trained on vast databases of images can also identify vehicles, weapons, locations, and physical features in low resolution CCTV frames previously thought unusable.
One area generating fresh leads is using AI facial recognition to reexamine visual evidence. Even with partial images from dated security footage, advanced neural networks can precisely match facial shapes, distances, and features. This can definitively place suspects in critical locations when crimes occurred decades ago. Facial recognition has linked serial offenders to multiple unsolved crimes after scanning historical CCTV footage from various cities.
Enhanced image processing can also extract plate numbers from poor resolution traffic cameras and match vehicles to suspects based on historical DMV records. Objects like clothing, bags, tools, and weapons captured visually can provide new suspect details by analyzing fabrics, logos, shapes, and stamps. Image enhancement techniques make faded tattoos more legible, allowing database searches for connected gang members.
Researchers at the Netherlands Forensic Institute and Police developed an AI technique to accurately estimate the height of suspects from security camera footage. By analyzing individuals' posture, limbs, and positional scale, their model can determine height to within 4 inches. This provides a vital physical descriptor for generating leads when only video evidence from a crime scene exists.
Artificial intelligence is even re-examining visual evidence that original investigators dismissed as irrelevant. Previously unknown suspects have been identified by re-analyzing crime scene backgrounds. Subtle indications of accomplices or witnesses captured peripherally in photos allow new lines of inquiry via facial recognition. Details like reflections in mirrors, shadows, or background noises unnoticed by detectives can direct case progress when re-examined by AI.
Algorithms are also spotting visual anomalies that human examiners originally misinterpreted. In a suspected strangulation case, pruning filters highlighted petechiae patterns on the victim's face that supported a homicide conclusion, despite initial ruling of death by natural causes. The ability of AI vision to suppress distracting data and focus on minute evidentiary details makes old photos a treasure trove.
Predictive analytics powered by artificial intelligence are enabling law enforcement to uncover and prioritize new suspect leads in challenging cold cases. By analyzing crime scene evidence and case histories with advanced machine learning algorithms, investigators can now generate profiles of the type of individuals statistically most likely involved. This moves the process forward by focusing efforts on persons of interest that data indicates have the highest probability of being responsible.
Predictive models work by detecting subtle patterns in existing evidence and case data. Algorithms are trained to recognize clusters of criminal behaviors, locations, relationships, timelines, physical traces, and other quantitative data points that are correlated with specific types of offenders. Models become increasingly accurate through feedback loops - successful leads and arrests continue improving the system's ability to spotlight promising new culprits.
A pioneering application of predictive analytics comes from chief data scientist Brent Schroeder of the Clark County Sheriff's Office in Vancouver, Washington. Schroeder developed a machine learning algorithm to probe cold cases by identifying similarities to solved murders based on victim and offender demographics, crime scene variables, weapon data, and circumstances. This highlighted "statistical doppelgÃ¤ngers" - cold cases bearing striking resemblance to already resolved murders. Focusing investigation on offenders linked to these analogous solved crimes generated groundbreaking new suspect leads.
New York University researchers take this concept even further, training AI to estimate the probability a given individual committed a specific murder based solely on crime scene forensics and victimology. Computer vision analyzes injuries while natural language algorithms parse medical examiner reports. The system then compares this data to population-level models that identify which combinations of age, gender, social status, past crimes, relationship types, and motives are most predictive of homicidal violence. This narrows high-potential suspects by over 160 times compared to traditional policing methods.
Of course, law enforcement is careful to emphasize that predictive analytics do not actually identify or accuse specific perpetrators. The algorithms simply surface persons meriting exploration based on factual data correlations. Any leads require old-fashioned police work to investigate further. And safeguards are implemented to remove racial, gender, and socioeconomic biases from influencing the AI models. But used responsibly, predictive analytics guide detective work out of dead ends towards more promising hypotheses.
Sentiment analysis, a branch of natural language processing that extracts emotional signals from text, is emerging as a powerful tool for charting the complex social dynamics surrounding cold case crimes. By parsing written witness statements, suspect interviews, journals, letters, emails, and other documents, advanced algorithms can map out the interpersonal attitudes, tensions, and power structures that may have catalyzed criminal acts.
This capability allows investigators to re-visualize relationships from the emotional perspectives of those involved at the time. Subtle details around family conflicts, romantic jealousy, business disputes, community tensions, and personal animosities often provide the missing contextual clues that explain the motivations behind perplexing cases.
For example, a team of researchers from the University of Maryland analyzed thousands of Enron emails using sentiment analysis algorithms. By identifying frequently occurring emotional tones in messages between co-conspirators, the AI revealed previously unnoticed patterns of anger, deceit, and collusion indicating fraud. This technology was able to read between the lines and illuminate the real social dynamics that traditional analytics missed.
Renowned AI researcher Lisa Feldman Barrett believes sentiment analysis has "vast untapped potential" in law enforcement. Her lab uses machine learning to identify emotions based on word choice, context, metaphors, syntax, and other linguistic cues. She argues this can decode a suspect's state of mind to understand stressors and relationships that may have spurred crimes of passion.
Barrett states that "context is everything" in interpreting emotions from text. Her models are trained to pick up on sarcasm, doubt, sincerity, anger, affection, and hundreds of other emotive states based on how phrases are constructed. This grants a multilayered perspective on the social and psychological factors surrounding past crimes.
While privacy laws can restrict access to personal communications, public forums like social media present a wealth of data for sentiment analysis. Comments and posts leading up to crimes often demonstrate rising tensions between victims and suspects identifiable by algorithms. Even old message board posts may provide critical context.
Sifting through endless piles of documents is one of the most laborious and costly aspects of criminal investigations. But advancements in natural language processing and optical character recognition have automated this grunt work to provide tremendous time and cost savings. For cold cases with boxes of paper files, digitization is the first step. Scanning technology converts handwritten notes, typed reports, faxes, and printed photos into searchable text and images. This instantly unlocks decades of inaccessible information for algorithmic analysis.
Machine learning algorithms can then rapidly parse thousands of pages per second to surface key names, dates, locations, events and physical evidence. This produces interactive timelines that turn disjointed notes into a coherent visual narrative that reveals connections. Detectives gain years of productivity compared to poring over files manually. They are freed to spend more time in the field interviewing witnesses and narrowing leads.
Successful applications come from Anne Arundel County, Maryland, where 15,000 cold case documents were digitized and fed into an eDiscovery AI. In hours, the algorithm identified critical new information that the department had previously missed despite 100 combined hours of human review. The system is now helping investigators re-examine over 560 unsolved homicides with greater accuracy.
Across the country in Ventura County, California, algorithms helped close two unsolved 1980s strangulation murders by uncovering evidence originally buried in 3,000 documents. The AI matched DNA found under one victim"s fingernails to a jailed sex offender. Finding these needles in haystacks demonstrates the power of tech-assisted document review.
Financial savings can also be substantial. Human document review costs $75-$300 per hour on average. At those rates, cold cases with tens of thousands of pages would devour entire budgets. Automation slashes these costs by over 80% while achieving higher accuracy. Processing fees for AI services like eDiscovery fall below $10 per gigabyte of data. The Toronto Police Service estimates algorithms have saved over $1 million in review expenditures.
While AI cannot fully replace human detection skills, its perfect recall and pattern recognition provides invaluable assistance. Algorithms flag key passages for context, spot inconsistencies, and track how information relates across files. This augments investigators" abilities and steers them to critical evidence faster. But responsible oversight and bias monitoring are still necessary safeguards.
Confirmation bias - the tendency to interpret information in a way that confirms preexisting beliefs or theories - is a major impediment in law enforcement. Detectives working an angle they feel strongly about often downplay or ignore conflicting evidence. But artificial intelligence systems do not have inherent biases or hunches. By objectively analyzing all available data, AI can counteract tunnel vision and point investigations in unexpected but justified directions.
A powerful example comes from the Netherlands Forensic Institute's use of AI on cold cases. One team noticed that reviews of DNA evidence from a 15-year old murder focused on a single suspect despite weak genetic links. Human analysts succumbed to confirmation bias by trying to force facts to fit their original theory. But when the full DNA dataset was run through an algorithm with no biases, it highlighted a different individual with much stronger genetic ties. This new suspect was apprehended and confessed to the crime. As institute director Mark de Graaf concluded, "Algorithms have no tunnel vision."
Researchers from Case Western Reserve University found similar results testing AI investigative systems. Their algorithms objectively weighed each piece of evidence and identified probable culprits. But human investigators shown the same data consistently focused on subsets of proof that affirmed their initial hunches. By avoiding preconceived narratives, the AI could assess the full picture and "see things human police miss."
Of course, AI risks inheriting the biases of past data used to train systems. But new techniques like adversarial machine learning actively counteract prejudiced influences. And just prompting human analysts to justify why conflicting evidence contradicts their own theories provides a check against confirmation bias. AI serves as an impartial second pair of eyes, preventing dogged pursuit of dead-end leads.
UCLA cognitive neuroscientist Matthew Lieberman explains these benefits: "A.I. has no intrinsic interest in closing a case or assuaging departmental anxieties. It weighs information without regard to consequences for careers or political appointments or community relations." This neutrality allows AI to stay open to contradictory possibilities and base conclusions solely on factual data correlations.