Why Algorithms Raise Old Questions in New Clothes
The old school police mindset is that they need to heavily patrol bad neighborhoods. The new school reformist mindset is that there are more arrests and ill-will in these neighborhoods specifically because they’re so heavily policed in the first place.
Artificial intelligence is already weighing into this tension, shaping criminal investigations, from “predictive” patrol maps to automated content-scanning and pattern-of-life analytics. The constitutional question isn’t whether technology can help police, it’s how far it can go before it collides with the Fourth Amendment and due process.
The very important, fundamental guiding principle, drawn from decades of search-and-seizure law, is simple: probable cause must rest on a concrete nexus to the specific facts of a case, not on broad generalizations about how criminals behave or what an algorithm usually predicts.
What Is Predictive Policing?
Predictive tools analyze historical data, arrests, calls for service, locations, or social graphs, to forecast where or who is “high risk.” This can influence everything from where officers patrol to who gets stopped, questioned, or surveilled. Useful? Potentially. Constitutionally sufficient for probable cause? Not by itself.
A statistical risk score or heat map is, at best, just a lead. To justify an intrusive search or arrest, police still need individualized facts that tie a person, device, or place to a specific suspected offense.
Probable Cause Demands Particularized Facts
The Supreme Court has long warned against “general warrants.” In practice, that’s why modern digital warrants must be particular about what’s being searched and supported by a nexus, as in why this device/account/place likely holds evidence of this crime.
Courts reject boilerplate assertions like “criminals use phones” or “drug dealers text.” Likewise, “the model flagged this area” or “the AI says this account is suspicious” cannot substitute for case-specific facts.
The Texas Lens: Stocker, Baldwin, and the Nexus Requirement
The use of AI and predictive policing is strongly analogous to Texas case law on warrant specificity. Texas courts have recently sharpened the “nexus” conversation in digital searches. In State v. Baldwin, the Court of Criminal Appeals criticized affidavits that rely on generic claims about cell-phone use among criminals, emphasizing the need for specific facts connecting a phone to the offense.
Later, in Stocker v. State, the same court clarified that while a nexus is required, lower courts shouldn’t impose a wooden rule that an affidavit always must show the phone was used during, before, or after the crime; other specific facts may suffice.
In other words, no boilerplate, but also no hyper-technical checklist, just a grounded, fact-driven connection between device and offense.
Applying Those Lessons to AI and Predictive Analytics
If an officer points to an algorithmic output, let’s say, a predictive “hot zone,” a risk score, or an AI match, the constitutional question mirrors Stocker/Baldwin’s nexus analysis:
Does the affidavit explain, in concrete terms, how the output relates to this suspect and this crime? Are there independent, particularized facts (observations, corroborated tips, timestamps, geolocation records, surveillance video) that link the target to criminal activity beyond “the model says so”? Is the requested search narrowly tailored (place to be searched, items to be seized), or is it a sweeping fishing expedition justified by a statistical hunch?
Probable Cause vs. Predictive “Hunches”
I think courts are likely to treat algorithmic signals like informant tips: they can initiate investigation, but they need strong corroboration. A heat map doesn’t equal reasonable suspicion of a particular person. A “network risk” score for an account doesn’t, by itself, justify rummaging through a phone. Just as warrant applications must avoid boilerplate language; AI-influenced applications must avoid algorithmic boilerplate (“the model flags this device”).
The affidavit should spell out why the tool’s output matters here – time, location, behavior matching the tip, independent observations, and links to the victim or scene.
Due Process, Confrontation, and the Black Box
Even when a warrant is obtained, due process questions will hang over the government’s head. If AI or predictive analytics produce pivotal evidence (e.g., automated content detection, pattern matching), defendants may argue for disclosure sufficient to test reliability under evidentiary standards (Kelly/Daubert analogs). Where out-of-court machine classifications are offered substantively, Confrontation Clause arguments can surface if a human witness can’t meaningfully explain the basis of the machine’s “statement.” Expect litigation over access to training data, error rates, false positive handling, and validation studies. My hope is that Texas prosecutors will be very cautious and conservative about the use of such non-specific analytics.
Practical Drafting Guide for Law Enforcement (and What Defense Should Look For)
For prosecutors and affiants, the most defensible AI-influenced warrant affidavits will:
- Identify the specific tool used and what it outputs (not just “AI flagged”).
- Explain why the output is probative in this case (timestamps, geospatial match to crime window, consistent behavior, corroborating witness statements).
- Limit the search to particular data likely to contain evidence (e.g., messages between specified dates, location data around the incident), rather than a full scrape.
For defense attorneys, red flags include:
- Conclusory statements and assertions that “AI indicates” criminality without case-specific linkage.
- Overbroad data grabs lacking temporal or content limits.
- Failure to disclose error rates/validation where a tool’s reliability is central.
- Affidavits that rest on predictive labels instead of observed facts.
Geofence, Keyword, and Network Warrants: The Next Flashpoints
Bulk digital warrants, geofences around a crime scene, keyword searches, mass “similar-photo” matches, all raise heightened particularity concerns.
The safest path for the State is to stage these requests (narrow first, then expand with new facts), minimize innocent-user data, and tie each step to fresh, individualized facts.
Defense attorneys should press for segmentation, minimization, and suppression if the warrant jumps from broad analytics to intrusive searches without a clear nexus.
What This All Means for Texans Right Now
Whether we like it or not, the times are changing fast with the rise of AI. In Montgomery County and across Texas, judges will recognize the promise of technology but apply the same constitutional touchstones they use for phones and cloud accounts: particularity and nexus. Stocker’s message isn’t that anything goes; it’s that courts must read affidavits holistically for specific, non-boilerplate facts that connect the target to the crime.
That same lens should govern AI and predictive policing: analytics can inform but not replace the individualized showing the Fourth Amendment requires.