I recently read Responsible Use of AI in Assessment: A Practical Overview, a 2026 report developed by the American Psychological Association’s Committee on Psychological Tests and Assessment. Several of its themes felt familiar. Human review, attention to uncertainty, privacy, bias, and the limits of the technology were considerations we had already been working to incorporate into Threatalytics AI before I encountered the report.
That connection was encouraging, but it also sharpened a question I think every developer and organization should ask: do those principles hold up in the way people actually use the tool?
It is easy to describe AI as responsible. It is harder to establish the conditions that make responsible use possible when information is incomplete, a team is under pressure, and a fluent answer arrives before anyone has verified its basis.
What the report contributes
The committee’s overview addresses psychological and educational assessment. It offers questions for evaluating AI tools and workflows, rather than a compliance checklist. It also states that it represents its authors’ views and is not official APA policy.1
Behavioral threat assessment has its own purposes, responsibilities, and professional boundaries. My application of these ideas to prevention work is a professional reflection, not a claim that the report evaluates Threatalytics AI. It provides neither an endorsement of the platform nor evidence that a particular deployment is effective, fair, or compliant.
What it does provide is a useful basis for examining how a team selects a tool, understands its limits, and decides what level of reliance is justified. For me, that is more valuable than treating familiar principles as a badge of approval.
Human oversight has to change the work
The report describes oversight as active professional involvement in reviewing, interpreting, and, when necessary, intervening in AI output.2 In threat assessment, I would translate that into a practical expectation: the reviewer must be able to inspect the information behind a conclusion and have the authority to revise or reject it.
Consider a fictional workplace review. A supervisor reports repeated complaints following corrective action. A coworker reports questions about a manager’s schedule. An AI-generated summary describes the combination as evidence of preparation for violence. The language sounds coherent, but the original reports may not support that interpretation.
A qualified team would need to examine what was actually said, whether the accounts are independent, why the schedule questions were asked, and what additional context could support or challenge concern. It should consider proportionate interim measures while resolving important gaps. The summary is a starting point for inquiry, not a finding the team inherits.
Human review is weak if the reviewer has no time to check the sources, sees only the generated summary, or assumes that disagreeing with the tool requires special justification. Oversight needs to be built into the workflow before an output influences a consequential action.
Structure should preserve uncertainty
One reason to use assistive intelligence is to make complex information easier to work through. That benefit is lost if the process makes uncertain information appear more certain simply by organizing it neatly.
A direct observation, a third-party account, and an inference are different kinds of information. A useful working picture preserves those distinctions. It makes missing context visible, identifies questions that could change the assessment, and allows the team to reconsider its interpretation as new information emerges.
The committee’s discussion of bias asks readers to consider evidence for an intended use, evidence against it, and important gaps in that evidence.3 For prevention teams, that invites scrutiny of both the tool and the information supplied to it. An account shaped by conflict or selective reporting does not become objective because an AI system summarizes it.
Privacy begins before submission
The overview raises questions about necessary data, authorized access, retention, deletion, confidentiality, and consent in assessment practice.4 The operational lesson I take from that is straightforward: decide what information is needed and permitted before entering it into an AI system.
Threatalytics AI users are instructed to avoid personally identifiable information and use neutral labels such as Subject and Reporting Party. Names can be redacted in outputs, but output redaction does not undo the original submission. A combination of role, location, and circumstances may also identify someone even when their name is absent.
Organizations need to confirm the actual access, retention, deletion, provider terms, and required agreements for their proposed use. Instruction-level privacy reminders do not establish those protections. The applicable notice, permission, and consent questions also need to be reviewed for the setting and purpose, rather than assumed to be identical across every assessment context.
Instructions are a starting point
We built Threatalytics AI around the belief that AI should help qualified people think more clearly while leaving decisions with the team. It is designed to organize supplied information, surface gaps and questions, and support professional review. It is not a diagnostic tool, a violence-prediction engine, or a replacement for trained professionals.
Those are intended boundaries and uses. They still need to be examined through evaluation, user training, and review of actual performance. Saying that a tool considers bias does not demonstrate fair outcomes. Asking it to distinguish facts from assumptions does not guarantee that it will do so consistently. Potential time savings should include the effort needed to verify and correct an output.
The report’s sections on competence and continuous improvement reinforce the need for ongoing learning and evaluation.5 In practice, teams need a route for reporting errors, a responsible person who acts on that feedback, and a reason to revisit performance when the tool or its use changes. Developers have responsibilities here too. Accountability cannot simply be handed to the person reviewing the final screen.
A standard to keep building toward
What encouraged me about this report was the emphasis on questions that should remain open throughout development and use. Can people understand the tool’s purpose? Can they challenge its output? Are privacy arrangements clear? Is the intended benefit supported by evidence? What happens when the system gets something wrong?
That is the direction we intend to keep building toward with Threatalytics AI. A more disciplined foundation for human-led decisions requires transparency about both the assistance a tool offers and the work still needed to justify reliance on it.
Does this sound familiar?
What would responsible AI use require in your team?
If your organization is exploring AI but has unresolved questions about professional oversight, information handling, or how to evaluate its usefulness, those questions deserve attention before routine use.
Contact us to discuss your workflow, the boundaries your organization needs, and whether Threatalytics AI could be a suitable assistive tool. Please keep initial inquiries free of names or sensitive case details.
Talk Through Your ChallengeSource and perspective note
American Psychological Association, APA Committee on Psychological Tests and Assessment (CPTA). (2026). Responsible Use of AI in Assessment: A Practical Overview.
1. Introduction, p. II, and executive summary, pp. 1–2: scope, intended use, and status of the overview.
2. Human Oversight, pp. 9–10.
3. Bias and Fairness, pp. 3–4.
4. Privacy and Confidentiality and Informed Consent, pp. 5–6.
5. Competence and Training, pp. 7–9, and Continuous Improvement, pp. 12–13. Page references use the report’s printed page numbers.
The applications to behavioral threat assessment and the discussion of Threatalytics AI reflect Jason Destein’s perspective. The workplace example is fictional. This article does not imply APA endorsement, certification, product validation, or a finding of compliance. The report is linked at its official source and is not reproduced here.