StarTalk Explains UAP Threat Assessment and Data Infrastructure with AARO Director

Overview

In this StarTalk episode, host Neil deGrasse Tyson welcomes John Koslovsky, director of the All Domain Anomaly Resolution Office, to discuss how unidentified anomalous phenomena are evaluated for potential threats. The conversation covers why UFOs were rebranded as UAP, data collection across government and public channels, and how the office triages cases based on data quality and threat potential.

Key themes include sensor networks across the DoD, the move toward citizen science and public reporting, and the role of transparency in building trust while protecting sensitive information. The discussion also touches on notable case studies used to illustrate how analysts distinguish mundane phenomena from potential breakthroughs or threats.

SEO Driven Summary

StarTalk hosts a conversation about UAPs with John Koslovsky, director of the All Domain Anomaly Resolution Office. The episode explains the rebranding from UFOs to UAPs, the need to avoid bias when investigating unidentified phenomena, and how the government collects and analyzes data from multiple sources including DoD channels, the FAA, law enforcement, academia, and the public.

Why Rebrand UFOs as UAPs

The guests discuss how the public equates UFOs with extraterrestrials, while for investigators the term Unidentified and Anomalous Phenomena keeps the inquiry bias-free. This distinction allows investigators to explore a wide range of explanations and potential technologies rather than assuming alien origin.

Detection, Sensors, and Data

Detection relies on human reports, cameras, radars, and electromagnetic sensors. Koslovsky emphasizes the need to move beyond eyeballs to standardized sensors, and to connect data across federal agencies and private partnerships. He notes that standardization, calibration, and cross-corroboration are essential to separate noise from real signals.

Public Involvement and Transparency

The Office plans to launch a public reporting mechanism and to share more data publicly while ensuring sensitive capabilities remain protected. The aim is to empower citizen scientists and amateur observers to contribute high quality data in a way that protects operational security.

Data Integrity and AI

The team discusses automation and machine learning to triage large volumes of reports, adding safeguards against AI generated manipulation and preserving the role of human analysts. They acknowledge an arms race with AI hallucinations and spoofing, but see automated tools as a means to prioritize meaningful cases for deeper study.

Notable Case Studies

Future Plans

The discussion closes with the promise of more transparent reporting, international cooperation, a scientific journal in FY26, and workshops that engage citizen scientists while maintaining rigorous standards of evidence and safety.