White Paper: 2025 UAP Workshop - Narrative Data, Infrastructures, and Analysis

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as radar or weather records. Interviews and psychological screening of witnesses was offered as
an example of how to assess motivations and reduce false reports, though it was acknowledged
that this is difficult to implement at scale. At the same time, biases in favor of certain professions
(pilots, police) must be acknowledged due to enhanced observational training and skills. A
phenomenological approach (qualitative analysis of indicators of lived experiences) allowing
patterns to emerge from narrative accounts was recommended as a complement to quantitative
methods, ensuring that unusual but meaningful details are not prematurely excluded.
AI and analytical methods
AI offers opportunities for pattern recognition, hypothesis generation, and efficiency gains in
large-scale text and multimodal data analysis. Techniques such as semantic search, clustering,
and multimodal modeling (for example, combining acoustic and infrared signals) can help
identify anomalies. AI is also valuable for routine tasks, such as extracting dates or locations
from unstructured text, or triaging likely misidentifications. However, there are risks associated
with AI. Hallucination (the generation of convincing but false conclusions) remains a core
concern. AI analysis is only as reliable as the quality of its input, underscoring the “garbage in,
garbage out” principle. Additionally, LLMs are already biased by UFO-related cultural content,
potentially skewing analyses. Small datasets limit the potential for model training, though pre-
trained models may still be repurposed. Best practices involve an iterative human-AI
collaboration, where algorithms provide preliminary analysis that is verified, corrected, and
enriched by human researchers. Ensemble approaches, leveraging multiple models, may reduce
error rates. Overall, tasks must be carefully defined to align AI methods with research goals,
ensuring a balance between qualitative depth and quantitative rigor.
Forward-looking strategy and key considerations
The group emphasized the need for a forward-looking research infrastructure that integrates
proactive data collection, robust metadata standards, and interdisciplinary collaboration. Some
argued for focusing on new, higher-quality data collection while others urged continued
investment in historical data to preserve its potential value. Future infrastructure priorities
include a unified security solution for managing classified and unclassified data, improved
questionnaire design for witness reports, and benchmarking systems to track analytic
performance over time. Importantly, even “low quality” or stigmatized reports should not be
discarded but made available for diverse lines of inquiry and data reuse.
Finally, participants stressed the need for citizen engagement and ethical responsibility. Public
contributors must be incorporated into coherent strategies for data collection and community-
engaged research. At the same time, researchers must remain vigilant about the risks of
disinformation, AI hallucination, and epistemic injustice, ensuring that narratives are respected in
their original form. Balancing transparency with security, and methodological rigor with
openness to the “weird stuff,” will be essential for building a sustainable, credible, and
innovative field of UAP research.