AI Behavior Science

AI Behavior Science is an emerging research field focused on the systematic study of how AI systems behave: how they reason, generalize, fail, coordinate, self-report uncertainty, and interact with human and institutional constraints. The field aims to develop shared conceptual frameworks, empirical methods, and accountability tools for understanding AI behavior as a scientific object of study.

This repository is the canonical home for the “AI Behavior Science — Founding Territory Paper” and for future work that builds out the area, including foundational frameworks, taxonomies, methodological standards, the Agent Epistemic Accountability Layer (AEAL), and related computational artifacts.

Repository Purpose

This repository is intended to:

• host the founding territory paper for AI Behavior Science;
• organize core definitions, scope, and research questions;
• develop taxonomies and framework documents;
• track AEAL and related accountability-oriented infrastructure;
• support future public research collaborations and extensions.

Founding Territory Paper

The initial anchor document for this repository is:

AI Behavior Science — Founding Territory Paper

DOI: https://doi.org/10.5281/zenodo.19562751

This DOI serves as the permanent, citable identifier for the founding paper.

Suggested citation

papanokechi. “AI Behavior Science — Founding Territory Paper.” Version 1.0. Zenodo (2026). https://doi.org/10.5281/zenodo.19562751

Future Frameworks

Planned workstreams include:

• AEAL — Agent Epistemic Accountability Layer
  • provenance-aware reasoning and reporting;
  • auditable confidence and uncertainty signaling;
  • accountability interfaces for agentic systems.
• behavior taxonomies for model, agent, and multi-agent settings;
• evaluation protocols for reliability, calibration, and failure modes;
• conceptual bridges between theoretical analysis and computational artifacts.

Related Computational Artifacts

Where relevant, this repository will link to companion code and artifact repositories, including:

• pcf-spectral-classes — example computational research artifact connected to adjacent formal and experimental work.

Community Roadmap

1. Publish and refine the founding territory paper.
2. Release a first public taxonomy of AI behavioral phenomena.
3. Define AEAL design principles and reference interfaces.
4. Add reproducible artifacts, benchmarks, and case studies.
5. Support broader community contributions, discussion, and review.

License

This repository is released under CC BY 4.0 unless otherwise noted.