Pith Number

pith:KETQPU2C

pith:2026:KETQPU2CJME3YM42CRF37UKMV2

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What Should Explanations Contain? A Human-Centered Explanation Content Model for Local, Post-Hoc Explanations

Helmut Degen

A fourteen-code model derived from industrial user studies specifies what content local post-hoc explanations should contain.

arxiv:2605.14207 v1 · 2026-05-14 · cs.HC

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Claims

C1strongest claim

The resulting fourteen-code model is organized into four groups: rule-based, causal, epistemic (actual), and epistemic (similar), with twelve codes grounded in the corpus and two as theoretical extensions. An eleven-member expert panel supported the content adequacy of all codes (I-CVI ≥ 0.82; scale-level agreement of 0.93 for relevance, 0.92 for boundary clarity, and 0.94 for understandability). A stratified subsample of 82 units yielded Krippendorff's α = 0.920 and Cohen's κ = 0.920.

C2weakest assumption

That the six user studies and 325 meaning units adequately represent the explanation needs of users across broader industrial AI applications, and that expert panel judgments on content adequacy will hold for actual end-users in deployment.

C3one line summary

A 14-code content model for local post-hoc AI explanations, derived from 325 user statements and validated by experts with high reliability scores.

References

158 extracted · 158 resolved · 2 Pith anchors

[1] Case-based reasoning: Foundational issues, methodological variations, and system approaches 1994

[2] Lau, Jose Echevarria, and Zoya Bylinskii 2020 · doi:10.1145/3313831.3376615

[3] Adhikari, A., Tax, D.M.J., Satta, R., Faeth, M., 2019. LEAFAGE: Example-Based and Feature-Importance-Based Explanations for Black-Box ML Models, in: Proceedings of the 2019 IEEE International Conferen 2019 · doi:10.1109/fuzz-ieee.2019.8858846

[4] UP- GRADE: Universal Patching and Remediation for Autonomous De- fense.https://arpa-h.gov/explore-funding/programs/upgrade 2024

[5] Aechtner, J., Cabrera, L., Katwal, D., Onghena, P., Valenzuela, D.P., Wilbik, A., 2022. Comparing User Perception of Explanations Devel- oped with XAI Methods, in: Proceedings of the 2022 IEEE Interna 2022 · doi:10.1109/fuzz-ieee55066.2022.9882743

Receipt and verification

First computed	2026-05-17T23:39:10.975562Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

512707d3424b09bc339a144bbfd14caeaa935cc552f99385c6147ec7827ac427

Aliases

arxiv: 2605.14207 · arxiv_version: 2605.14207v1 · doi: 10.48550/arxiv.2605.14207 · pith_short_12: KETQPU2CJME3 · pith_short_16: KETQPU2CJME3YM42 · pith_short_8: KETQPU2C

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curl -sH 'Accept: application/ld+json' https://pith.science/pith/KETQPU2CJME3YM42CRF37UKMV2 \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 512707d3424b09bc339a144bbfd14caeaa935cc552f99385c6147ec7827ac427

Canonical record JSON

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