pith:3BWCD45E
Towards Rigorous Explainability by Feature Attribution
Symbolic methods can provide rigorous feature importance assignments in explainable AI unlike non-symbolic approaches such as Shapley values
arxiv:2604.15898 v2 · 2026-04-17 · cs.AI
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\pithnumber{3BWCD45E4NX4APHFYXABWXAPZS}
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Record completeness
Claims
Non-symbolic methods such as Shapley values lack rigor and can mislead, while symbolic methods provide rigorous alternatives for feature importance assignment.
That symbolic methods can be made practical and scalable for the complex, high-dimensional models used in real-world machine learning.
The paper reviews work on symbolic methods for rigorous feature attribution in XAI as an alternative to non-rigorous non-symbolic techniques.
Receipt and verification
| First computed | 2026-05-28T01:05:12.286051Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
d86c21f3a4e36fc03ce5c5c01b5c0fcc8461c7515b0209391327d7a4df60b355
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/3BWCD45E4NX4APHFYXABWXAPZS \
| 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: d86c21f3a4e36fc03ce5c5c01b5c0fcc8461c7515b0209391327d7a4df60b355
Canonical record JSON
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