pith:LTEXOPHK
Explainable AI Isn't Enough! Rethinking Algorithmic Contestability
Standard XAI tools like counterfactuals only flag nearby errors and fall short of providing grounds to overturn algorithmic decisions.
arxiv:2605.16041 v1 · 2026-05-15 · stat.ML · cs.LG
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Claims
Standard XAI explanations such as counterfactuals, LIME, or Anchors, even when combined with human intuitions about decision continuity or monotonicity, reveal only errors in the neighborhood of the individual, but provide insufficient grounds for overturning the decision at hand.
That the three identified types of evidence (predictive multiplicity, incorrect feature values, and neglected overruling evidence) render decisions normatively indefensible according to the decision maker's own ethical standards, as stated in the section proposing the operational definition of contestability.
The paper defines algorithmic contestability as identifying evidence to overturn potentially incorrect decisions and identifies three types of such evidence that make decisions normatively indefensible under the decision maker's standards.
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Receipt and verification
| First computed | 2026-05-20T00:01:50.381946Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
5cc9773cea3ff0c92b61c19d220233180f877f50f20305cf0523f73a52dbdc41
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/LTEXOPHKH7YMSK3BYGOSEARTDA \
| 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: 5cc9773cea3ff0c92b61c19d220233180f877f50f20305cf0523f73a52dbdc41
Canonical record JSON
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