Pith Number

pith:ITRNJ4XM

pith:2026:ITRNJ4XM54A5KJQXHVRVNKBO4P

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Multi-Dimensional Model Integrity and Responsibility Assessment Index and Scoring Framework

Hung Cao, Phuc Truong Loc Nguyen, Thanh Hung Do, Truong Thanh Hung Nguyen

A single aggregated score across five responsibility dimensions shows that higher predictive accuracy does not guarantee better overall model integrity in tabular tasks.

arxiv:2605.14550 v1 · 2026-05-14 · cs.LG

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Claims

C1strongest claim

Experiments on healthcare, financial, and socioeconomic datasets show that higher predictive performance does not necessarily imply better overall integrity and responsibility. In several cases, simpler models achieve a stronger cross-dimensional balance than more complex deep tabular architectures.

C2weakest assumption

That established metrics for the five dimensions can be normalized and direction-aligned in a way that produces a meaningful single score without introducing arbitrary biases or losing critical trade-off information.

C3one line summary

MIRAI is a unified index that combines five responsibility dimensions into one score for tabular models, demonstrating that predictive performance does not ensure high overall integrity.

References

43 extracted · 43 resolved · 2 Pith anchors

[1] Motion2Meaning: A Clinician-Centered Framework for Contestable LLM in Parkinson’s Disease Gait Interpretation 2025

[2] Heart2Mind: Human-Centered Contestable Psychiatric Disorder Prediction System Using Wearable ECG Monitors 2026

[3] XEdgeAI: A human-centered industrial inspection framework with data-centric Explainable Edge AI approach 2025

[4] ODExAI: A Comprehensive Object Detection Explainable AI Evalu- ation 2025

[5] LangXAI: Integrating Large Vision Models for Generating Textual Ex- planations to Enhance Explainability in Visual Perception Tasks 2024

Receipt and verification

First computed	2026-05-17T23:39:05.719286Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

44e2d4f2ecef01d526173d6356a82ee3e52ecd502a854b8795c2b44239f69125

Aliases

arxiv: 2605.14550 · arxiv_version: 2605.14550v1 · doi: 10.48550/arxiv.2605.14550 · pith_short_12: ITRNJ4XM54A5 · pith_short_16: ITRNJ4XM54A5KJQX · pith_short_8: ITRNJ4XM

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/ITRNJ4XM54A5KJQXHVRVNKBO4P \
  | 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: 44e2d4f2ecef01d526173d6356a82ee3e52ecd502a854b8795c2b44239f69125

Canonical record JSON

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    "cross_cats_sorted": [],
    "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/",
    "primary_cat": "cs.LG",
    "submitted_at": "2026-05-14T08:29:36Z",
    "title_canon_sha256": "85290615db58483d44677a18b0b63dc7884883a2a62b854d6e31ca8eae27efa3"
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