Improving Explainable AI Interpretability: Mathematical Models for Evaluating Explanation Methods.
Notice bibliographique
Résumé
<title>Abstract</title> AI has transformed various industries. Understanding and trusting AI decision-making processes is crucial as they become more integrated into our lives. Explainable AI (XAI) aims to provide transparency and interpretability to AI models, addressing concerns about accountability, fairness, and ethical AI. Lack of transparency in AI can lead to uncertainty, especially in critical domains where incorrect or biased decisions can have adverse outcomes. This paper aims to introduce Explainable Artificial Intelligence (XAI) and its significance in enhancing transparency, accountability, fairness, and trustworthiness in AI systems. The primary focus is on presenting mathematical expressions for quantitatively evaluating the accuracy and relevance of explanations offered by XAI methods, thereby enhancing the quality and dependability of these explanations. The paper conducts a literature review on XAI methods and their applications, specifically examining whether evaluation metrics are provided for assessing the explanations. The paper presents a mathematical formulation for an Intrusion Detection System (IDS) that utilizes autoencoders along with an explanation technique like SHAP, as a case study. We further present the application of the proposed evaluation metrics and mathematical formulas for quantitative assessment of the correctness of the explanations. Screenshots of the results have been presented for each of the quantitative mathematical formulas of each metric. The contributions to the mathematical derivation of the IDS case study is also profound wherein we adopt the cross-entropy loss function for derivation and mathematically provide solutions to address the overfitting problem with L1regularization and also express the threshold updation using Chebyshev’s formula. The results presented in the results and discussion section include the correctness evaluation of the mathematical formulations of the evaluation metrics for XAI, which is demonstrated using a case study (Autoencoder-based Intrusion Detection System with SHAPley explanations) demonstrating their applicability and transparency. The significance of XAI in promoting comprehension and confidence in AI systems is underscored by this paper. Through transparency and interpretability, XAI effectively tackles apprehensions related to accountability, fairness, and ethical AI. The mathematical assessment metrics put forth in this study provide a means to evaluate the accuracy and pertinence of explanations furnished by XAI techniques, thereby facilitating advancements and comparisons in AI research and development. The future generalized implementation of these metrics with real-time data across various domains will enhance the practicality and usefulness of XAI across diverse domains. This study was conducted on open-access data obtained from Canadian Institute for Cybersecurity and NSL KDD dataset.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,031 | 0,015 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,003 | 0,007 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».