Statements (47)
| Predicate | Object |
|---|---|
| gptkbp:instanceOf |
gptkb:physicist
|
| gptkbp:canBe |
bias_in_AI_models
|
| gptkbp:canEstablish |
regulatory compliance
|
| gptkbp:canLeadTo |
informed decision-making
improved user experience better model performance |
| gptkbp:hasFacility |
to make AI decisions understandable
|
| https://www.w3.org/2000/01/rdf-schema#label |
explainable AI
|
| gptkbp:improves |
trust_in_AI_systems
|
| gptkbp:isAvailableIn |
customer service
marketing strategies business decision-making |
| gptkbp:isChallengedBy |
data privacy concerns
intellectual property issues technical limitations |
| gptkbp:isDiscussedIn |
academic papers
industry conferences media articles |
| gptkbp:isEvaluatedBy |
performance metrics
user studies |
| gptkbp:isFacilitatedBy |
user acceptance
|
| gptkbp:isInfluencedBy |
regulatory requirements
user feedback societal expectations |
| gptkbp:isPromotedBy |
academic institutions
ethical_AI_advocates |
| gptkbp:isRelatedTo |
accountability
machine learning transparency |
| gptkbp:isSupportedBy |
government initiatives
industry standards collaborative research open-source tools |
| gptkbp:isUsedIn |
healthcare
finance autonomous vehicles |
| gptkbp:isUtilizedFor |
complexity of models
lack of standard metrics subjectivity of explanations |
| gptkbp:isUtilizedIn |
real-world applications
theoretical frameworks |
| gptkbp:isVisitedBy |
data scientists
researchers |
| gptkbp:technology |
gptkb:SHAP
LIME interpretable models model-agnostic methods |