MLchartDataset catalogue

Explaniability

Term · Oil and gas · MLC-T-OAG-001351

The degree to which the internal workings and outputs of a machine learning model can be understood by humans. For high-stakes decisions in the oil and gas sector, such as well placement or process safety alerts, explainable AI provides the reasoning behind a model's prediction. Techniques like SHAP (SHapley Additive exPlanations) or LIME (Local Interpretable Model-agnostic Explanations) are used to quantify the contribution of each input feature to a specific outcome.

Table 1. Record
IdentifierMLC-T-OAG-001351
FieldOil and gas
SubjectDigital
Record as JSON 
{
  "id": "MLC-T-OAG-001351",
  "term": "Explaniability",
  "field": "Oil and gas",
  "definition": "The degree to which the internal workings and outputs of a machine learning model can be understood by humans. For high-stakes decisions in the oil and gas sector, such as well placement or process safety alerts, explainable AI provides the reasoning behind a model's prediction. Techniques like SHAP (SHapley Additive exPlanations) or LIME (Local Interpretable Model-agnostic Explanations) are used to quantify the contribution of each input feature to a specific outcome.",
  "subject": "Digital",
  "url": "https://mlchart.com/terminology/oil-and-gas/explaniability/"
}

Record 1,455 of 4,469 in Oil and gas terminology (MLC-0103). Request the full dataset.