Сравнение на методи

	LIME: Локално интерпретируеми моделно-независими обяснения	Контрафактуални обяснения
Област	Машинно обучение	Машинно обучение
Семейство	Machine learning	Machine learning
Година на възникване	2016	2017
Създател	Marco Ribeiro, Sameer Singh & Carlos Guestrin	Sandra Wachter, Brent Mittelstadt & Chris Russell
Тип	post-hoc local explanation	Post-hoc, model-agnostic explanation
Основополагащ източник	Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). "Why should I trust you?": Explaining the predictions of any classifier. ACM SIGKDD, 1135–1144.	Wachter, S., Mittelstadt, B., & Russell, C. (2017). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. Harvard Journal of Law & Technology, 31, 841–887.
Други названия	Local Surrogate Explanations, Model-Agnostic Local Explanations, Locally Faithful Approximations, Yerel Yorumlanabilir Model-Bağımsız Açıklamalar	Algorithmic Recourse, Contrastive Explanations, What-If Explanations, Karşıolgusal Açıklamalar
Свързани	2	2
Резюме	LIME, introduced by Ribeiro, Singh, and Guestrin in 2016, explains the predictions of any black-box classifier or regressor by building a simple, locally faithful surrogate model around a single prediction of interest. Rather than explaining the global model, LIME focuses on why a specific instance was classified the way it was, making complex models such as deep neural networks and ensemble methods interpretable to end-users, domain experts, and auditors.	Counterfactual explanations, introduced by Wachter, Mittelstadt, and Russell in 2017, answer the question: 'What is the smallest change to the input that would have produced a different model output?' Rather than explaining why a model made a decision, they describe what would need to change for that decision to be reversed, making them particularly valuable for high-stakes applications such as credit scoring, medical diagnosis, and hiring decisions under frameworks like the EU GDPR.