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Papers on “machine learning interpretability explainable AI”

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1. AI-Assisted Pipeline for Dynamic Generation of Trustworthy Health Supplement Content at Scale

   Kefallinos, Dionysios, Alexandris, Georgios, Maras, Alexis, et al. · 2018 · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · 46,036 cites

   Although geospatial question answering systems have received increasing attention in recent years, existing prototype systems struggle to properly answer qualitative spatial questions. In this work, we propose a unique framework for answering qualitative spatial questions, which comprises three main components: a geoparser that takes the input questions and extracts place semantic information from text, a reasoning system which is embedded with a crisp reasoner, and finally, answer extraction, which refines the solution space and generates final answers. We present an experimental design to evaluate our framework for point-based cardinal direction calculus (CDC) relations by developing an au

2. From local explanations to global understanding with explainable AI for trees

   Scott Lundberg, Gabriel Erion, Hugh Chen, et al. · 2020 · Nature Machine Intelligence · 9,936 cites

3. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead

   Cynthia Rudin · 2019 · Nature Machine Intelligence · 9,924 cites

4. Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI

   Alejandro Barredo Arrieta, Natalia Díaz-Rodríguez, Javier Del Ser, et al. · 2019 · Information Fusion · 9,796 cites

5. Physics-informed machine learning

   George Em Karniadakis, Ioannis G. Kevrekidis, Lu Lu, et al. · 2021 · Nature Reviews Physics · 7,699 cites

6. Machine Learning: Algorithms, Real-World Applications and Research Directions

   Iqbal H. Sarker · 2021 · SN Computer Science · 5,368 cites

7. Towards A Rigorous Science of Interpretable Machine Learning

   Finale Doshi‐Velez, Been Kim · 2017 · arXiv (Cornell University) · 3,190 cites

   As machine learning systems become ubiquitous, there has been a surge of interest in interpretable machine learning: systems that provide explanation for their outputs. These explanations are often used to qualitatively assess other criteria such as safety or non-discrimination. However, despite the interest in interpretability, there is very little consensus on what interpretable machine learning is and how it should be measured. In this position paper, we first define interpretability and describe when interpretability is needed (and when it is not). Next, we suggest a taxonomy for rigorous evaluation and expose open questions towards a more rigorous science of interpretable machine learni

8. Explainable AI: A Review of Machine Learning Interpretability Methods

   Pantelis Linardatos, Vasilis Papastefanopoulos, Sotiris Kotsiantis · 2020 · Entropy · 2,907 cites

   Recent advances in artificial intelligence (AI) have led to its widespread industrial adoption, with machine learning systems demonstrating superhuman performance in a significant number of tasks. However, this surge in performance, has often been achieved through increased model complexity, turning such systems into "black box" approaches and causing uncertainty regarding the way they operate and, ultimately, the way that they come to decisions. This ambiguity has made it problematic for machine learning systems to be adopted in sensitive yet critical domains, where their value could be immense, such as healthcare. As a result, scientific interest in the field of Explainable Artificial Inte