Journal
APPLIED SCIENCES-BASEL
Volume 12, Issue 19, Pages -Publisher
MDPI
DOI: 10.3390/app12199423
Keywords
explainable artificial intelligence; evaluation methods; human-centred; computer-centred; literature review
Categories
Funding
- Portugal 2020 framed under the Operational Programme for Competitiveness and Internationalization (COMPETE 2020)
- Fundacao para a Ciencia and Technology (FCT)
- Carnegie Mellon University
- European Regional Development Fund [45905]
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The lack of transparency in powerful Machine Learning systems has led to the emergence of the XAI field. Researchers focus on developing explanation techniques to better understand the system's reasoning for a particular output. This paper presents a survey of Human-centred and Computer-centred methods to evaluate XAI systems, and proposes a new taxonomy for clearer categorization of these evaluation methods.
The lack of transparency of powerful Machine Learning systems paired with their growth in popularity over the last decade led to the emergence of the eXplainable Artificial Intelligence (XAI) field. Instead of focusing solely on obtaining highly performing models, researchers also develop explanation techniques that help better understand the system's reasoning for a particular output. An explainable system can be designed, developed, and evaluated from different perspectives, which enables researchers from different disciplines to work together on this topic. However, the multidisciplinary nature of XAI systems creates new challenges for condensing and structuring adequate methodologies to design and evaluate such systems. This paper presents a survey of Human-centred and Computer-centred methods to evaluate XAI systems. We propose a new taxonomy to categorize XAI evaluation methods more clearly and intuitively. This categorization gathers knowledge from different disciplines and organizes the evaluation methods according to a set of categories that represent key properties of XAI systems. Possible ways to use the proposed taxonomy in the design and evaluation of XAI systems are also discussed, alongside with some concluding remarks and future directions of research.
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