TY  - JOUR
T1  - Survey on Feature Attribution Methods in Explainable AI
AU  - Ko, Gihyuk 
AU  - Lim, Gyumin 
AU  - Cho, Homook 
JO  - Journal of KIISE, JOK
PY  - 2020
DA  - 2020/1/14
DO  - 10.5626/JOK.2020.47.12.1181
KW  - feature attribution
KW  - input feature importance
KW  - XAI
KW  - artificial intelligence
KW  - survey categorization
AB  - As artificial intelligence (AI)-based technologies are increasingly being used in areas that can have big socioeconomic effects, there is a growing effort to explain decisions made by AI models. One important direction in such eXplainable AI (XAI) is the ‘feature attribution’ method, which explains AI models by assigning a contribution score to each input feature. In this work, we surveyed nine recently developed feature attribution methods and categorized them using four different criteria. Based on the categorizations, we found that the current methods focused only on specific settings such as generating local, white-box explanations of neural networks and lacked theoretical foundations such as axiomatic definitions. We suggest future research directions toward a unified feature attribution method based on our findings.