The adoption of machine learning in health care hinges on the transparency of the used algorithms, necessitating the need for explanation methods. However, despite a growing literature on explaining neural networks, no consensus has been reached on how to evaluate those explanation methods. We propose IROF, a new approach to evaluating explanation methods that circumvents the need for manual evaluation. Compared to other recent work, our approach requires several orders of magnitude less computational resources and no human input, making it accessible to lower resource groups and robust to human bias.
|Title of host publication||Proceedings of the Workshop AI for Affordable Healthcare at ICLR 2020|
|Number of pages||11|
|Publication status||Published - 2020|
|Event||Workshop AI for Affordable Healthcare at ICLR 2020 - Virtual event, Addis Ababa, Ethiopia|
Duration: 26 Apr 2020 → 26 Apr 2020
|Workshop||Workshop AI for Affordable Healthcare at ICLR 2020|
|Period||26/04/2020 → 26/04/2020|