Explaining Explanations to Society
By Leilani H. Gilpin, Cecilia Testart, Nathaniel Fruchter, Julius Adebayo. 2019
36 citations recorded by the source corpus.
There is a disconnect between explanatory artificial intelligence (XAI) methods and the types of explanations that are useful for and demanded by society (policy makers, government officials, etc.) Questions that experts in artificial intelligence (AI) ask opaque systems provide inside explanations, focused on debugging, reliability, and validation. These are different from those that society will ask of these systems to build trust and confidence in their decisions. Although explanatory AI systems can answer many questions that experts desire, they often don't explain why they made decisions in a way that is precise (true to the model) and understandable to humans. These outside explanations can be used to build trust, comply with regulatory and policy changes, and act as external validation. In this paper, we focus on XAI methods for deep neural networks (DNNs) because of DNNs' use in decision-making and inherent opacity. We explore the types of questions that explanatory DNN systems can answer and discuss challenges in building explanatory systems that provide outside explanations for societal requirements and benefit.
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
- Timo Speith
- G. Newlands
- Min Kyung Lee
- Mohammad Hossein Jarrahi
- Christine T. Wolf
- Eliscia Kinder
- Will Sutherland
- Holger Hermanns
Citations in the corpus
- Algorithmic management in a work context (2021, background): “Yet, scholars have noted a gap between technical XAI advances (which often are most useful to AI developers in processes of debugging) and the types of explanations that workers might need or want in actual use settings (Gilpin et al., 2019).”
- What Do We Want From Explainable Artificial Intelligence (XAI)? - A Stakeholder Perspective on XAI and a Conceptual Model Guiding Interdisciplinary XAI Research (2021, background): “This means that this information does not directly facilitate understanding for nonexperts [16].”
- A Review of Taxonomies of Explainable Artificial Intelligence (XAI) Methods (2022, background): “, a denied loan decision) came to be, a person should be enabled to assess whether this output was based on valid criteria or not [29, 57].”