# Explanation in Artificial Intelligence: Insights from the Social Sciences

## TL;DR

This paper advocates for integrating social sciences research into explainable AI to improve the quality and human-likeness of AI explanations, emphasizing cognitive biases and social expectations.

## Contribution

It reviews interdisciplinary research from philosophy, psychology, and social sciences to inform and enhance explanation methods in artificial intelligence.

## Key findings

- Humans use cognitive biases in explanations.
- Social expectations influence explanation quality.
- Existing AI explanations often lack grounding in social science insights.

## Abstract

There has been a recent resurgence in the area of explainable artificial intelligence as researchers and practitioners seek to make their algorithms more understandable. Much of this research is focused on explicitly explaining decisions or actions to a human observer, and it should not be controversial to say that looking at how humans explain to each other can serve as a useful starting point for explanation in artificial intelligence. However, it is fair to say that most work in explainable artificial intelligence uses only the researchers' intuition of what constitutes a `good' explanation. There exists vast and valuable bodies of research in philosophy, psychology, and cognitive science of how people define, generate, select, evaluate, and present explanations, which argues that people employ certain cognitive biases and social expectations towards the explanation process. This paper argues that the field of explainable artificial intelligence should build on this existing research, and reviews relevant papers from philosophy, cognitive psychology/science, and social psychology, which study these topics. It draws out some important findings, and discusses ways that these can be infused with work on explainable artificial intelligence.