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1"

DARPA"XAI"Literature"Review"" " " " " " " " " p."

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Explanation in Human-AI Systems:

A Literature Meta-Review

Synopsis of Key Ideas and Publications

and

Bibliography for Explainable AI

Prepared by Task Area 2

Shane T. Mueller

Michigan Technical University

Robert R. Hoffman, William Clancey, Abigail Emrey

Institute for Human and Machine Cognition

Gary Klein

MacroCognition, LLC

DARPA XAI Program

February 2019

!

2"

DARPA"XAI"Literature"Review"" " " " " " " " " p."

"

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Explanation in Human-AI Systems

Executive Summary

This is an integrative review that address the question, "What makes for a good

explanation?" with reference to AI systems. Pertinent literatures are vast. Thus, this review is

necessarily selective. That said, most of the key concepts and issues are exressed in this Report.

The Report encapsulates the history of computer science efforts to create systems that explain

and instruct (intelligent tutoring systems and expert systems). The Report expresses the

explainability issues and challenges in modern AI, and presents capsule views of the leading

psychological theories of explanation. Certain articles stand out by virtue of their particular

relevance to XAI, and their methods, results, and key points are highlighted.

It is recommended that AI/XAI researchers be encouraged to include in their research

reports fuller details on their empirical or experimental methods, in the fashion of experimental

psychology research reports: details on Participants, Instructions, Procedures, Tasks, Dependent

Variables (operational definitions of the measures and metrics), Independent Variables

(conditions), and Control Conditions.

In the papers reviewed in this Report one can find methodological guidance for the

evaluation of XAI systems. But the Report highlights some noteworthy considerations: The

differences between global and local explanations, the need to evaluate the performance of the

human-machine work system (and not just the performance of the AI or the performance of the

users), the need to recognize that experiment procedures tacitly impose on the user the burden of

self-explanation.

Corrective/contrastive user tasks support self-explanation or explanation-as-exploration.

Tasks that involve human-AI interactivity and co-adaptation, such as bug or oddball detection,

hold promise for XAI evaluation since they too conform to the notions of "explanation-as-

exploration" and explanation as a co-adaptive dialog process. Tasks that involve predicting the

AI's determinations, combined with post-experimental interviews, hold promise for the study of

mental models in the XAI context.

5"

DARPA"XAI"Literature"Review"" " " " " " " " " p."

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1. Purpose, Scope, and Organization

The purpose of this document is to distill from existing scientific literatures the resent key ideas

that pertain to the DARPA XAI Program.

Importance of the Topic

For decision makers who rely upon analytics and data science, explainability is a real issue. If the

computational system relies on a simple decision model such as logistic regression, they can

understand it and convince executives who have to sign off on a system because it seems

reasonable and fair. They can justify the analytical results to shareholders, regulators, etc. But for

"Deep Nets" and "Machine Learning" systems, they can no longer do this. There is a need to find

ways to explain the system to the decision maker so that they know that their decisions are going

to be reasonable, and simply invoking a neurological metaphor might not be sufficient. The goals

of explanation involve persuasion, but that comes only as a consequence of understanding the

hot the AI works, the mistakes the system can make, and the safety measures surrounding it.

... current efforts face unprecedented difficulties: contemporary models are more

complex and less interpretable than ever; [AI systems are] used for a wider array

of tasks, and are more pervasive in everyday life than in the past; and [AI is]

increasingly allowed to make (and take) more autonomous decisions (and

actions). Justifying these decisions will only become more crucial, and there is

little doubt that this field will continue to rise in prominence and produce exciting

and much needed work in the future (Biran and Cotton, 2017, p. 4).

This quotation brings into relief the importance of XAI. Governments and the general public are

expressing concern about the emerging "black box society." A proposed regulation before the

European Union (Goodman and Flaxman, 2016) prohibits "automatic processing" unless user's

rights are safeguarded. Users have a "right to an explanation" concerning algorithm-created

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