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DARPA's Explainable Artificial Intelligence Program Deep Learning and Security DARPA’s Explainable Artificial Intelligence Program David Gunning, David W. Aha n Dramatic success in machine learning dvances in machine learning (ML) techniques promise has led to a new wave of AI applications (for to produce AI systems that perceive, learn, decide, and example, transportation, security, medicine, act on their own. However, they will be unable to finance, defense) that offer tremendous A explain their decisions and actions to human users. This lack benefits but cannot explain their decisions is especially important for the Department of Defense, whose and actions to human users. DARPA’s explainable artificial intelligence (XAI) challenges require developing more intelligent, autonomous, program endeavors to create AI systems and symbiotic systems. Explainable AI will be essential if whose learned models and decisions users are to understand, appropriately trust, and effectively can be understood and appropriately manage these artificially intelligent partners. To address this, trusted by end users. Realizing this goal DARPA launched its explainable artificial intelligence (XAI) requires methods for learning more program in May 2017. DARPA defines explainable AI as AI explainable models, designing effective systems that can explain their rationale to a human user, explanation interfaces, and understanding characterize their strengths and weaknesses, and convey an the psychologic requirements for effective understanding of how they will behave in the future. Naming explanations. The XAI developer teams are fi this program explainable AI (rather than interpretable, addressing the rst two challenges by fl creating ML techniques and developing comprehensible, or transparent AI, for example) re ects ’ principles, strategies, and human-computer DARPA s objective to create more human-understandable AI interaction techniques for generating effec- systems through the use of effective explanations. It also tive explanations. Another XAI team is reflects the XAI team’s interest in the human psychology of addressing the third challenge by summa- explanation, which draws on the vast body of research and rizing, extending, and applying psychologic expertise in the social sciences. theories of explanation to help the XAI evaluator define a suitable evaluation framework, which the developer teams will use to test their systems. The XAI teams completed the first of this 4-year program in May 2018. In a series of ongoing evaluations, the developer teams are assessing how well their XAM systems’ explanations improve user un- derstanding, user trust, and user task performance. 44 AI MAGAZINE Copyright © 2019, Association for the Advancement of Artificial Intelligence. All rights reserved. ISSN 0738-4602 Deep Learning and Security Early AI systems were predominantly logical and Model induction refers to techniques that experi- symbolic; they performed some form of logical in- ment with any given ML model— such as a black ference and could provide a trace of their inference box— to infer an approximate explainable model. For steps, which became the basis for explanation. There example, the model-agnostic explanation system of was substantial work on making these systems more Ribeiro et al. (2016) inferred explanations by ob- explainable, but they fell short of user needs for serving and analyzing the input-output behavior of a comprehension (for example, simply summarizing black box model. the inner workings of a system does not yield a suf- DARPA used these strategies to categorize a portfolio ficient explanation) and proved too brittle against of new ML techniques and provide future practitioners real-world complexities. with a wider range of design options covering the Recent AI success is due largely to new ML tech- performance-explainability trade space. niques that construct models in their internal repre- sentations. These include support vector machines, random forests, probabilistic graphical models, re- XAI Concept and Approach inforcement learning (RL), and deep learning (DL) neural networks. Although these models exhibit high The XAI program’s goal is to create a suite of new or performance, they are opaque. As their use has in- modified ML techniques that produce explainable creased, so has research on explainability from the models that, when combined with effective explanation perspectives of ML (Chakraborty et al. 2017; Ras et al. techniques, enable end users to understand, appropri- 2018) and cognitive psychology (Miller 2017). Simi- ately trust, and effectively manage the emerging gen- larly, many XAI-related workshops have been held re- eration of AI systems. The target of XAI is an end user cently on ML (for example, the International Conference who depends on decisions or recommendations pro- on Machine Learning, the Conference on Neural In- duced by an AI system, or actions taken by it, and formation Processing Systems), AI (for example, the In- therefore needs to understand the system’s rationale. ternational Joint Conference on Artificial Intelligence), For example, an intelligence analyst who receives rec- and HCI (for example, the Conference on Human- ommendations from a big data analytics system needs Computer Interaction, Intelligent User Interfaces) con- to understand why it recommended certain activity for ferences, as have special topic meetings related to XAI. further investigation. Similarly, an operator who tasks There seems to be an inherent tension between ML an autonomous vehicle to drive a route needs to un- performance (for example, predictive accuracy) and derstand the system’s decision-making model to ap- explainability; often the highest-performing methods propriately use it in future missions. Figure 3 illustrates (for example, DL) are the least explainable, and the the XAI concept: provide users with explanations that most explainable (for example, decision trees) are the enable them to understand the system’s overall least accurate. Figure 1 illustrates this with a notional strengths and weaknesses, convey an understanding of graph of the performance-explainability trade-off for how it will behave in future or different situations, and various ML techniques. perhaps permit users to correct the system’smistakes. When DARPA formulated the XAI program, it envi- This user-centered concept poses interrelated re- sioned three broad strategies to improve explainability, search challenges: (1) how to produce more explain- while maintaining a high level of learning performance, able models, (2) how to design explanation interfaces, based on promising research at the time (figure 2): deep and (3) how to understand the psychologic require- explanation, interpretable models, and model induction. ments for effective explanations. The first two chal- Deep explanation refers to modified or hybrid DL lenges are being addressed by the 11 XAI research techniques that learn more explainable features or teams, which are developing new ML techniques to representations or that include explanation genera- produce explainable models, and new principles, tion facilities. Several design choices might produce strategies, and HCI techniques (for example, visuali- more explainable representations (for example, training zation, language understanding, language generation) data selection, architectural layers, loss functions, to generate effective explanations. The third challenge regularization, optimization techniques, training is the focus of another XAI research team that is sequences). Researchers have used deconvolutional summarizing, extending, and applying psychologic networks to visualize convolutional network layers, and theories of explanation. techniques existed for associating semantic concepts The XAI program addresses two operationally rel- with deep network nodes. Approaches for generating evant challenge problem areas (figure 4): data analytics image captions could be extended to train a second deep (classification of events of interest in heterogeneous network that generates explanations without explicitly multimedia data) and autonomy (decision policies identifying the original network’s semantic features. for autonomous systems). These areas represent Interpretable models are ML techniques that learn two important ML problem categories (supervised more structured, interpretable, or causal models. Early learning and RL) and Department of Defense in- examples included Bayesian rule lists (Letham et al. terests (intelligence analysis and autonomous systems). 2015), Bayesian program learning, learning models of The data analytics challenge was motivated by a causal relationships, and use of stochastic grammars common problem: intelligence analysts are presented to learn more interpretable structure. with decisions and recommendations from big data SUMMER 2019 45 Deep Learning and Security Learning Techniques Performance vs. Explainability Neural Nets Graphical Models Deep Ensemble Learning Bayesian Methods Belief Nets SRL Random CRFs HBNs Forests AOGs Statistical MLNs Decision Learning Performance Models Markov Trees Explainability SVMs Models Figure 1. Learning Performance Versus Explainability Trade-Off for Several Categories of Learning Techniques. analytics algorithms and must decide which to report as consists of two phases. Phase 1 (18 months) com- supporting evidence in their analyses and which to menced in May 2017 and includes initial technology pursue further. These algorithms often produce false demonstrations of XAI systems. Phase 2 (30 months) alarms that must be pruned and
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