Enhancing AI Communication: Rationale Generation for Explainable AI

Enhancing AI Communication: Rationale Generation for Explainable AI

Table of Contents:

1. Introduction
2. The Challenge of AI Communication
3. The Goal: Generating Natural Language Explanations
4. The Rationale Generation Approach
5. Data Collection and Training
6. Evaluation of the System
7. Quantitative and Qualitative Analysis
8. Perceptions of the Rationale Systems
9. User Preferences between Rationale Styles
10. Future Directions and Conclusion

Introduction

In this article, we will explore the fascinating world of automated rationale generation and its potential for improving AI communication. We'll Delve into the challenges posed by AI's lack of ability to express motivation and how generating natural language explanations can bridge this gap. We'll explore the rationale generation approach, including the inspiration we draw from related work in philosophy and psychology. We'll also discuss the importance of translating explanations from a language of thought to a language of communication. Lastly, we'll provide an overview of the data collection, training, and evaluation processes used in this research.

The Challenge of AI Communication

One of the biggest hurdles in AI communication is the inability of AI systems to express their motivation. Unlike humans, AI does not possess the capability to think out loud and convey its intentions in a natural language. Consequently, non-technical and even some technical experts struggle to understand and trust these so-called Blackbox systems. Without trust, collaboration becomes difficult. In this article, we explore an aspirational goal of enabling self-driving cars, for example, to think out loud in natural language to enhance user understanding and trust.

The Goal: Generating Natural Language Explanations

The Core objective of this research is to generate plausible natural language explanations that a human would think and communicate in. We call this approach "rationale generation." The rationale is an explanation that justifies an action, and it is generated Based on how a human would reason. Inspired by related work in philosophy of science, mind, and psychology, we aim to translate the language of thought used by AI systems into a language of communication that humans can understand and trust.

The Rationale Generation Approach

To generate rationales, we adopt a machine translation perspective. We treat explanation generation as a translation problem, where we translate the data structures and numbers used by AI systems into natural language. The explanations are collected from humans who perform specific tasks and think out loud in the process. We refer to these rationales as "translations" from the language of thought to the language of communication. In this article, we provide an overview of the pipeline used for rationale generation, including data collection, model training, and evaluation.

Data Collection and Training

Collecting data for rationale generation requires an appropriate environment. To simulate this environment, we developed a game based on the classic arcade game Frogger. In this game, players navigate a frog through obstacles while thinking out loud. We utilized a turn-taking game design to tightly associate actions and explanations. An automated speech-to-text system transcribed the participants' utterances, which were then reviewed and edited in real-time. This data was instrumental in training the sequence-to-sequence neural network for rationale generation.

Evaluation of the System

To evaluate the effectiveness of the rationale generation system, we conducted two experiments. The first experiment focused on user perceptions of the generated rationales. Participants watched videos of Frogger gameplay accompanied by rationales. They rated these rationales along Dimensions such as confidence, human-likeness, adequacy of justification, and understandability. The Second experiment aimed to understand user preferences between different types of rationales. Participants compared rationales generated using a complete view or a focused view configuration. These experiments provided insights into the benefits and limitations of each rationale style.

Quantitative and Qualitative Analysis

The quantitative analysis of user perceptions revealed that the generated rationales outperformed random baselines. However, the qualitative analysis provided deeper insights into the reasoning behind these perceptions. Participants identified important factors such as confidence, human-likeness, and understandability as determinants of their ratings. The analysis also shed light on the role of Detail in the rationales and how it influenced user preferences.

Perceptions of the Rationale Systems

The Perception study highlighted the strengths and weaknesses of the rationale systems. Participants found that rationales conveying a Sense of awareness and long-term planning instilled confidence in the AI system. Human-likeness of the rationales varied depending on participants' perspective on human fallibility. Understandability was affected by the accuracy of environmental descriptions. By analyzing participants' open-ended responses, we gained valuable insights into the factors influencing perceptions of the rationale generation system.

User Preferences between Rationale Styles

The evaluation study comparing complete view and focused view rationales revealed interesting findings. Participants recognized the important difference in the level of detail between the two styles. Complete view rationales, which provided a broad picture of the environment, were preferred for developing mental models and proactive troubleshooting. Focused view rationales, which emphasized raw mechanics, were favored for understanding unexpected behavior. These findings highlight the role of detail in addressing different user needs and preferences.

Future Directions and Conclusion

While this research provides a promising first step towards explainable AI, there is still much work to be done. Future directions include introducing interactivity to the system, allowing users to contest explanations and exploring the role of explanations in team settings. The insights gained from this research can also be applied to domains beyond gaming, such as self-driving cars and cybersecurity. In conclusion, rationale generation offers a valuable approach to enhance AI communication and foster trust between humans and AI systems.

Highlights:

• Rationale generation is a promising approach to enhance AI communication and trust.
• Collection of data through task-based gameplay and thinking out loud.
• Sequence-to-sequence neural network for translating data structures to natural language explanations.
• Quantitative and qualitative evaluation of generated rationales.
• User perceptions influenced by factors such as confidence, human-likeness, and understandability.
• User preferences differ based on the level of detail in rationales.
• Future directions include interactivity and exploring explanations in team settings.

FAQ:

Q: How do You Collect data for rationale generation? A: We developed a game based on Frogger where players think out loud while navigating the game. Automated speech-to-text technology transcribes their utterances, which are reviewed and edited in real-time.

Q: What factors influence user perceptions of the generated rationales? A: User perceptions are influenced by factors such as confidence in the system, human-likeness of the rationales, and understandability of the explanations.

Q: Are there different styles of rationales generated in the system? A: Yes, we explore two styles: complete view and focused view rationales. Complete view rationales provide a holistic picture of the environment, while focused view rationales focus on raw mechanics.

Q: How do users' preferences differ between rationale styles? A: Users prefer complete view rationales for developing mental models and proactive troubleshooting. Focused view rationales are favored for understanding unexpected behavior.

Q: What are the future directions of this research? A: Future directions include introducing interactivity to the system, allowing users to contest explanations, and exploring explanations in team settings. The insights gained can be applied to domains beyond gaming, such as self-driving cars and cybersecurity.