AI RESEARCH BREAKTHROUGH
Informing Design and Research Concerning Conversationally Explainable AI Systems by Collecting and Distilling Human Explanatory Dialogues
This study pioneers an empirical approach to grounding Conversationally Explainable AI (CXAI) systems in human-human explanatory dialogue patterns. By distilling real-world interactions, we identify crucial dialogue capabilities often overlooked in AI design, pushing towards more human-aligned and trustworthy AI explanations.
Quantifying the Impact on Human-AI Interaction
Our analysis provides concrete metrics outlining the scope and depth of human explanatory dialogues, laying a data-driven foundation for future CXAI development.
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Dialogues Collected
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Dialogue Capabilities Identified
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
The Role of Implicit Reasoning in AI Explanations
A primary finding is the crucial role of arguments, particularly enthymemes (truncated arguments), in human-human explanatory dialogues. Explainers often implicitly convey general rules or patterns (warrants) when supporting predictions with specific data, which users are able to infer.
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of dialogues involved AI explanation, highlighting the importance of argumentative structures.
Enterprise Application: Designing CXAI systems that can infer and leverage implicit warrants in their explanations will lead to more natural and effective user understanding. This requires advanced reasoning capabilities beyond mere data presentation.
Empirical Grounding for CXAI Design
Our methodology involves collecting human-human explanatory dialogues, distilling them into human-computer interactions, and iteratively refining a dialogue model to identify core CXAI capabilities. This bridges the gap between theoretical AI explanations and real-world human communication.
Enterprise Process Flow
Collect Human Dialogues
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Manually Rewrite to Human-Computer Interactions
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Iterative Refinement of Dialogue Model
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Identify CXAI Capabilities & Challenges
Enterprise Application: This iterative, human-centered approach ensures that CXAI systems are designed based on actual user behaviors and needs, rather than theoretical assumptions, leading to more practical and adoptable AI solutions.
Addressing User Misconceptions Proactively
The study reveals situations where explainees' questions presuppose false beliefs concerning predictions or feature values. The ability for the explainer to detect and signal these presupposition violations is critical for forming correct user mental models.
| Capability | Previous CXAI Systems (15 studied) | This Study (Observed in human-human) |
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Enterprise Application: Implementing CXAI systems with advanced NLU to detect and correct user presuppositions can prevent significant misunderstandings and build greater trust, especially in high-stakes domains like healthcare or finance.
Balancing Human Intuition and Model Truth
The analysis shows that human explainers often go beyond the model's actual information, using their own knowledge or assumptions. This highlights a critical design choice for CXAI: whether to strictly provide model-grounded explanations or to augment them with external domain knowledge. Our approach prioritizes model-grounded explanations to prevent users from forming incorrect mental models of the AI's capabilities, while acknowledging the human tendency to infer broader causalities.
The Challenge of Explanation Fidelity
In real-world human-human explanations, it's common for explainers to integrate personal insights not directly derived from the AI model. For instance, an operator might speculate about a user's personality based on a music preference that the AI doesn't explicitly link to the prediction. For CXAI, this raises the question: should the system mimic human-like blending of knowledge, or maintain strict fidelity to the model's workings?
Our research suggests that for building trust and accurate understanding, CXAI systems should focus on model-grounded explanations. If external domain knowledge is integrated, it should be clearly demarcated to avoid misleading users about the AI's actual reasoning capabilities. This ensures transparency and helps users form a correct mental model of the AI's internal logic, which is crucial for accountability in critical applications.
Enterprise Application: Enterprises deploying CXAI must decide on the scope of explanation. Prioritizing model fidelity, while potentially less 'human-like', ensures transparency and prevents misattribution of reasoning capabilities to the AI, fostering greater confidence in its predictions.
Calculate Your Potential AI-Driven ROI
Estimate the efficiency gains and cost savings your enterprise could realize by implementing advanced CXAI solutions, guided by human-centered design.
Projected Annual Savings
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Your Path to CXAI Excellence
Our structured implementation roadmap ensures a seamless integration of human-centered CXAI capabilities into your existing systems.
Phase 1: Discovery & Dialogue Analysis
Initial assessment of your current AI systems and user interaction patterns. Collection and distillation of existing human-AI explanatory dialogues to identify specific CXAI desiderata tailored to your domain.
Phase 2: Dialogue Model & NLU/NLG Development
Design and development of a custom dialogue management model, focusing on the identified capabilities (e.g., argumentation, presupposition handling). Iterative development of Natural Language Understanding and Generation components.
Phase 3: Integration & User-Centric Validation
Seamless integration of the CXAI module with your predictive models. Rigorous testing with end-users to validate understanding, trust, and decision-making improvements, ensuring alignment with human communicative strategies.
Phase 4: Continuous Improvement & Scaling
Deployment with ongoing monitoring and feedback loops. Iterative enhancements based on real-world usage data and evolving user needs to ensure long-term effectiveness and scalability of your CXAI solution.
Ready to Build Human-Centered AI Explanations?
Leverage our empirical research and cutting-edge methodology to design conversationally explainable AI systems that truly resonate with your users. Book a consultation to explore how we can tailor these insights to your enterprise.
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