Explainable Artificial Intelligence (XAI) refers to methods and techniques seeking to make AI’s decision-making processes more transparent and understandable, and provide insights into the outcome of a ML-model (Gerlings, Shollo and Constantiou, 2021). A part of this process includes integrating psychology research techniques to will explore three key areas: understanding cognitive models, applying behavioral research methods, and enhancing human-AI interaction through user-centered design.
Cognitive Models and Their Relevance to XAI
Cognitive models have long been used in psychology to understand how humans perceive, think, and make decisions. These models provide a structured way of representing mental processes and can be invaluable in the realm of XAI.
Cognitive Architectures
Cognitive architectures, such as ACT-R (Adaptive Control of Thought-Rational) and SOAR (State, Operator, and Result), have been developed to simulate human cognitive processes. These architectures can be used to inform the development of AI systems, ensuring that they mimic human-like reasoning processes. By incorporating cognitive models into AI systems, we can create more intuitive and human-like explanations for the AI’s decisions.
For instance, if an AI system uses a cognitive architecture to simulate human decision-making, it can provide explanations that resonate with human users. This alignment with human cognitive processes makes the AI’s actions more predictable and understandable, thereby enhancing trust and usability.
Mental Models
Mental models refer to the internal representations or cognitive structures that individuals create to understand and interact with the world around them (Westbrook, 2006). In the context of XAI, understanding users’ mental models is crucial. If an AI system’s explanations align with the user’s mental model, the user is more likely to comprehend and accept the AI’s decisions.
Psychologists use various techniques, such as think-aloud protocols and cognitive task analysis, to study mental models. These techniques can be employed in XAI research to gather insights into how users perceive and interpret AI-generated explanations. By doing so, we can design AI systems that provide explanations in a way that matches the user’s mental framework, leading to better user satisfaction and trust.
Behavioral Research Methods in XAI
Behavioral research methods have been pivotal in psychology for studying human behavior, cognition, and emotions. These methods can be effectively applied to XAI to evaluate and improve the explainability of AI systems.
Experimental Designs
Controlled experiments are a staple in psychological research, being commonly used to evaluating interfaces (e.g. McGuffin and Balakrishnan, 2005; as cited on Blanford, Cox and Cairns, 2008) and styles of interaction (e.g. Moyles and Cockburn, 2005; as cited on Blanford, Cox and Cairns, 2008), and to understanding cognition in the context of interactions with systems (e.g. Li et al, 2006; as cited on Blanford, Cox and Cairns, 2008). Integrating this approach into XAI is allowing researchers to isolate and examine the effects of specific variables on behavior. In the context of XAI, experimental designs can be used to assess the effectiveness of different explanation strategies.
For example, researchers can design experiments to compare the comprehensibility and effectiveness of various types of explanations provided by an AI system. Participants could be asked to perform tasks with the assistance of AI systems that offer different explanation formats, such as textual descriptions, visual aids, or interactive elements. By measuring task performance, user satisfaction, and trust in the AI, researchers can determine which explanation strategies are most effective.
Surveys and Questionnaires
Surveys and questionnaires are widely used in psychology to gather self-reported data on attitudes, beliefs, and experiences. These tools can be adapted for use in XAI research to collect feedback from users regarding their perceptions of AI explanations.
Surveys can be designed to assess various aspects of explainability, such as clarity, completeness, and relevance of the explanations. Additionally, questionnaires can probe deeper into users’ cognitive and emotional responses to the explanations, providing valuable insights into how different explanation strategies impact user experience. By analyzing this data, researchers can refine AI explanation methods to better meet user needs.
Observational Studies
Observational studies involve systematically observing and recording behavior in naturalistic settings. In XAI research, observational methods can be employed to study how users interact with AI systems in real-world scenarios.
For instance, researchers can observe how users seek explanations from AI systems during decision-making processes, noting patterns and challenges that arise. This observational data can reveal important contextual factors that influence the effectiveness of AI explanations. By understanding these factors, researchers can develop more context-aware explanation strategies that enhance user comprehension and trust.
Enhancing Human-AI Interaction through User-Centered Design
User-centered design (UCD) is an approach that prioritizes the needs, preferences, and limitations of end-users throughout the design process. Integrating UCD principles into XAI development ensures that AI systems provide explanations that are not only technically accurate but also user-friendly and meaningful.
Participatory Design
Participatory design involves engaging users as active participants in the design process. This collaborative approach can be highly beneficial in XAI research, as it allows users to contribute their perspectives and preferences regarding AI explanations.
By involving users in the design of explanation interfaces, researchers can gather valuable feedback on what types of explanations are most helpful and how they should be presented. Participatory design sessions can include activities such as co-design workshops, where users work alongside designers and developers to create prototype explanations and interfaces. This iterative process ensures that the final product aligns with user needs and expectations.
Usability Testing
Usability testing is a core component of UCD, involving the evaluation of a product’s ease of use and overall user experience. In the context of XAI, usability testing can be used to assess the effectiveness of explanation interfaces and identify areas for improvement.
During usability testing sessions, participants interact with AI systems that provide explanations, performing tasks and providing feedback on their experience. Researchers can use various metrics, such as task completion time, error rates, and user satisfaction ratings, to evaluate the usability of the explanations. By analyzing these metrics, researchers can identify usability issues and refine explanation interfaces to enhance user comprehension and engagement.
Iterative Design and Prototyping
The iterative design process involves creating and refining prototypes based on user feedback. This approach is essential for developing effective XAI systems, as it allows researchers to continually improve explanations based on real user input.
By creating low-fidelity prototypes of explanation interfaces, researchers can quickly test different designs and gather feedback from users. These prototypes can then be iteratively refined, incorporating user suggestions and addressing identified issues. This iterative process ensures that the final explanation interfaces are user-centered and effectively support users in understanding AI decisions.
Conclusion
The integration of psychology research techniques into Explainable Artificial Intelligence (XAI) holds immense potential for enhancing the transparency and usability of AI systems. By leveraging cognitive models, behavioral research methods, and user-centered design principles, we can develop AI systems that provide explanations that are not only technically accurate but also intuitive and meaningful to users. As the field of XAI continues to evolve, the collaboration between psychologists and AI researchers will be crucial in creating AI systems that are both powerful and comprehensible, ultimately fostering greater trust and acceptance of AI technologies.
References
Gerlings, J, Shollo, A & Constantiou, I (2021). Reviewing the Need for Explainable Artificial Intelligence (xAI). https://arxiv.org/pdf/2012.01007
Westbrook, L (2006). Mental models: a theoretical overview and preliminary study. https://www.researchgate.net/publication/220195846_Mental_models_A_theoretical_overview_and_preliminary_study