AI2030 Responsible AI Tools Marketplace

Key Takeaways

Current XAI methods fail to serve everyday users: The paper demonstrates through experiments that "explanations generated by prominent XAI techniques might not be effective in helping users understand and make decisions," revealing a critical gap between technical explainability and practical usability for household members.
Smart home adoption depends on trustworthy explanations: The research identifies explainability, trust, transparency, accountability, and fairness as key challenges preventing wider adoption of AI-enabled smart home systems, suggesting that addressing these concerns through better explanations is essential for consumer acceptance.
Technical explanations aren't enough for non-experts: While state-of-the-art XAI methods benefit AI developers and practitioners, the paper emphasizes that "they may not be easily understood by general users," highlighting the need to bridge the communication gap between how AI systems explain themselves and how ordinary people actually understand them.
Human-centered design is necessary, not optional: The paper advocates for "human-centered XAI methods" and emphasizes "the importance of delivering readily comprehensible explanations to enhance user satisfaction," arguing that explanations must be designed with the end-user's needs and comprehension level in mind rather than technical accuracy alone.
Human-computer interaction expertise is critical: The research highlights "relevant human-computer interaction (HCI) methodologies" as essential to solving the explainability problem, suggesting that responsible AI in smart homes requires collaboration between AI researchers and HCI specialists.

Abstract

Smart home systems are gaining popularity as homeowners strive to enhance their living and working environments while minimizing energy consumption. However, the adoption of artificial intelligence (AI)-enabled decision-making models in smart home systems faces challenges due to the complexity and black-box nature of these systems, leading to concerns about explainability, trust, transparency, accountability, and fairness. The emerging field of explainable artificial intelligence (XAI) addresses these issues by providing explanations for the models' decisions and actions. While state-of-the-art XAI methods are beneficial for AI developers and practitioners, they may not be easily understood by general users, particularly household members. This paper advocates for human-centered XAI methods, emphasizing the importance of delivering readily comprehensible explanations to enhance user satisfaction and drive the adoption of smart home systems. We review state-of-the-art XAI methods and prior studies focusing on human-centered explanations for general users in the context of smart home applications. Through experiments on two smart home application scenarios, we demonstrate that explanations generated by prominent XAI techniques might not be effective in helping users understand and make decisions. We thus argue for the necessity of a human-centric approach in representing explanations in smart home systems and highlight relevant human-computer interaction (HCI) methodologies, including user studies, prototyping, technology probes analysis, and heuristic evaluation, that can be employed to generate and present human-centered explanations to users.

This is the pre-print version of our accepted paper at the 2nd World Conference on eXplainable Artificial Intelligence (xAI2024), which will be held in Valletta, Malta in 17-19 July, 2024

Key Takeaways

Current XAI methods fail to serve everyday users: The paper demonstrates through experiments that "explanations generated by prominent XAI techniques might not be effective in helping users understand and make decisions," revealing a critical gap between technical explainability and practical usability for household members.
Smart home adoption depends on trustworthy explanations: The research identifies explainability, trust, transparency, accountability, and fairness as key challenges preventing wider adoption of AI-enabled smart home systems, suggesting that addressing these concerns through better explanations is essential for consumer acceptance.
Technical explanations aren't enough for non-experts: While state-of-the-art XAI methods benefit AI developers and practitioners, the paper emphasizes that "they may not be easily understood by general users," highlighting the need to bridge the communication gap between how AI systems explain themselves and how ordinary people actually understand them.
Human-centered design is necessary, not optional: The paper advocates for "human-centered XAI methods" and emphasizes "the importance of delivering readily comprehensible explanations to enhance user satisfaction," arguing that explanations must be designed with the end-user's needs and comprehension level in mind rather than technical accuracy alone.
Human-computer interaction expertise is critical: The research highlights "relevant human-computer interaction (HCI) methodologies" as essential to solving the explainability problem, suggesting that responsible AI in smart homes requires collaboration between AI researchers and HCI specialists.

Abstract

This is the pre-print version of our accepted paper at the 2nd World Conference on eXplainable Artificial Intelligence (xAI2024), which will be held in Valletta, Malta in 17-19 July, 2024

Explaining AI Decisions: Towards Achieving Human-Centered Explainability in Smart Home Environments

Key Takeaways

Abstract

Paper Info

Responsible AI Topics

Links

Explaining AI Decisions: Towards Achieving Human-Centered Explainability in Smart Home Environments

Key Takeaways

Abstract

Paper Info

Responsible AI Topics

Links