A. Main Factors of Attraction 1. Physical attraction We would like to believe that beauty is only skin deep, and therefore, a trivial determinant of liking. Indeed, when asked what they looked for in a potential date, most people put physical attractiveness at the bottom of their list. Elaine Hatfield randomly matched incoming students at the University of Minnesota for a blind date. The students previously had been given a battery of personality tests. Intelligence, masculinity, femininity, dominance, submission, dependence, independence, sensitivity and sincerity had little effect on liking. The one determinant of whether or not a couple liked each other and actually repeated their date was their physical attractiveness.

http://www.airitifile.com/al/15603822/5358/15603822-200004-x-48-39-46-a.pdf

從人際吸引(interpersonal attraction)的觀點談體育課中師生的互動

A surge of interest in explainable AI (XAI) has led to a vast collection of algorithmic work on the topic. While many recognize the necessity to incorporate explainability features in AI systems, how to address real-world user needs for understanding AI remains an open question. By interviewing 20 UX and design practitioners working on various AI products, we seek to identify gaps between the current XAI algorithmic work and practices to create explainable AI products. To do so, we develop an algorithm-informed XAI question bank in which user needs for explainability are represented as prototypical questions users might ask about the AI, and use it as a study probe. Our work contributes insights into the design space of XAI, informs efforts to support design practices in this space, and identifies opportunities for future XAI work. We also provide an extended XAI question bank and discuss how it can be used for creating user-centered XAI.

Questioning the AI: Informing Design Practices for Explainable AI User Experiences

Well-designed technologies that offer high levels of human control and high levels of computer automation can increase human performance, leading to wider adoption. The Human-Centered Artificial In...

Human-Centered Artificial Intelligence: Reliable, Safe & Trustworthy

The need for interpretable and accountable intelligent systems grows along with the prevalence of artificial intelligence applications used in everyday life. Explainable intelligent systems are designed to self-explain the reasoning behind system decisions and predictions, and researchers from different disciplines work together to define, design, and evaluate interpretable systems. However, scholars from different disciplines focus on different objectives and fairly independent topics of interpretable machine learning research, which poses challenges for identifying appropriate design and evaluation methodology and consolidating knowledge across efforts. To this end, this paper presents a survey and framework intended to share knowledge and experiences of XAI design and evaluation methods across multiple disciplines. Aiming to support diverse design goals and evaluation methods in XAI research, after a thorough review of XAI related papers in the fields of machine learning, visualization, and human-computer interaction, we present a categorization of interpretable machine learning design goals and evaluation methods to show a mapping between design goals for different XAI user groups and their evaluation methods. From our findings, we develop a framework with step-by-step design guidelines paired with evaluation methods to close the iterative design and evaluation cycles in multidisciplinary XAI teams. Further, we provide summarized ready-to-use tables of evaluation methods and recommendations for different goals in XAI research.

A Multidisciplinary Survey and Framework for Design and Evaluation of Explainable AI Systems

Many organizations have published principles intended to guide the ethical development and deployment of AI systems; however, their abstract nature makes them difficult to operationalize. Some organizations have therefore produced AI ethics checklists, as well as checklists for more specific concepts, such as fairness, as applied to AI systems. But unless checklists are grounded in practitioners' needs, they may be misused. To understand the role of checklists in AI ethics, we conducted an iterative co-design process with 48 practitioners, focusing on fairness. We co-designed an AI fairness checklist and identified desiderata and concerns for AI fairness checklists in general. We found that AI fairness checklists could provide organizational infrastructure for formalizing ad-hoc processes and empowering individual advocates. We highlight aspects of organizational culture that may impact the efficacy of AI fairness checklists, and suggest future design directions.

https://dl.acm.org/doi/pdf/10.1145/3313831.3376445

Co-Designing Checklists to Understand Organizational Challenges and Opportunities around Fairness in AI

Advances in artificial intelligence (AI) frame opportunities and challenges for user interface design. Principles for human-AI interaction have been discussed in the human-computer interaction community for over two decades, but more study and innovation are needed in light of advances in AI and the growing uses of AI technologies in human-facing applications. We propose 18 generally applicable design guidelines for human-AI interaction. These guidelines are validated through multiple rounds of evaluation including a user study with 49 design practitioners who tested the guidelines against 20 popular AI-infused products. The results verify the relevance of the guidelines over a spectrum of interaction scenarios and reveal gaps in our knowledge, highlighting opportunities for further research. Based on the evaluations, we believe the set of design guidelines can serve as a resource to practitioners working on the design of applications and features that harness AI technologies, and to researchers interested in the further development of human-AI interaction design principles.

Guidelines for Human-AI Interaction

Software-based agents are becoming increasingly ubiquitous and automated. However, current technology and algorithms are still fallible, which considerably affects users’ trust and interaction with such agents. In this article, we investigate two factors that can engender user trust in agents: reliability and attractiveness of agents. We show that agent reliability is not more important than agent attractiveness. Subjective user ratings of agent trust and perceived accuracy suggest that attractiveness may be even more important than reliability.

/pdf/brains-or-beauty-how-to-engender-trust-in-user-agent-4aqwip0ts1.pdf

Penny Collisson

Papers

Guidelines for Human-AI Interaction

Brains or Beauty: How to Engender Trust in User-Agent Interactions