The increasing prevalence of artificial intelligence (AI) will likely lead to new interactions and impacts for the general public. An understanding of people’s perceptions of AI can be leveraged to design and deploy AI systems toward human needs and values. We conducted semi-structured interviews with 25 individuals in the general public and 20 AI experts in the United States (U.S.) to assess perceptions of AI across levels of expertise. Qualitative analysis revealed that ideas about humanness and ethics were central to perceptions of AI in both groups. Humanness, the set of traits considered to distinguish humans from other intelligent actors, was used to articulate beliefs about AI’s characteristics. Ethics arose in discussions of the role of technology in society and centered around views of AI as made and used by people. General public and expert participants expressed similar perceptions of AI, but articulated beliefs slightly differently. We discuss the implications of humanness-related beliefs and ethical concerns for AI development and deployment.

Users of biometric systems have expanded from traditional law enforcement operators to the general population with diverse backgrounds. Past research has shown that usability was improved when users received real-time system feedback regarding their hand positioning and how to correct any undesirable positions. Users can perform fingerprint self-captures successfully by following real-time textual corrective instructions as reported by Choong, Theofanos and Guan. This paper builds on their work with two research questions: what symbols best convey the textual instructions, and, can diverse international users perform ten-print fingerprint self-captures by following graphics-only guidance with no language? We developed a prototype biometric self-capture kiosk to investigate these research questions. A total of 140 subjects participated in two controlled experiments. The findings show that the self-capture prototype is a highly usable system with evidence of great potential for supporting diverse users. By following the real-time graphics-only guidance, participants quickly learned, and were comfortable and confident to capture their own fingerprints without any assistance.

Compared with traditional password and other identification methods, biometrics such as face, iris, and fingerprints for automatic personal identification and verification have many advantages, and are increasingly gaining popularity in all kinds of applications. As the technologies mature, the community has begun to realize that usability has great impact on the final accuracy and efficiency of a biometric system. Although research has shown that effective user feedback can improve the quality of the fingerprint images captured and user satisfaction, currently user feedback information of fingerprint devices used in real world applications is very limited. We design a rich, quality-driven interactive real-time user feedback mechanism for unattended fingerprint kiosk. The system aims to improve the quality of biometric samples during the acquisition process by feeding rich information back to the user instantaneously by measuring objective parameters of the image. The paper proposes an innovative, cost-efficient, real-time algorithm for fingertip detection, slap/thumb rotation detection, and finger region intensity estimation. The paper provides detailed information on the technical solution and its implementation. Preliminary results show that the methodology can potentially increase efficiency, effectiveness, and user satisfaction of a fingerprint biometric system.

This paper discusses the development of a test bed to evaluate the combined performance of the human operator and an explosive ordnance disposal robot. We have other means of evaluating the capabilities of the robots but for the robots to be truly useful it is necessary to understand how effectively and efficiently operators will be able to use these robots in critical situations. In this paper we discuss the tasks developed for the test bed and how we are going about development of the metrics for assessing the human-robot performance and, more specifically, the human-robot user interface.