Related papers: Design principles for a hybrid intelligence decisi…
The increasing deployment of artificial intelligence (AI) tools to inform decision making across diverse areas including healthcare, employment, social benefits, and government policy, presents a serious risk for disabled people, who have…
We propose a one-day transdisciplinary creative workshop in the broad area of HCI focused on multiple opportunities of incorporating participatory design into research and industry practice. This workshop will become a venue to share…
Traditional sociotechnical systems (STS) theory has been widely used, but there are many new characteristics in the STS environment as we enter the intelligence era, resulting in the limitations of traditional STS. Based on the…
What do we want from machine intelligence? We envision machines that are not just tools for thought, but partners in thought: reasonable, insightful, knowledgeable, reliable, and trustworthy systems that think with us. Current artificial…
Humans as designers have quite versatile problem-solving strategies. Computer agents on the other hand can access large scale computational resources to solve certain design problems. Hence, if agents can learn from human behavior, a…
AI systems increasingly support human decision-making across domains of professional, skill-based, and personal activity. While previous work has examined how AI might affect human autonomy globally, the effects of AI on domain-specific…
We present a reinforcement learning based framework for human-centered collaborative systems. The framework is proactive and balances the benefits of timely actions with the risk of taking improper actions by minimizing the total time spent…
Hybrid human-ML systems increasingly make consequential decisions in a wide range of domains. These systems are often introduced with the expectation that the combined human-ML system will achieve complementary performance, that is, the…
Recommender Systems are a subclass of machine learning systems that employ sophisticated information filtering strategies to reduce the search time and suggest the most relevant items to any particular user. Hybrid recommender systems…
AI predictive systems are increasingly embedded in decision making pipelines, shaping high stakes choices once made solely by humans. Yet robust decisions under uncertainty still rely on capabilities that current AI lacks: domain knowledge…
Use of artificial intelligence (AI) in human contexts calls for ethical considerations for the design and development of AI-based systems. However, little knowledge currently exists on how to provide useful and tangible tools that could…
Despite the widespread use of artificial intelligence (AI), designing user experiences (UX) for AI-powered systems remains challenging. UX designers face hurdles understanding AI technologies, such as pre-trained language models, as design…
The integration of machine learning (ML) into spatial design holds immense potential for optimizing space utilization, enhancing functionality, and streamlining design processes. ML can automate tasks, predict performance outcomes, and…
Research exploring how to support decision-making has often used machine learning to automate or assist human decisions. We take an alternative approach for improving decision-making, using machine learning to help stakeholders surface ways…
Calibrated trust in automated systems (Lee and See 2004) is critical for their safe and seamless integration into society. Users should only rely on a system recommendation when it is actually correct and reject it when it is factually…
The opaque nature of many intelligent systems violates established usability principles and thus presents a challenge for human-computer interaction. Research in the field therefore highlights the need for transparency, scrutability,…
Agile system development life cycle (SDLC) focuses on typical functional and non-functional system requirements for developing traditional software systems. However, Artificial Intelligent (AI) systems are different in nature and have…
Human-AI complementarity, the idea that combining human and AI judgments can outperform either alone, offers a promising pathway toward robust oversight of advanced AI systems. However, whether human-AI complementarity can be achieved on…
Over the last 20 years, a very large number of startups have been launched, ranging from mobile application and game providers to enormous corporations that have started as tiny startups. Startups are an important topic for research and…
Decision support systems help decision makers make better decisions in the face of complex decision problems (e.g. investment or policy decisions). Fisheries and Aquaculture is a domain where decision makers face such decisions since they…