Keynote Speakers
Assoc. Prof. Koen Smit
HU University of Applied Sciences Utrecht, Netherlands
Koen Smit is a University of Applied Sciences professor focusing on Digital Ethics at the HU University of Applied Sciences Utrecht, in the Netherlands. He obtained his PhD in Computer Science in 2018 at the Open Universiteit. His research primarily focuses on the combination of Business Process Management, Business Rules Management, Decision Management, Decision Mining, Digital Twin technology, Social Robotics and Value-Oriented Design methods and techniques. His interest also leans towards how said technological innovations can be designed and implemented in such a way that human and public values are explicitly and adequately considered. He regularly reviews and/or publishes and presents his research contributions at conferences and journals (e.g., HICSS, ICIS, PACIS, AMCIS, PJAIS, JITTA, JMIR, IST, and BPM). Furthermore, he is part of the management team of the Institute for ICT of the same university. He supervises several PhD and Professional Doctorate students on his focus areas.
Title: Designing for Values in Action: A Problem–Solution Approach to VSD
Abstract: Value Sensitive Design (VSD) promises to put human values at the centre of technology, but practitioners routinely struggle to translate VSD’s conceptual ideas and lenses into concrete design processes. This paper closes that gap by reframing VSD around the everyday problems and solutions that designers and public servants face. Using a Design Science Research approach we introduce a practice-oriented framework that maps VSD’s conceptual, empirical and technical investigations onto two actionable spaces, problem and solution, and makes explicit which lens and methods fit each space. The framework also includes a problem typology (technology-driven vs. societal issues) and treats the technical lens orthogonally so teams can better separate design of artifacts from inquiry about values and contexts. Validated retrospectively in three public-service design cases, the model helped clarify roles, guide method choices and improve stakeholder engagement. We close by showing how attendees can apply the framework immediately in their projects, and present possible future directions to further advance the field of ethical design and implementation of IS/IT.
Assoc. Prof. Sen Chai
McGill University, Canada
Sen CHAI is a tenured associate professor of Strategy and Organization at McGill's Desautels Faculty of Management and Desautels Faculty Scholar in Montreal, Canada. Sen's research examines the entire developmental course of creative innovations from idea conception to commercialization, with the goal of helping managers and policymakers better support innovation as well as avoid or manage failures in the hopes of increasing organizations' chances of creating breakthrough ideas. Her current projects include studying the role of anticipation in failure management, both in the context of innovation catastrophes and crowdfunding campaigns. She is also exploring how AI tools affect idea generation in research. Her research has been published in top management journal outlets such as the Strategic Management Journal, Organization Science, Research Policy and MIT Sloan Management Review. In the classroom, Sen delivers courses in innovation management, entrepreneurship and technology strategy to audiences from undergraduate students to executives. Prior to McGill, she taught at ESSEC Business School in Paris and completed a post-doc at the National Bureau of Economic Research in Cambridge, MA. Before her doctoral studies, she worked in the San Francisco and Seattle offices of Deloitte Consulting LLP as a consultant helping clients optimize their business processes. She has also passed all three levels of the CFA curriculum.
Title: How experience moderates the impact of AI suggestions on researchers' perceptions of their ideas
Abstract: At the heart of scientific discovery are researchers who identify ideas worthy of inquiry. In performing research tasks, researchers are increasingly using generative artificial intelligence (AI) technologies—large language models, in particular. However, little is known about how generative AI assists researchers with one of the most fundamental tasks in science—the generation of initial research ideas, and their willingness to adopt the technology for such purpose. We show that one’s research experience is key in understanding how researchers receive generative AI suggestions when formulating new research ideas: experience negatively moderates the effect of generative AI on perceived novelty and impact of the researcher’s idea, and on views of one’s own research agendas. By focusing on the critical moment of idea generation, we extend prior findings showing how generative AI affects various task performance. We provide an initial understanding of how researchers respond to generative AI at a moment when the paradigm of human knowledge production may be shifting from a human only to a joint human-AI model. Our findings have implications for researchers, organizations, and policy, as individuals and industry producing knowledge need to balance building their own expertise and incorporating generative AI into the research process to advance knowledge.