The aim of this workshop is to connect the artificial intelligence (AI) and design thinking (DT) community to discuss and enable potential opportunities for the design of human-centered AI solutions. Generally, design thinking has become a pervasive innovation approach with a huge impact on the organizational culture and core elements of the innovation process. This human-centered approach to innovation aims at solving wicked problems and enables structure and direction at the same time. Therefore, we put a strong focus on the exploration of challenges with the development or use of AI-based technologies, which may benefit from approaches like human-centered design or design thinking. For instance, the level of AI maturity in intelligent applications is constantly increasing. However, more than 80% of AI applications never reach deployment due a wrong strategic approach, lack of data quality or missing AI awareness in employees and those that do, remain below profitability expectations.
The goal of this workshop is thus twofold. First, the ambition is to understand the main challenges in the design of such applications from a user perspective and identify which stage of the data-science process can be enriched through elements and methodologies of design thinking to increase the overall success of the intended AI application. For instance, the introduction of a strategic selection process within the ideation phase by balancing technical feasibility, desirability and business viability or the application of prototyping to enable rapid iteration and focus on targeted feedback from relevant stakeholders. Second, this workshop also covers several research streams like responsible AI or the explainability of AI (xAI) and discusses how such elements from design-thinking might be applied to support and enable intelligent solutions to increase the end-user acceptance and the overall level of trust.
The intended outcome of the workshop is to propose an AI-DT framework addressing the identified data-science or end-user adoption challenges from a design thinking perspective, through evidence from research, as well as from industrial projects.
The aim of this workshop is to connect the artificial intelligence (AI) and design thinking (DT) community to discuss and enable potential opportunities for the design of human-centered AI solutions. Generally, design thinking has become a pervasive innovation approach with a huge impact on the organizational culture and core elements of the innovation process. This human-centered approach to innovation aims at solving wicked problems and enables structure and direction at the same time. Therefore, we put a strong focus on the exploration of challenges with the development or use of AI-based technologies, which may benefit from approaches like human-centered design or design thinking. For instance, the level of AI maturity in intelligent applications is constantly increasing. However, more than 80% of AI applications never reach deployment due a wrong strategic approach, lack of data quality or missing AI awareness in employees and those that do, remain below profitability expectations.
The goal of this workshop is thus twofold. First, the ambition is to understand the main challenges in the design of such applications from a user perspective and identify which stage of the data-science process can be enriched through elements and methodologies of design thinking to increase the overall success of the intended AI application. For instance, the introduction of a strategic selection process within the ideation phase by balancing technical feasibility, desirability and business viability or the application of prototyping to enable rapid iteration and focus on targeted feedback from relevant stakeholders. Second, this workshop also covers several research streams like responsible AI or the explainability of AI (xAI) and discusses how such elements from design-thinking might be applied to support and enable intelligent solutions to increase the end-user acceptance and the overall level of trust.
The intended outcome of the workshop is to propose an AI-DT framework addressing the identified data-science or end-user adoption challenges from a design thinking perspective, through evidence from research, as well as from industrial projects.
Abstract length:
800 words
Deadline for abstract submission:
25 April 2022
Notification of review outcome:
16 May 2022
Length of paper:
4-9 pages as ‘Poster Extended Abstracts’ in the form of short research papers in the Springer CCIS volumes of the Proceedings to be published after the Conference.
Submissions of 10-20 pages will be included in the LNCS Paper Proceedings to be published after the Conference.
Deadline for camera-ready submission and registration: 31 May 2022
This workshop encourages a wide range of submissions from any disciplinary background: conceptual research papers, case studies and literature reviews.
Relevant topics for this workshop need to cover elements from design thinking (e.g., Explore, Define, Ideate, Prototype, Test) within the context of artificial intelligence, including (but not limited to) any of the following:
Sources:
Liedka, J. (2018). Why Design Thinking Works. Harvard Business Review, pp. 72-79
Verganti, R., Vendraminelli, L., Iansiti, M. (2020). Innovation and Design in the Age of Artifical Intelligence. Journal of Product Management, 37(3), pp. 212-227 https://doi.org/10.1111/jpim.12523
WebLinks:
https://venturebeat.com/2019/07/19/why-do-87-of-data-science-projects-never-make-it-into-production/
https://nexocode.com/blog/posts/applying-design-thinking-to-ai/
This workshop has a duration of 4 hours (two 2-hourly sessions with half an hour break in-between). It will be organized as a remote (on-line) workshop.
Authors are asked to present their papers during the workshop. The presentation time will be time limited, so that there is sufficient time for discussions. The time slot per presentation is expected to be approximately 15 minutes.
Agenda:
Break
Registered Conference participants who do not present a paper during the workshop are also invited to attend.
Accepted papers will be published in the ‘Late Breaking Work’ volumes of the proceedings to appear after the conference. Accepted papers of 4-9 pages will be included in the Springer CCIS "Late Breaking Work - Posters" volume as ‘Poster Extended Abstracts’ in the form of short research papers. Guidelines for the preparation of camera-ready posters will be available in due course.
Accepted papers of 10-20 pages will be included in the "HCII 2022 - Late Breaking Work - Papers" Springer LNCS volumes of the Proceedings. Guidelines for the preparation of camera-ready papers will be available in due course.
For oral presentation of your contribution at the Workshop (and inclusion of your paper in the "Late Breaking Work" volumes of the Proceedings), you need to be registered for the Conference by 31 May 2022.