Abstract
Increasing automation in the healthcare sector calls for a Hybrid Intelligence (HI) approach to closely study and design the collaboration of humans and autonomous machines. Ensuring that medical HI systems' decision-making is ethical is key. The use of Team Design Patterns (TDPs) can advance this goal by describing successful and reusable configurations of design problems in which decisions have a moral component and facilitating communication in multidisciplinary teams designing HI systems. For this research, TDPs were developed describing a set of solutions for a design problem in a medical HI system: mitigating harmful biases in machine learning algorithms. The Socio-Cognitive Engineering (SCE) methodology was employed, integrating operational demands, human factors knowledge, and a technological analysis into a set of TDPs. A survey was created to assess the usability of the patterns with regards to their understandability, effectiveness, and generalizability. Results showed that TDPs are a useful method to unambiguously describe solutions for diverse HI design problems with a moral component on varying abstraction levels, usable by a heterogeneous group of multidisciplinary researchers. Additionally, results indicated that the SCE approach and the developed questionnaire are suitable methods for creating and assessing TDPs.
| Original language | English |
|---|---|
| Number of pages | 12 |
| Journal | CEUR Workshop Proceedings |
| Volume | 2846 |
| Publication status | Published - 2021 |
| Event | 2021 AAAI Spring Symposium on Combining Machine Learning and Knowledge Engineering, AAAI-MAKE 2021 - Palo Alto, United States Duration: 22 Mar 2021 → 24 Mar 2021 |
Keywords
- Bias mitigation
- Hybrid intelligence
- Moral decision-making
- Socio-cognitive engineering
- Team design patterns
- Value-sensitive design
