Team design patterns for moral decisions in hybrid intelligent systems: A case study of bias mitigation
conference paper
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. © 2021 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). CEUR Workshop Proceedings (CEUR-WS.org).
Topics
TNO Identifier
956060
ISSN
16130073
Publisher
Ceur-WS
Source title
CEUR Workshop Proceedings: AAAI Spring Symposium on Combining Machine Learning and Knowledge Engineering, AAAI-MAKE 2021; Palo Alto; United States; 22 March 2021 through 24 March 2021