Team design patterns for moral decisions in hybrid intelligent systems: A case study of bias mitigation

Jip J. van Stijn, Mark A. Neerincx, Annette ten Teije, Steven Vethman

Research output: Contribution to journalConference articleScientificpeer-review

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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 languageEnglish
Number of pages12
JournalCEUR Workshop Proceedings
Volume2846
Publication statusPublished - 2021
Event2021 AAAI Spring Symposium on Combining Machine Learning and Knowledge Engineering, AAAI-MAKE 2021 - Palo Alto, United States
Duration: 22 Mar 202124 Mar 2021

Keywords

  • Bias mitigation
  • Hybrid intelligence
  • Moral decision-making
  • Socio-cognitive engineering
  • Team design patterns
  • Value-sensitive design

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