Abstract

Design intentions guide design efforts but complex systems can lead to designers’ intentions being eclipsed. Artificial Intelligence systems are examples of complex sociotechnical systems that exercise self-learning, innovation and creativity that can exceed their designers’ imaginations. This paper’s proposition is that sociotechnical systems design offers scope for improved reliability and is built on three features of current design practice. First, design teams seek cooperative cognition to work together but joint understanding can be impoverished by inadequately understood outcome scenarios. Second, design team collaboration is bounded by innate psychological biases which can influence design decisions. Third, some views of risk in design thinking suffer from a limited conception of uncertainty and its influence. These constraints in design practice are examined, considering the reach of Artificial Intelligence as an example design domain, and how such constraints may be addressed in design practice.

Keywords

socio-technical systems, uncertainty, human factors, artificial intelligence

Creative Commons License

Creative Commons Attribution-NonCommercial 4.0 International License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License

Conference Track

Research Paper

COinS
 
Jun 25th, 9:00 AM

Cognitive challenges in complex system design

Design intentions guide design efforts but complex systems can lead to designers’ intentions being eclipsed. Artificial Intelligence systems are examples of complex sociotechnical systems that exercise self-learning, innovation and creativity that can exceed their designers’ imaginations. This paper’s proposition is that sociotechnical systems design offers scope for improved reliability and is built on three features of current design practice. First, design teams seek cooperative cognition to work together but joint understanding can be impoverished by inadequately understood outcome scenarios. Second, design team collaboration is bounded by innate psychological biases which can influence design decisions. Third, some views of risk in design thinking suffer from a limited conception of uncertainty and its influence. These constraints in design practice are examined, considering the reach of Artificial Intelligence as an example design domain, and how such constraints may be addressed in design practice.

 

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