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Research Papers: Design Theory and Methodology

Exploring Biases Between Human and Machine Generated Designs

[+] Author and Article Information
Christian E. Lopez

Mem. ASME
Industrial and Manufacturing Engineering,
The Pennsylvania State University,
State College, PA 16802
e-mail: cql5441@psu.edu

Scarlett R. Miller

Mem. ASME
Engineering Design and Industrial Engineering,
The Pennsylvania State University,
State College, PA 16802
e-mail: shm13@psu.edu

Conrad S. Tucker

Mem. ASME
Engineering Design and Industrial Engineering,
The Pennsylvania State University,
State College, PA 16802
e-mail: ctucker4@psu.edu

1Corresponding author.

2Engineering Design, The Pennsylvania State University, 213 N Hammond Building, State College, PA 16802.

Contributed by the Design Theory and Methodology Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received June 30, 2018; final manuscript received October 26, 2018; published online December 20, 2018. Assoc. Editor: Tahira Reid.

J. Mech. Des 141(2), 021104 (Dec 20, 2018) (10 pages) Paper No: MD-18-1525; doi: 10.1115/1.4041857 History: Received June 30, 2018; Revised October 26, 2018

The objective of this work is to explore the possible biases that individuals may have toward the perceived functionality of machine generated designs, compared to human created designs. Toward this end, 1187 participants were recruited via Amazon mechanical Turk (AMT) to analyze the perceived functional characteristics of both human created two-dimensional (2D) sketches and sketches generated by a deep learning generative model. In addition, a computer simulation was used to test the capability of the sketched ideas to perform their intended function and explore the validity of participants' responses. The results reveal that both participants and computer simulation evaluations were in agreement, indicating that sketches generated via the deep generative design model were more likely to perform their intended function, compared to human created sketches used to train the model. The results also reveal that participants were subject to biases while evaluating the sketches, and their age and domain knowledge were positively correlated with their perceived functionality of sketches. The results provide evidence that supports the capabilities of deep learning generative design tools to generate functional ideas and their potential to assist designers in creative tasks such as ideation.

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Figures

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Fig. 1

Example of human and computer generated boat sketches

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Fig. 2

Instruction page from questionnaire

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Fig. 3

Computer simulation environment in Unity

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