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research-article

Exploring Biases between Human and Machine Generated Designs

[+] Author and Article Information
Christian Lopez

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

Scarlett Miller

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

Conrad Tucker

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

1Corresponding author.

ASME doi:10.1115/1.4041857 History: Received June 30, 2018; Revised October 26, 2018

Abstract

The objective of this work is to explore the possible biases that individuals may have towards the perceived functionality of machine generated designs, compared to human created designs. Towards this end, 1,187 participants were recruited via Amazon Mechanical Turk to analyze the perceived functional characteristics of both human created 2D sketches as well as 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 a 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.

Copyright (c) 2018 by ASME
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