Designing with Data: Human-Algorithm Collaboration in the Ultra-Fast Fashion Industry

Margot Hanley1

1 Cornell Tech, United States

While scholarship to date on human-machine collaboration has focused on characterizing human-in-the-loop decision making [1] for high-stakes professions such as the judiciary [2], it has dedicated less attention to creative industries—the exception being scholars studying journalists working with algorithms [3, 4] and musicians working with metrics [5]. This paper examines how the practice of fashion design is transforming with the extensive integration of algorithms and big data in the design process. The fashion industry provides a rich field for addressing questions concerning the division of human and machine judgment in creative work, as a site in which the use of algorithms is guiding and making design decisions at an accelerating pace. Furthermore, decisions are poised to shape material output in an industry already under scrutiny for its egregious levels of carbon emissions and broadly unsustainable production practices. 

In this paper, we explore human-algorithm collaboration in ultra-fast fashion (UFF) firms—a new cohort of companies distinctive for the extent that they draw inspiration directly from social media platforms, such as TikTok, and for the speed at which they generate new product offerings. These firms increasingly rely on algorithmic systems to drive decisions in the design process and as a result the role of the “auteur” fashion designer is shifting. While traditional firms make use of in-house designers, UFF firms eschew design expertise altogether, using audience A/B testing to determine their designs. Finesse, an “AI driven” fashion company, delegates design to product development executives, who work alongside the firm’s proprietary algorithms. Shein similarly de-prioritizes the role of in-house design expertise, instead selling garments chosen by the company’s proprietary algorithms and analytics system. 

In this paper, we interview ten fashion designers at UFF firms. We find that while algorithmic systems and big data are increasingly prevalent in the design process, the industry still relies on human work: creative professionals drawing from a diverse array of inputs, reflecting their judgment, lived experience, and taste. Our findings respond to the field’s need for empirical work which engages with logics and practices of professions, adding a rich account to the oversimplified discourse around AI and automation. 

References 

[1] Green, Ben, and Yiling Chen. “The principles and limits of algorithm-in-the-loop decision making.” Proceedings of the ACM on Human-Computer Interaction 3.CSCW (2019): 1-24. 

[2] Green, Ben, and Yiling Chen. “Algorithmic risk assessments can alter human decision-making processes in high-stakes government contexts.” Proceedings of the ACM on Human-Computer Interaction 5.CSCW2 (2021): 1-33. 

[3] Christin, Angèle. “Algorithms in practice: Comparing web journalism and criminal justice.” Big Data & Society 4.2 (2017): 2053951717718855. 

[4] Petre, Caitlin. “42. Data-Driven Editorial? Considerations for Working With Audience Metrics.” Towards a Critical Data Practice (2021): 299. 

[5] Baym, Nancy, et al. “Making Sense of Metrics in the Music Industries.” International Journal of Communication 15 (2021): 3418-3441