The DOI matches its Crossref record; 19 of the 19 references it cites were found in a registry or an index.
Explainable AI based Interventions for Pre-season Decision Making in Fashion Retail
Open accessAbstract
Future of sustainable fashion lies in adoption of AI for a better understanding of consumer shopping behaviour and using this understanding to further optimize product design, development and sourcing to finally reduce the probability of overproducing inventory. Explainability and interpretability are highly effective in increasing the adoption of AI based tools in creative domains like fashion. In a fashion house, stakeholders like buyers, merchandisers and financial planners have a more quantitative approach towards decision making with primary goals of high sales and reduced dead inventory. Whereas, designers have a more intuitive approach based on observing market trends, social media and runways shows. Our goal is to build an explainable new product forecasting tool with capabilities of interventional analysis such that all the stakeholders (with competing goals) can participate in collaborative decision making process of new product design, development and launch.
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“Sajja et al. (2021) demonstrated the use of explainable ML predictions of consumer behavior in helping fashion designers plan for new products.”
From Explainable machine learning for public policy: Use cases, gaps, and research directions (2023) · cites this paper as Sajja et al., 2021
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Explainable machine learning for public policy: Use cases, gaps, and research directions
Amarasinghe, K., Rodolfa, K. T., Lamba, H., & Ghani, R. (2023) · Data & Policy · doi:10.1017/dap.2023.2 · Cited by 73
Metadata
- Authors
- Sajja, S., Aggarwal, N., Mukherjee, S., Manglik, K., Dwivedi, S., & Raykar, V.
- Affiliations
- IBM Research - India (IN)
- Venue
- Proceedings of the 3rd ACM India Joint International Conference on Data Science & Management of Data (8th ACM IKDD CODS & 26th COMAD)
- Publisher
- ACM
- Published
- 2021-01-02
- Pages
- 281-289
- OpenAlex
- W3049433596
- Cited by
- 22
- References
- 19
- Citations by year
- 2026: 1 · 2025: 5 · 2024: 7 · 2023: 3 · 2022: 3 · 2021: 3
- FWCI
- 0.86
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Joint International Conference on Data Science & Management of Data (CODS-COMAD) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Social Sciences › Arts and Humanities › Visual Arts and Performing Arts › Cultural and Historical Studies
- Social Sciences › Decision Sciences › Management Science and Operations Research › Impact of AI and Big Data on Business and Society
- Keywords
- explainable AI, fashion retail, new product forecasting, sustainable fashion, interpretability, collaborative decision-making, fashion design, consumer shopping behaviour
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