Fashion Retailers Using AI - This Is What You Must Know: Insights from Experts' Discussion

Last week, leading experts from academia, industry, and regulatory backgrounds gathered to discuss the legal and commercial implications of AI explainability, with a particular focus on its impact in retail. Hosted by Prof. Shlomit Yaniski Ravid of Yale Law and Fordham Law, the panel brought together thought leaders to address the growing need for transparency in AI-driven decision-making, emphasizing the importance of ensuring AI operates within ethical and legal parameters and the need to "open the black box" of AI decision-making.

Regulatory Challenges and the New AI Standard ISO 42001

Tony Porter, former Surveillance Camera Commissioner for the UK Home Office, provided insights into regulatory challenges surrounding AI transparency. He highlighted the significance of ISO 42001, the international standard for AI management systems, which offers a framework for responsible AI governance. "Regulations are evolving rapidly, but standards like ISO 42001 provide organizations with a structured approach to balancing innovation with accountability," Porter noted.

AI Explainability Webinar. Image credit - Fordham University

AI Explainability in Retail: Industry Perspectives

The panel featured representatives from leading AI companies, who shared how their organizations implement transparency in AI systems, particularly in retail applications.

Pini Usha from Buffers.ai shared insights on AI-driven inventory optimization, a critical application in retail. Buffers.ai serves medium to large retail and manufacturing brands, including H&M, P&G, and Toshiba. The company helps retailers, particularly in the fashion industry, tackle inventory optimization challenges such as forecasting, replenishment, and assortment planning. By ensuring the right product quantities are delivered to the right locations, Buffers.ai helps prevent stockouts and excess inventory.

Pini Usha, regularly meeting with fashion retailer CEOs and VP supply chain to screen supply chain processes, leveraging expertise and deep industry knowledge to pinpoint areas where they might be holding too much stock, missing out on top-selling items or optimizing their assortment for maximum profitability.

Unlike traditional ERP solutions, Buffers.ai offers a full-SaaS ERP plugin that integrates seamlessly with systems like SAP and Priority, delivering measurable ROI within months. "Transparency is key. If businesses cannot understand how AI predicts demand fluctuations or supply chain risks, they will be hesitant to rely on it," Usha noted.

Buffers.ai Inventory Optimization Platform. Credit: Buffers.ai

Buffers.ai integrates explainability tools that allow clients to visualize and adjust AI-driven forecasts, ensuring alignment with real-time business operations and market trends. For example, when placing a new product with no historical data, the system analyzes similar product trends, store characteristics, and local demand signals. If a branch has historically shown strong demand for comparable items, the system might recommend a higher quantity there—even without past data for the new product. Similarly, when allocating inventory between branches and online stores, the system details factors like regional sales performance, customer traffic patterns, and online conversion rates to explain its recommendations.

Facial Recognition in Retail

Matan Noga from Corsight AI discussed the role of explainability in facial recognition technology, which is increasingly used in retail for security and customer experience enhancement. Corsight AI specializes in real-world facial recognition, providing solutions to law enforcement, airports, malls, and retailers. "We focus on real-time, high-speed recognition while ensuring compliance with privacy laws and ethical AI standards," Noga said.

Transforming Legal Decision-Making

Alex Zilberman from Chamelio, a legal intelligence platform, addressed the role of AI in corporate legal teams. "Trust in AI-powered legal tools comes from transparency. Our system ensures professionals understand the source of every recommendation," Zilberman said. When asked how Chamelio handles AI hallucinations, Zilberman explained that the system flags uncertainties and asks for human input when encountering unprecedented scenarios, such as a clause with no precedent or conflicting legal terms. This ensures legal professionals remain in control of critical decisions.

AI-Powered Image Intelligence

Daphne Tapia from ImiSight highlighted the importance of explainability in AI-powered image intelligence, particularly in high-stakes applications like border security and environmental monitoring. "AI explainability means understanding why a specific object or change was detected," Tapia said. ImiSight ensures its AI remains robust and reliable by continuously refining its models based on real-world data and user feedback. The company collaborates with regulatory agencies to ensure its AI meets international compliance standards.

The panel underscored the critical role of AI explainability in fostering trust, accountability, and ethical use of AI technologies, particularly in retail and other high-stakes industries. By prioritizing transparency and human oversight, organizations can ensure AI systems are both effective and trustworthy, aligning with evolving regulatory standards and public expectations.