A recent gathering of esteemed experts from academia, industry, and regulatory backgrounds sparked a discussion on the legal and commercial implications of AI explainability, with a focus on its impact on the retail sector. Under the guidance of Professor Shlomit Yaniski Ravid from Yale Law and Fordham Law, the panel convened to address the increasing need for transparency in AI-driven decision-making, emphasizing the importance of ensuring AI operates within ethical and legal boundaries and the necessity of ‘opening the black box’ of AI decision-making.

Regulatory Challenges and the Emergence of the New AI Standard ISO 42001

Tony Porter, former Surveillance Camera Commissioner for the UK Home Office, shared his insights on the regulatory challenges surrounding AI transparency. He highlighted the significance of ISO 42001, the international standard for AI management systems, which provides a framework for responsible AI governance. “As regulations evolve rapidly, standards like ISO 42001 offer organizations a structured approach to balancing innovation with accountability,” Porter noted. The panel discussion, led by Prof. Yaniski Ravid, featured representatives from leading AI companies, who shared their experiences in implementing transparency in AI systems, particularly in retail and legal applications.

Chamelio: Revolutionizing Legal Decision-Making with Explainable AI

Alex Zilberman from Chamelio, a legal intelligence platform designed exclusively for in-house legal teams, addressed the role of AI in corporate legal operations. Chamelio transforms the way in-house legal teams operate through an AI agent that learns from and utilizes the legal knowledge stored in its repository of contracts, policies, compliance documents, corporate records, regulatory filings, and other business-critical legal documents.

Chamelio’s AI agent performs core legal tasks such as extracting important obligations, streamlining contract reviews, monitoring compliance, and delivering actionable insights that would otherwise remain buried in thousands of pages of documents. The platform integrates with existing tools and adapts to a team’s legal knowledge.

“Trust is the primary requirement for building a system that professionals can rely on,” Zilberman stated. “This trust is achieved by providing as much transparency as possible. Our solution enables users to understand the origin of each recommendation, ensuring they can confirm and verify every insight.” Chamelio avoids the ‘black box’ model by allowing legal professionals to trace the reasoning behind AI-generated recommendations.

For instance, when the system encounters unfamiliar areas of a contract, it flags the uncertainty and requests human input, enabling legal professionals to control important decisions, particularly in unprecedented scenarios like clauses with no precedent or conflicting legal terms.

Buffers.ai: Transforming Inventory Optimization

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, helping retailers – particularly in the fashion industry – tackle inventory optimization challenges like forecasting, replenishment, and assortment planning.

Buffers.ai offers a full-SaaS ERP plugin that integrates with systems like SAP and Priority, providing a return on investment within months. “Transparency is key,” Usha said. “If businesses cannot understand how AI predicts demand fluctuations or supply chain risks, they will be hesitant to rely on it.” Buffers.ai integrates explainability tools that enable clients to visualize and adjust AI-driven forecasts, ensuring alignment with real-time business operations and market trends.

Corsight AI: Facial Recognition in Retail and Law Enforcement

Matan Noga from Corsight AI discussed the role of explainability in facial recognition technology, which is increasingly used for security and customer experience enhancement in retail. Corsight AI specializes in real-world facial recognition and provides its solutions to law enforcement, airports, malls, and retailers.

The company’s technology is used for applications like watchlist alerting, locating missing persons, and forensic investigations. Corsight AI differentiates itself by focusing on high-speed, real-time recognition that complies with evolving privacy laws and ethical AI guidelines. The company collaborates with government and commercial clients to promote responsible AI adoption, emphasizing the importance of explainability in building trust and ensuring ethical use.

ImiSight: 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. ImiSight specializes in multi-sensor integration and analysis, utilizing AI/ML algorithms to detect changes, anomalies, and objects in sectors like land encroachment, environmental monitoring, and infrastructure maintenance.

“AI explainability means understanding why a specific object or change was detected,” Tapia said. “We prioritize traceability and transparency to ensure users can trust our system’s outputs.” ImiSight continuously refines its models based on real-world data and user feedback, collaborating with regulatory agencies to ensure its AI meets international compliance standards.

The panel underscored the vital 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.

Watch the full session here.