Recently, prominent experts from academic institutions, industry leaders, and regulatory agencies came together to examine the critical legal and commercial consequences of AI explainability, with a specific focus on retail applications. Led by Professor Shlomit Yaniski Ravid from Yale Law and Fordham Law, the gathering underscored the pressing need for clear, transparent AI-driven decisions, stressing that AI must operate within ethical guidelines and legal frameworks.
Understanding Regulatory Trends: ISO 42001 and AI Oversight
Tony Porter, the UK’s former Surveillance Camera Commissioner, highlighted key regulatory challenges and emphasized ISO 42001’s crucial role. This international standard sets guidelines for responsible AI governance, helping organizations achieve a balance between innovation and accountability amidst rapidly changing regulatory environments.
During the session moderated by Professor Yaniski Ravid, executives from various AI-focused companies provided practical examples of transparency in their AI practices, particularly within retail and legal contexts.
Chamelio: Enhancing Legal Decision-Making with Explainable AI
Alex Zilberman of Chamelio, a specialized AI platform built exclusively for in-house legal departments, illustrated how AI streamlines corporate legal processes. Chamelio’s intelligent AI assists legal teams by automating contract analysis, compliance oversight, and obligation tracking, significantly boosting efficiency.
According to Zilberman, building trust among legal professionals hinges on transparency. Chamelio achieves this by clearly presenting the logic behind every recommendation. When uncertainty arises, the system transparently flags such areas, requesting human review instead of guessing. This allows legal experts to confidently guide important decisions, particularly in unprecedented legal scenarios.
Buffers.ai: Transforming Inventory Optimization with Transparent AI
Pini Usha from Buffers.ai discussed AI’s role in optimizing inventory management for large retail and manufacturing clients such as H&M, P&G, and Toshiba. The company focuses on solving complex retail challenges, including demand prediction, inventory replenishment, and product assortment planning, effectively reducing stock shortages and excessive inventory.
Buffers.ai provides seamless integration with ERP solutions like SAP and Priority, ensuring quick returns on investment. Usha emphasized the importance of transparency, noting that businesses must fully understand how AI systems generate forecasts. The platform’s transparent forecasts enable users to visualize factors such as comparable product trends and regional sales histories, enhancing trust and confidence in AI-driven decisions.
Corsight AI: Transparent Facial Recognition for Enhanced Security
Matan Noga from Corsight AI explained the necessity of transparency in facial recognition technology, increasingly utilized in retail for both security and customer engagement purposes. Corsight specializes in high-speed, accurate facial recognition solutions that comply with evolving privacy and ethical guidelines, serving diverse clients from law enforcement to shopping centers.
Corsight actively collaborates with clients to ensure ethical and transparent deployment of facial recognition technology, reinforcing public trust and maintaining compliance with privacy laws.
ImiSight: Reliable AI Image Intelligence
Daphne Tapia from ImiSight highlighted the critical importance of explainability in AI-powered image intelligence applications, crucial in sensitive fields like border security and environmental protection. ImiSight employs advanced AI algorithms to analyze images from multiple sensors, accurately detecting anomalies and changes.
Tapia explained that traceability and clear reasoning behind AI-driven detections are central to user trust. ImiSight constantly refines its algorithms using real-world feedback and collaborates with regulatory agencies to maintain compliance with global standards.
The panel collectively agreed that prioritizing AI transparency and explainability is fundamental to building trust, accountability, and ethical standards, especially in sensitive sectors like retail and legal services. By maintaining human oversight and clear communication of AI decision-making processes, businesses can effectively harness AI’s potential while meeting evolving regulatory expectations and societal standards.
