Demystifying AI: Insights on Explainability's Role in Legal Tech and Beyond

In a fascinating gathering last week, some of the brightest minds from academia, industry, and regulatory bodies came together to explore the legal and commercial influence of artificial intelligence (AI) explainability—particularly within the retail sector. This insightful meeting, hosted by Professor Shlomit Yaniski Ravid from Yale Law and Fordham Law, aimed to illuminate the increasing demand for transparency in AI-driven decisions. The key takeaway? We need to ‘open the black box’ of AI systems to ensure they operate within ethical and legal boundaries.

Regulatory Challenges: What's the New Standard?

Tony Porter, the former Surveillance Camera Commissioner for the UK Home Office, shed light on the hurdles surrounding AI transparency regulations. He introduced the audience to ISO 42001, an international standard for AI management systems that provides a framework for responsible AI usage. “While legislation is quickly advancing, standards like ISO 42001 help organizations strike a balance between innovation and accountability,” Porter articulated. The panel, led by Prof. Yaniski Ravid, included representatives from renowned AI companies, who shared how they’re navigating transparency in AI systems, especially in legal and retail contexts.

Chamelio: Changing the Game for Legal Teams

Alex Zilberman from Chamelio, a cutting-edge legal intelligence platform tailored for in-house legal teams, spoke passionately about AI's role in reshaping corporate legal operations. Chamelio harnesses an AI agent that learns from a repository filled with contracts, policies, compliance documents, and regulatory filings.

This smart platform takes on core legal chores like extracting key obligations, streamlining contract reviews, and flagging compliance issues—all while delivering meaningful insights that typically hide away in lengthy documents. Zilberman emphasized, “Trust is paramount in building a system that legal professionals rely on.” And they achieve this trust through transparency, allowing users to understand the origins of each recommendation and verification of insights.

Chamelio’s approach sidesteps the problematic 'black box' model by letting lawyers trace the AI's reasoning behind its suggestions. For instance, when the system encounters unfamiliar contract sections, it doesn’t take wild guesses; it flags them for human review. This process ensures that legal professionals maintain control over critical decisions, especially in unique scenarios.

Inventory Optimization with Buffers.ai

Pini Usha from Buffers.ai discussed the innovative ways AI can tackle inventory challenges, particularly in retail. Buffers.ai assists notable brands—think H&M and P&G—by optimizing inventory and addressing headaches like forecasting and replenishment. Their aim? Making sure the right products are at the right place, reducing those frustrating stockouts and excess inventory.

Functioning as a full Software as a Service (SaaS) ERP plugin, Buffers.ai integrates with systems like SAP and Priority, delivering ROI in mere months. “Transparency is crucial here,” Usha stated. If businesses can't grasp how AI predicts trends or supply chain risks, they’re hesitant to adopt these new technologies.

Buffers.ai incorporates explainability tools that help clients visualize and adjust AI-driven forecasts, aligning them with current business realities. For example, when suggesting the stock of a new product for which there's no historical data, the platform analyzes related product trends and regional demand signals, making informed recommendations.

Facial Recognition Advances from Corsight AI

Matan Noga from Corsight AI shared thoughts on the growing role of explainability in facial recognition tech, critical for improving security and customer experiences in retail. Corsight AI focuses on delivering high-speed, real-time recognition solutions that abide by evolving privacy regulations.

Their technology spans applications like missing person searches and watchlist alerting. By emphasizing clarity in their processes, Corsight works hand-in-hand with both governmental and commercial sectors, driving responsible AI use.

ImiSight's AI-Powered Image Analysis

Daphne Tapia from ImiSight highlighted the essential need for explainability within AI-driven image intelligence, especially in critical sectors like border security. ImiSight leverages AI/ML algorithms to track anomalies in various areas like environmental monitoring. “Understanding the reasoning behind detected changes is our priority,” Tapia noted, underscoring the importance of transparency for user trust.

This panel highlighted a vital point: explainability in AI is paramount for trust, accountability, and ethical technology use, particularly across sensitive industries like retail and law enforcement. By prioritizing clarity and human oversight, businesses can ensure their AI systems not only perform effectively but also align with current regulations and public expectations.

For those wanting a deeper dive, you can watch the full session here.