Last Updated: 12 March 2025
Last week, leading experts from academia, industry, and regulatory backgrounds gathered to discuss the legal and commercial implications of AI explainability. The industry and academia panel discussion, hosted by Professor Shlomit Yaniski Ravid of Yale Law and Fordham Law, brought together thought leaders to address the growing need for transparency in AI-driven decision-making.
Dr. Hanan Mandel, an internationally recognized expert in AI, emphasized the importance of ensuring AI operates within ethical and legal parameters, advocating for "opening the black box" of AI decision-making.
Tony Porter, former Surveillance Camera Commissioner for the UK Home Office discussed the significance of ISO 42001 and its role in responsible AI governance.
Daphne Tapia from ImiSight highlighted the necessity of AI explainability in Image Intelligence, emphasizing how clear, traceable AI decisions are critical for applications in environmental monitoring and security.
Matan Noga from Corsight AI highlighted the challenges and solutions in ensuring explainability in real-world facial recognition technology.
Pini Usha from Buffers.ai elaborated on the necessity of explainable AI in supply chain optimization, ensuring businesses can confidently act on AI-driven forecasts.
Alex Zilberman from Chamelio addressed the importance of transparency in AI-powered legal intelligence, ensuring legal professionals retain oversight and control over critical judgments.
As artificial intelligence continues to reshape industries, explainability is becoming a critical requirement, particularly in high-stakes applications like environmental monitoring, border security, and infrastructure assessment. ImiSight recently participated in a panel discussion exploring the importance of AI transparency, regulatory compliance, and responsible AI governance.
Daphne Tapia, ImiSight highlighted how AI models must be both accurate and accountable:
"AI systems are only as effective as the trust they inspire. At ImiSight, we ensure that our image intelligence solutions provide clear, traceable, and interpretable insights. Our technology integrates multi-sensor analysis to detect anomalies, land changes, and infrastructure risks, but what sets us apart is our commitment to explainability—ensuring users understand why a particular detection was made. AI explainability means giving users confidence in the system’s outputs. Our models analyze vast datasets, detect anomalies, and refine results through temporal analysis, making sure that every decision is clear and justifiable."
Daphne Tapia, ImiSight
Pain Point: Governments and conservation organizations struggle to track unauthorized land use, deforestation, and climate-related changes in real time.
ImiSight’s Solution: Our AI models leverage satellite imagery, UAV footage, and other sensor data to monitor environmental changes. By integrating temporal analysis, we can differentiate between natural occurrences and man-made interventions, providing stakeholders with precise, timely alerts.
Pain Point: Security agencies need reliable AI-driven surveillance to detect unauthorized movements and structural vulnerabilities while avoiding false positives that waste time and resources.
ImiSight’s Solution: Our multi-sensor fusion technology integrates radar, thermal imaging, and aerial data to detect and classify security threats. AI explainability ensures that each flagged anomaly includes reasoning behind its classification, allowing for swift and confident decision-making by human operators.
Pain Point: Utility companies and municipalities lack effective tools to monitor infrastructure degradation, leading to unexpected failures and costly repairs.
ImiSight’s Solution: Our AI models assess structural integrity by analyzing imagery from drones and ground sensors, detecting early signs of wear and tear. Explainability features provide engineers with a breakdown of AI-detected risks, confidence scores, and suggested actions, ensuring maintenance is proactive rather than reactive.
ImiSight’s image intelligence platform is designed with user experience and trust in mind, incorporating multiple layers of AI explainability:
The panel discussion featured insights from various industry experts on the role of AI explainability:
Corsight AI: Ethical AI in Facial Recognition Matan Noga from Corsight AI emphasized the importance of transparency in AI-powered facial recognition, particularly in security and law enforcement. Corsight AI ensures its technology complies with privacy laws and ethical AI standards, enabling both public and private sector organizations to leverage AI responsibly.
Buffers.ai: AI-Driven Retail Optimization Pini Usha from Buffers.ai discussed how explainability is critical for AI-driven inventory optimization in retail. With clients like H&M and P&G, Buffers.ai integrates explainability tools into its demand forecasting systems, allowing businesses to visualize AI-driven decisions and adjust them in real time.
Chamelio: Legal AI with Full Transparency Alex Zilberman from Chamelio showcased how explainability is key in AI-powered legal intelligence. Chamelio’s platform extracts obligations from contracts and compliance documents while ensuring every AI-driven recommendation is traceable, verifiable, and free from the ‘black box’ problem—a critical factor in legal decision-making.
AI explainability is not just a technical requirement; it’s a necessity for trust and adoption. ImiSight remains committed to pioneering transparent AI solutions that enhance security, environmental sustainability, and operational efficiency.
By ensuring AI-driven insights are clear, justifiable, and actionable, ImiSight empowers organizations to make informed decisions with confidence.
As AI adoption continues to grow, collaboration between academia, industry, and regulators will be crucial in establishing global standards for AI transparency. Through responsible innovation and continued engagement with stakeholders, ImiSight is shaping the future of explainable AI in image intelligence.
