AI Explainability: More Than Trust
AI explainability goes beyond trust, fostering human oversight, collaboration, and control, especially for high-stakes decisions and system maintenance.
AI explainability goes beyond trust, fostering human oversight, collaboration, and control, especially for high-stakes decisions and system maintenance.
Despite the hype, true AI agents are elusive. They lack causal understanding, limiting effective autonomous action in varied environments.
ICT leaders: IT costs are rising but it’s an investment in organisational competitiveness. Focus on value realisation through adoption, integration, and process streamlining.
As AI is progressively being adopted across every industry, organisations need to implement an AI Safe Use Policy as a first step to governing the adoption of the technology and mitigate its unique risks.
Latest report assesses GenAI vendors on six key ethics metrics like safeguards and transparency, covering updated solutions comprehensively.
Special Report: The IBRS framework technically evaluates GenAI trust via six core metrics, guiding model selection based on specific project risks.
Google’s universal AI assistant aims for proactive, personalised support via a world model and live capabilities, but caution is advised on its reasoning claims.
Google’s AI-enhanced Dataplex and BigLake updates, leveraging Apache Iceberg, champion open, integrated data management and governance, contrasting with Microsoft’s unified approach.
Microsoft’s Build 2025 announcements, especially around AI and the ‘agentic web’, will increase Azure, GitHub, and Microsoft 365 consumption and costs.