Building trust in AI requires transparency, accountability, and human oversight. AI Nutrition Labels, Trust Scores, and Overrides ensure responsible AI.

Google's latest study reveals that almost all developers now use AI coding tools, yet most remain skeptical about their reliability. Discover why trust lags behind technology and what it means for the future of software development.

When I was first tasked with integrating generative AI into Viamo’s IVR platform, serving millions of people across Africa and Asia’s emerging markets, it didn’t take me long to recognise that we...

Open or closed AI ecosystems; where does trust thrive? Balancing security, transparency, and innovation is key to AI’s future. Trust isn’t given; it’s engineered.

This blog explains how memory in LLMs underpins both usability and security, showing why its design is critical to preventing data leaks, emergent exploits, and autonomous chaos... and how Straiker…

62% doubt AI regulation. 58% don't check AI work. Why trust remains the biggest barrier to artificial intelligence.

AI must prioritize explainability, transparency, and governance to earn trust, especially in high-stakes domains where accuracy,

Building trustworthy AI means prioritizing transparency, responsibility, and human oversight—because no one wants an overconfident robot running their life.

Discover Anthropic’s groundbreaking method to prevent harmful AI behaviors. Learn how "persona vectors" can align AI systems with ethical and societal standards.: Read more about How Anthropic Plans...

Open or closed AI ecosystems; where does trust thrive? Balancing security, transparency, and innovation is key to AI’s future. Trust isn’t given; it’s engineered.

Enterprise GenAI: 5 Core Problems Hindering Implementation Problem 1: Reliability LLMs don’t break cleanly—they erode trust one fuzzy answer at a time. Ask the same question twice. Two different outputs. One wrong. One maybe right. Neither verifiable. When stakes are high, you need consistency, traceability, and domain alignment. Out-of-the-box models don’t deliver. RAG alone won’t save you either. We’ve seen it firsthand: 🔸 No logic enforcement 🔸 No confidence metrics 🔸 No accountability If your AI isn’t explainable, it isn’t enterprise-ready. How are you measuring reliability in your stack? Let's discuss. This is just Part 1 of the series. Stick around for more. #EnterpriseAI #GenAI #LLM #AIOrchestration #AITrust #AIForBusiness #FoundersVoice #MCP #AIOps #BuildInPublic

Frontline workers’ trust in employer-provided AI is declining, with many turning to unapproved tools. Companies can address this by: 1) measuring trust using real-time, behavioral metrics; 2) investing in frontline workers’ skills to foster collaboration with AI; 3) designing AI with worker input to ensure co-creation; 4) encouraging experimentation through digital playgrounds and supportive incentives; and 5) empowering team leaders to build trust and momentum, making adoption a movement driven by frontline leadership. These remedies help organizations close the trust gap and unlock AI’s full potential.

Google's latest study reveals that almost all developers now use AI coding tools, yet most remain skeptical about their reliability. Discover why trust lags behind technology and what it means for the future of software development.

A significant challenge lies in deciding what aspects of AI trustworthiness and risk should be included in...