EmbeddingGemma 2: an open, lightweight multimodal embedding model
deepmind.google·6d ago
AI Engineering
Hacker News·Aug 31·5 min read
What I Learned About AI Trust from Reconciling over 100B Transactions
TL;DR
Insights from reconciling over 100 billion transactions reveal critical lessons about trust in AI systems, emphasizing the importance of transparency and accountability.
✦ Why It Matters
Evaluate your AI systems for transparency to build user trust and engagement.
Key Takeaways
- Implement a maker-checker system for data verification.
- Create a shared vocabulary for key metrics across teams.
- Develop a conversational analytics interface for easier data access.
- Ensure traceability of data decisions to build trust in AI.
How It Works
Moniepoint's governance framework includes a maker-checker system that separates data uploaders from approvers, ensuring verification. This system is complemented by a conversational analytics interface that allows users to query data in plain language, relying on a governed data layer to provide accurate answers.