trust

Developers flagged a major privacy concern with Elon Musk's Grok AI model, leading to a trust crisis for the product.

The article discusses the concept of verifiable AI inference, exploring methods like trusted attestation and cryptographic proofs to ensure the authenticity and provenance of AI-generated outputs without rerunning the model.

NVIDIA's blog post highlights how open models like Nemotron enable enterprises and nations to build trustworthy, controllable, and customized AI systems, contrasting with closed models that limit inspection and adaptation.

A discussion on which boring internal tasks users would trust an AI agent to handle, and which tasks they would not.

A researcher demonstrates how to backdoor an open-weight coding model in under $100, raising concerns about trust in Chinese open-weight models like GLM 5.2.

An employee used an AI agent to auto-respond to Slack messages, and it gave a confidently wrong answer about a client deadline, highlighting the risk of trusting tone and fluency over accuracy.

The article argues that before AI agents can be widely deployed, they need verifiable identity and auditability to ensure trust and accountability. The ITU is working on international standards for this.

A pop quiz asking why AI labs pre-train their own models and design their own chips, with multiple choice answers ranging from unit economics to trust issues.

Discusses how the business model for AI agents may increasingly depend on establishing user trust after an initial click or interaction, highlighting the importance of post-click trust in AI-driven services.

This paper introduces psychological competence as a missing dimension in AI evaluation, proposing a conceptual framework to assess how AI systems support user cognition, emotional interpretation, and decision-making in human-facing roles.

This paper proposes an adversarial social epistemology framework for analyzing trust, deception, and inference chains in communicative landscapes involving humans and large language models, and outlines mechanisms for auditing trust breaches.

Explores the future of AI agents collaborating in networks, referencing projects like AnvitaFlow and Moltbook, and raises questions about trust and marketplaces for agent services.

Explores the barriers and concerns preventing developers and enterprises from deploying AI agents in production, including reliability, safety, and security issues.

The actual bottleneck in deploying AI for small businesses is no longer model speed but building trust and defining scope—handing off repetitive decisions that owners already know well is a practical first step.

Explores the reliability and accuracy of running AI models locally, questioning whether users can trust their outputs.

This research explores the concept of constraining a model's learning to only what trusted LoRA adapters can express, aiming to improve safety and reliability in fine-tuning.

The author explores the current state of AI agent-to-agent transactions, questioning how trust, escrow, and disputes are handled in practice, and invites real-world experiences from builders.

The author explores the idea of AI agents having a public, auditable memory to record important decisions, which could enhance trust but also introduce new complexities.

A national survey finds 63% of Americans uncomfortable with AI helping choose candidates and 80% worried about AI bots answering political surveys, highlighting public trust issues at the intersection of AI and democracy.

A practitioner shares hard-won lessons on pricing AI agents for small businesses, arguing that framing them as 'AI employees' with salary-like monthly fees works better than per-seat or cost-plus pricing, and that trust and security concerns must be addressed before price.