# AI Transparency Published articles for AI Transparency. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## The gap between brand and consumer perceptions of AI agent disclosure DevFeed: [The gap between brand and consumer perceptions of AI agent disclosure]() Original publisher: [Read original article]() Author: Jesse Sumrak Published: 2026-08-31T00:00:00Z Content type: opinion Language: en Sources: [Twilio Blog]() Topics: [AI Agent](), [Artificial Intelligence](), [Support]() Tags: [ai](), [ai-agent](), [ai-transparency](), [customer-experience](), [industry-insights](), [trust](), [widget]() ### AI overview Twilio research finds that 81% of brands say their AI agents identify themselves, while only 22% of consumers report experiencing that disclosure. The article examines how unclear or inconsistent disclosures create a transparency and trust gap and recommends identifying the AI clearly at the start of interactions. ### Source excerpt 81% of brands say their AI identifies itself. Only 22% of consumers agree. Learn why the gap exists and how to close it. ## Why 78% of customers try to bypass your AI agent DevFeed: [Why 78% of customers try to bypass your AI agent]() Original publisher: [Read original article]() Author: Jesse Sumrak Published: 2026-08-28T00:00:00Z Content type: article Language: en Sources: [Twilio Blog]() Topics: [AI Agent](), [Support](), [Automation](), [Artificial Intelligence]() Tags: [agents](), [ai](), [ai-agent](), [ai-transparency](), [automation](), [customer-service](), [industry-insights](), [support]() ### AI overview Twilio research reports that 78% of consumers have tried to bypass an AI agent to reach a human, while 63% want an easy escalation path. The article attributes this behavior to concerns about transparency, high-stakes tasks, and inefficient interactions, and describes consumers' preferences for disclosure, human escalation, and reliable AI. ### Source excerpt New Twilio research finds 78% of consumers have tried to bypass an AI agent to reach a human. Here's why (and what to do about it). ## EveryEvalEver aims to standardize AI benchmark reporting and sharing DevFeed: [EveryEvalEver aims to standardize AI benchmark reporting and sharing]() Original publisher: [Read original article]() Author: Kim Martineau Published: 2026-07-23T14:00:00Z Content type: article Language: en Sources: [IBM Research]() Topics: [Artificial Intelligence](), [benchmarking](), [LLM evaluation / benchmarking](), [ibm](), [hugging face](), [AI Models](), [data]() Tags: [ai](), [ai-evaluation](), [ai-models](), [ai-transparency](), [benchmarking](), [fairness-accountability-transparency](), [generative-ai](), [hugging-face](), [ibm](), [news](), [performance](), [reporting]() ### AI overview IBM, Hugging Face, and academic collaborators launched EveryEvalEver to make AI benchmark results easier to compare, replicate, and reuse. The project combines standardized reporting with a crowdsourced database of model evaluation results. ### Source excerpt IBM is part of a global team trying to make AI benchmarking results easier to compare, replicate, and reuse. ## Visible Watermarking with Gradio DevFeed: [Visible Watermarking with Gradio]() Original publisher: [Read original article]() Author: Margaret Mitchell Published: 2025-09-15T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [watermarking](), [gradio](), [hugging face](), [Artificial Intelligence](), [Generative AI]() Tags: [ai-content](), [ai-transparency](), [building](), [ethics](), [gradio](), [guide](), [hugging-face](), [open-source](), [text-generation](), [text-to-image](), [text-to-video](), [watermarking]() ### AI overview Hugging Face explains how Gradio can add visible watermarks to AI-generated images, video, and text in Spaces. The article highlights filename-based, open-image, NumPy-array, QR, custom text, and chatbot watermarking options to support attribution, transparency, and disclosure. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Why AI Trust Will Shape Your Next Decade of Software Development DevFeed: [Why AI Trust Will Shape Your Next Decade of Software Development]() Original publisher: [Read original article]() Author: Brendan Hann Published: 2025-06-24T04:00:00Z Content type: article Language: en Sources: [Blog RSS Feed | Snyk]() Topics: [Artificial Intelligence](), [AI Development](), [Securing AI](), [snyk](), [Security](), [sdlc](), [Platform Engineering](), [AI Strategy](), [prompt injection](), [Large Language Model]() Tags: [2025](), [agentic](), [ai](), [ai-adoption](), [ai-security](), [ai-transparency](), [automation](), [blog](), [code-security](), [compliance](), [developer](), [development](), [engineering](), [governance](), [interest](), [llm](), [platform-engineering](), [prompt-injection](), [risk-management](), [sdlc](), [security](), [snyk](), [snyk-platform](), [software-development](), [speed](), [trust]() ### AI overview The article argues that AI trust is foundational for safe, scalable software development as organizations adopt AI and agentic workflows. It describes AI trust as maintaining control, visibility, security, governance, and continuous risk management while benefiting from faster delivery, greater productivity, and automation. It highlights risks including insecure AI-generated code, prompt injection, data exfiltration, model manipulation, and misconfiguration, and presents Snyk's AI Security Platform as a way to embed risk management and security into the SDLC. ### Source excerpt Discover why AI Trust is crucial for secure, scalable software development in the AI era. Learn how Snyk's AI Security Platform helps you manage risk, enforce policies, and ensure continuous compliance.