# trust & safety Trust & safety is an umbrella term for teams and functions at internet companies and service providers that protect users from harmful and unwanted experiences by preventing, detecting, and responding to abuse. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Ensuring Safety in the Generative AI Ecosystem: Protecting Users from Non-Consensual Intimate Content DevFeed: [Ensuring Safety in the Generative AI Ecosystem: Protecting Users from Non-Consensual Intimate Content]() Original publisher: [Read original article]() Author: Android Developers (noreply@blogger.com) Published: 2026-08-25T17:00:00Z Content type: article Language: en Sources: [Android Developers Blog]() Topics: [ai safety](), [Generative AI](), [Google Play](), [trust & safety](), [LineageOS]() Tags: [ai](), [ai-safety](), [generative-ai](), [google-play](), [policy](), [safety](), [testing](), [trust-safety]() ### AI overview Google Play outlines safety requirements and safeguards for generative AI apps to help prevent non-consensual intimate imagery and other harmful content. The guidance covers app-lifecycle reviews, guardrail visibility, adversarial testing, and Android safety practices. ### Source excerpt Posted by Ron Aquino, Senior Director, Trust & Safety, Chrome, Android, and Play At Google Play, user safety and developer success go hand in hand. We continue to see growth in apps with AI generated features, and indeed, adding generative AI into your apps is a great way to unlock incredible creative possibilities. However, AI features also bring new safety challenges - such as the rise of AI-facilitated generation of non-consensual intimate imagery (NCII). Google Play's policies prohibit the facilitation, creation, or distribution of non-consensual sexual content. Harmful applications designed to target, harass, or exploit individuals have absolutely no place on Google Play, and we are committed to enforcing our policies to keep the store a safe space for developers to thrive. We know that the vast majority of you are dedicated to building positive, ethical tools. To protect both your hard work and our shared user base, we are investing heavily in platform protections, technical defenses, and developer resources to stop abuse. How we're safeguarding our shared ecosystem Protecting the platform is a continuous effort. Bad actors attempt to exploit distribution channels, monetization paths, and model boundaries. To help keep the ecosystem fair and safe, we've put a multi-layered defense strategy in place: Safeguards across the app lifecycle: Generative AI features are dynamic and can be less predictable, so safety isn't just a one-time check when you submit your app. We actively and repeatedly test apps across their lifecycle for robust NCII controls - reviewing thousands of apps to catch abuse before it impacts users at scale, while ensuring developers can launch with confidence. Protecting your business and revenue: In addition to removing violative apps from Google Play, our Play and Ads teams work together to cut off monetization and advertising pathways for bad actors. Apps that are suspended or removed for attempting to generate or monetize harmful content suc ## Evaluating AI at Scale: How Thumbtack Approaches Reliability, Safety, and Quality in GenAI DevFeed: [Evaluating AI at Scale: How Thumbtack Approaches Reliability, Safety, and Quality in GenAI]() Original publisher: [Read original article]() Author: Thumbtack Engineering Published: 2026-04-29T00:16:16Z Content type: article Language: en Sources: [Thumbtack Engineering - Medium]() Topics: [Human-AI evaluation](), [genai](), [trust & safety]() Tags: [ai](), [ai-engineering](), [ai-evaluation](), [data-science](), [evaluation](), [genai](), [research](), [safety](), [technical]() ### AI overview Thumbtack describes a learning-driven, exploratory approach to evaluating generative AI experiences. Its strategy combines cross-functional insights with a parallel-path MVP evaluation system to address probabilistic outputs, unsupported claims, harmful content, changing model behavior, and trust-related risks. ### Source excerpt A practical look at how Thumbtack navigates evaluation for emerging AI experiences and what we've learned along the way. By: Shishir Dash, Director of Applied Science & Teja Venkat Kolli, Senior Applied Scientist Evaluating AI at ScaleIntroduction AI is reshaping how people interact with products, and Thumbtack is no exception. We're introducing AI into more aspects of our customer and local service professional (pro) experiences -- from helping customers articulate what they need, to generating helpful summaries, to offering clearer explanations of how pros may fit those needs. But evaluating generative AI is uniquely challenging. Unlike traditional software, its outputs are probabilistic, wide-ranging, and capable of subtle errors: mistakes in tone, inaccuracies, unsupported claims, or harmful assumptions. Rather than attempt to formalize a single rigid evaluation framework, we've taken a learning-driven, exploratory approach, pairing cross-functional insights with a parallel-path MVP evaluation system. This balanced strategy allows us to move quickly while staying grounded in safety, responsibility, and quality. Why AI Evaluation Matters Evaluation is essential because generative AI can produce unsupported or overly strong claims. Sometimes it can misinterpret user intent or vary in style or tone from one version to the next. It can sometimes generate harmful, biased, or inappropriate content. It can also drift over time due to model updates or prompt changes. For a marketplace built on trust, these challenges matter. Customers need accurate guidance; pros need fair, clear representation. Evaluation helps ensure every AI interaction strengthens and not undermines that trust. Our Approach: Exploration, Learning, and MVP Paths The landscape of AI evaluation is still evolving. New research, tooling, and patterns emerge every month. Rather than over-commit to a single approach, we've adopted a mixed strategy rooted in: Exploration and fast learning across multiple pro