# human feedback

Human-provided evaluations or annotations used to build, evaluate, or improve AI datasets and models.

This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles.

## Paul Christiano joins OpenAI Foundation Board

DevFeed: [Paul Christiano joins OpenAI Foundation Board](<https://devfeed.tech/articles/paul-christiano-joins-openai-foundation-board-6604.md>)

Original publisher: [Read original article](<https://openai.com/index/paul-christiano-joins-openai-foundation-board>)

Published: 2026-09-09T17:00:00Z

Content type: news

Language: en

Sources: [OpenAI News](<https://devfeed.tech/sources/openai-news.md>)

Topics: [Responsibility & Safety](<https://devfeed.tech/topics/responsibility-safety.md>), [human feedback](<https://devfeed.tech/topics/human-feedback.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [ai-alignment](<https://devfeed.tech/tags/ai-alignment.md>), [company](<https://devfeed.tech/tags/company.md>), [government](<https://devfeed.tech/tags/government.md>), [openai](<https://devfeed.tech/tags/openai.md>), [research](<https://devfeed.tech/tags/research.md>), [rlhf](<https://devfeed.tech/tags/rlhf.md>), [safety](<https://devfeed.tech/tags/safety.md>), [security](<https://devfeed.tech/tags/security.md>)

### AI overview

OpenAI announces Paul Christiano's appointment to the OpenAI Foundation Board and its Safety and Security Committee. The article highlights his experience in AI alignment, frontier AI model evaluation, safety and security risk mitigation, governance, and reinforcement learning from human feedback.

### Source excerpt

Paul Christiano joins the OpenAI Foundation Board and its Safety and Security Committee, bringing experience in AI alignment, safety, and standards.

## Calibrating LLM-Based Population Estimates with Human Validation

DevFeed: [Calibrating LLM-Based Population Estimates with Human Validation](<https://devfeed.tech/articles/calibrating-llm-based-population-estimates-with-human-validation-29997.md>)

Original publisher: [Read original article](<https://engineering.indeedblog.com/blog/2026/08/calibrating-llm-based-population-estimates-with-human-validation/>)

Author: Hiroshi Urata

Published: 2026-08-12T00:29:34Z

Content type: article

Language: en

Sources: [Indeed](<https://devfeed.tech/sources/indeed.md>)

Topics: [Large Language Model](<https://devfeed.tech/topics/llm.md>), [human feedback](<https://devfeed.tech/topics/human-feedback.md>), [data](<https://devfeed.tech/topics/data.md>), [LLM evaluation / benchmarking](<https://devfeed.tech/topics/llm-evaluation-benchmarking.md>)

Tags: [classification](<https://devfeed.tech/tags/classification.md>), [data](<https://devfeed.tech/tags/data.md>), [data-science](<https://devfeed.tech/tags/data-science.md>), [dataset](<https://devfeed.tech/tags/dataset.md>), [evaluation](<https://devfeed.tech/tags/evaluation.md>), [false-negative](<https://devfeed.tech/tags/false-negative.md>), [false-positive](<https://devfeed.tech/tags/false-positive.md>), [llm](<https://devfeed.tech/tags/llm.md>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>), [measurement](<https://devfeed.tech/tags/measurement.md>), [validation](<https://devfeed.tech/tags/validation.md>)

### AI overview

The article explains how human validation can calibrate LLM-based population estimates. It distinguishes an LLM's apparent positive rate from true prevalence, uses human-validated samples to estimate sensitivity and specificity, and applies those error estimates to correct population-level measurements and quantify uncertainty.

### Source excerpt

Key Idea Human validation is not only for evaluating an LLM. It can also calibrate how the LLM is used as a scalable measurement instrument for population estimation. An LLM can classify thousands of records at low cost, but the proportion it classifies as positive is not necessarily the true proportion in the population. By [...]

## Introducing Real World VoiceEQ: Measuring the human quality of voice AI

DevFeed: [Introducing Real World VoiceEQ: Measuring the human quality of voice AI](<https://devfeed.tech/articles/introducing-real-world-voiceeq-measuring-the-human-quality-of-voice-ai-7454.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/real-world-voiceeq>)

Author: David Ayllon; Alice; Jeff Brooks; Franc Camps Febrer; Jakub Piotr Cłapa; Theo Lebryk; Jens Madsen; Olya Ossipova; Sharath Rao; Hoon Shin

Published: 2026-07-15T00:00:00Z

Content type: article

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [voice ai](<https://devfeed.tech/topics/voice-ai.md>), [Benchmark](<https://devfeed.tech/topics/benchmark.md>), [Human-AI evaluation](<https://devfeed.tech/topics/human-ai-evaluation.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [asr](<https://devfeed.tech/topics/asr.md>), [human feedback](<https://devfeed.tech/topics/human-feedback.md>), [Open Source](<https://devfeed.tech/topics/open-source.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [announcement](<https://devfeed.tech/tags/announcement.md>), [audio](<https://devfeed.tech/tags/audio.md>), [benchmark](<https://devfeed.tech/tags/benchmark.md>), [benchmarks](<https://devfeed.tech/tags/benchmarks.md>), [evaluation](<https://devfeed.tech/tags/evaluation.md>), [human-feedback](<https://devfeed.tech/tags/human-feedback.md>), [leaderboard](<https://devfeed.tech/tags/leaderboard.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [reinforcement-learning](<https://devfeed.tech/tags/reinforcement-learning.md>), [speech](<https://devfeed.tech/tags/speech.md>), [voice-ai](<https://devfeed.tech/tags/voice-ai.md>)

### AI overview

Real World VoiceEQ is a benchmark for evaluating the human quality of voice AI beyond latency and word error rate. It measures how voice systems recognize, produce, and respond to acoustic information such as tone, emotion, speaker identity, and background context across ASR, TTS, speech-to-speech, and speech understanding. The benchmark covers more than 40 voice models, 15+ evaluation dimensions, and more than 60 metrics, using over 1 million human ratings.

### Source excerpt

We're on a journey to advance and democratize artificial intelligence through open source and open science.

## Coding Challenge #122 - AI-Powered Contract Review Agent

DevFeed: [Coding Challenge #122 - AI-Powered Contract Review Agent](<https://devfeed.tech/articles/coding-challenge-122-ai-powered-contract-review-agent-29198.md>)

Original publisher: [Read original article](<https://codingchallenges.substack.com/p/coding-challenge-122-ai-powered-contract>)

Author: John Crickett

Published: 2026-05-30T08:01:36Z

Content type: tutorial

Language: en

Sources: [Coding Challenges](<https://devfeed.tech/sources/coding-challenges.md>)

Topics: [Code Challenge](<https://devfeed.tech/topics/code-challenge.md>), [AI Agent](<https://devfeed.tech/topics/ai-agent.md>), [Orchestration](<https://devfeed.tech/topics/orchestration.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [pdf](<https://devfeed.tech/topics/pdf.md>), [human feedback](<https://devfeed.tech/topics/human-feedback.md>), [Concurrency](<https://devfeed.tech/topics/concurrency.md>), [Streaming](<https://devfeed.tech/topics/streaming.md>), [observability](<https://devfeed.tech/topics/observability.md>), [web applications](<https://devfeed.tech/topics/web-applications.md>)

Tags: [agent](<https://devfeed.tech/tags/agent.md>), [ai-agent](<https://devfeed.tech/tags/ai-agent.md>), [coding](<https://devfeed.tech/tags/coding.md>), [concurrency](<https://devfeed.tech/tags/concurrency.md>), [orchestration](<https://devfeed.tech/tags/orchestration.md>), [pdf](<https://devfeed.tech/tags/pdf.md>), [review](<https://devfeed.tech/tags/review.md>), [streaming](<https://devfeed.tech/tags/streaming.md>), [visibility](<https://devfeed.tech/tags/visibility.md>), [web](<https://devfeed.tech/tags/web.md>)

### AI overview

This coding challenge guides readers through building an AI-powered contract review agent with Trigger.dev. The application uploads PDF contracts, extracts and analyzes clauses in parallel with LLMs, pauses for human approval, and streams a final summary to the frontend.

### Source excerpt

This challenge is to build your own AI powered agent to review documents.

## Doppel's AI defense system stops attacks before they spread

DevFeed: [Doppel's AI defense system stops attacks before they spread](<https://devfeed.tech/articles/doppel-s-ai-defense-system-stops-attacks-before-they-spread-6387.md>)

Original publisher: [Read original article](<https://openai.com/index/doppel>)

Published: 2025-10-28T10:00:00Z

Content type: article

Language: en

Sources: [OpenAI News](<https://devfeed.tech/sources/openai-news.md>)

Topics: [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [threat detection](<https://devfeed.tech/topics/threat-detection.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [Fine-tuning](<https://devfeed.tech/topics/fine-tuning.md>), [OpenAI](<https://devfeed.tech/topics/openai.md>), [Social engineering](<https://devfeed.tech/topics/social-engineering.md>), [human feedback](<https://devfeed.tech/topics/human-feedback.md>), [spoofing](<https://devfeed.tech/topics/spoofing.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [fine-tuning](<https://devfeed.tech/tags/fine-tuning.md>), [gpt](<https://devfeed.tech/tags/gpt.md>), [human-feedback](<https://devfeed.tech/tags/human-feedback.md>), [internet](<https://devfeed.tech/tags/internet.md>), [phishing](<https://devfeed.tech/tags/phishing.md>), [social-engineering](<https://devfeed.tech/tags/social-engineering.md>), [threat-detection](<https://devfeed.tech/tags/threat-detection.md>)

### AI overview

Doppel uses OpenAI GPT-5 and o4-mini models, together with reinforcement fine-tuning and human-graded feedback, to detect, classify, and remove deepfake, phishing, spoofed-domain, and impersonation threats. The system reduces analyst workloads by 80%, triples threat-handling capacity, and cuts response times from hours to minutes.

### Source excerpt

Doppel uses GPT-5 and reinforcement fine-tuning to stop deepfake and impersonation attacks, cutting analyst workloads by 80% and reducing response times from hours to minutes.

## Argilla 2.4: Easily Build Fine-Tuning and Evaluation Datasets on the Hub -- No Code Required

DevFeed: [Argilla 2.4: Easily Build Fine-Tuning and Evaluation Datasets on the Hub -- No Code Required](<https://devfeed.tech/articles/argilla-2-4-easily-build-fine-tuning-and-evaluation-datasets-on-the-hub-no-code-required-7103.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/argilla-ui-hub>)

Author: Natalia Elvira; ben burtenshaw; Daniel Vila

Published: 2024-11-04T00:00:00Z

Content type: article

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [argilla](<https://devfeed.tech/topics/argilla.md>), [datasets](<https://devfeed.tech/topics/datasets.md>), [hugging face](<https://devfeed.tech/topics/hugging-face.md>), [Fine-tuning](<https://devfeed.tech/topics/fine-tuning.md>), [human feedback](<https://devfeed.tech/topics/human-feedback.md>), [LLM evaluation / benchmarking](<https://devfeed.tech/topics/llm-evaluation-benchmarking.md>), [spaces](<https://devfeed.tech/topics/spaces.md>), [CSV](<https://devfeed.tech/topics/csv.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [argilla](<https://devfeed.tech/tags/argilla.md>), [artificial-intelligence](<https://devfeed.tech/tags/artificial-intelligence.md>), [data](<https://devfeed.tech/tags/data.md>), [dataset](<https://devfeed.tech/tags/dataset.md>), [datasets](<https://devfeed.tech/tags/datasets.md>), [deployment](<https://devfeed.tech/tags/deployment.md>), [evaluation](<https://devfeed.tech/tags/evaluation.md>), [fine-tuning](<https://devfeed.tech/tags/fine-tuning.md>), [github](<https://devfeed.tech/tags/github.md>), [hub](<https://devfeed.tech/tags/hub.md>), [hugging-face](<https://devfeed.tech/tags/hugging-face.md>), [human-feedback](<https://devfeed.tech/tags/human-feedback.md>), [oauth](<https://devfeed.tech/tags/oauth.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [space](<https://devfeed.tech/tags/space.md>), [spaces](<https://devfeed.tech/tags/spaces.md>)

### AI overview

Argilla 2.4 introduces a no-code workflow for importing public Hugging Face Hub datasets into Argilla Spaces. Users can collect human feedback, annotate or curate datasets, and prepare them for fine-tuning or model evaluation, with Hugging Face OAuth supporting community contributions or restricted collaboration.

### Source excerpt

We're on a journey to advance and democratize artificial intelligence through open source and open science.

## Building AI agents just got faster with Wordware (and Neon)

DevFeed: [Building AI agents just got faster with Wordware (and Neon)](<https://devfeed.tech/articles/building-ai-agents-just-got-faster-with-wordware-and-neon-5098.md>)

Original publisher: [Read original article](<https://neon.com/blog/building-ai-agents-just-got-faster-with-wordware-and-neon>)

Author: Carlota Soto

Published: 2024-08-19T15:23:42Z

Content type: article

Language: en

Sources: [Blog -- Neon Docs](<https://devfeed.tech/sources/blog-neon-docs.md>)

Topics: [AI Development](<https://devfeed.tech/topics/ai-development.md>), [Prompt Engineering](<https://devfeed.tech/topics/prompt-engineering.md>), [human feedback](<https://devfeed.tech/topics/human-feedback.md>), [ide](<https://devfeed.tech/topics/ide.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [ai-agents](<https://devfeed.tech/tags/ai-agents.md>), [ai-development](<https://devfeed.tech/tags/ai-development.md>), [case-studies](<https://devfeed.tech/tags/case-studies.md>), [community](<https://devfeed.tech/tags/community.md>), [human-feedback](<https://devfeed.tech/tags/human-feedback.md>), [ide](<https://devfeed.tech/tags/ide.md>), [llms](<https://devfeed.tech/tags/llms.md>), [prompt-engineering](<https://devfeed.tech/tags/prompt-engineering.md>)

### AI overview

Wordware is presented as an IDE for prompt engineering that helps teams build AI applications and agents through natural-language programming, templates, and rapid iteration. The article explains how human feedback and prompt-first workflows can shorten development cycles while Neon preview branches help identify database migration problems before production.

### Source excerpt

"Wordware is all about building and iterating quickly, so there's alignment with Neon. With Neon's preview branches, we can catch issues early (like a migration breaking on a copy of the main database) and fix them before they hit production. By spotting and fixing problems ear...

## Powering ML-Based Systems With Reliable Data: The Data Annotation Journey

DevFeed: [Powering ML-Based Systems With Reliable Data: The Data Annotation Journey](<https://devfeed.tech/articles/powering-ml-based-systems-with-reliable-data-the-data-annotation-journey-28027.md>)

Original publisher: [Read original article](<https://tech.trivago.com/post/2022-09-01-powering-ml-based-systems-with-reliable-data-annotation/>)

Author: Omayma Said Senior Data Scientist @ trivago Linkedin profile

Published: 2022-09-01T00:00:00Z

Content type: tutorial

Language: en

Sources: [Trivago](<https://devfeed.tech/sources/trivago.md>)

Topics: [data](<https://devfeed.tech/topics/data.md>), [datasets](<https://devfeed.tech/topics/datasets.md>), [human feedback](<https://devfeed.tech/topics/human-feedback.md>), [Machine Learning & Artificial Intelligence](<https://devfeed.tech/topics/machine-learning-artificial-intelligence.md>)

Tags: [data](<https://devfeed.tech/tags/data.md>), [data-science](<https://devfeed.tech/tags/data-science.md>), [datasets](<https://devfeed.tech/tags/datasets.md>), [development](<https://devfeed.tech/tags/development.md>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>), [quality](<https://devfeed.tech/tags/quality.md>)

### AI overview

This article explains why reliable data collection, curation, cleaning, and annotation are essential to building effective machine-learning systems. It introduces the data-centric AI movement and shares practical lessons from several data-annotation projects, including work at trivago.

### Source excerpt

In the last few years, organisations have been increasing their investments in building Machine Learning (ML) based systems. In practice, such systems often took longer than expected to be built...