society-ethics/BlogPostOpenness

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license: cc-by-4.0
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## Mission: Open and Good ML
In our mission to democratize good machine learning (ML), we examine how supporting ML community work also empowers examining and preventing possible harms. Open development and science decentralizes power so that many people can collectively work on AI that reflects their needs and values. While [openness enables broader perspectives to contribute to research and AI overall, it faces the tension of less risk control](https://arxiv.org/abs/2302.04844).

Moderating ML artifacts presents unique challenges due to the dynamic and rapidly evolving nature of these systems. In fact, as ML models become more advanced and capable of producing increasingly diverse content, the potential for harmful or unintended outputs grows, necessitating the development of robust moderation and evaluation strategies. Moreover, the complexity of ML models and the vast amounts of data they process exacerbate the challenge of identifying and addressing potential biases and ethical concerns.

As hosts, we recognize the responsibility that comes with potentially amplifying harm to our users and the world more broadly. Often these harms disparately impact minority communities in a context-dependent manner. We have taken the approach of analyzing the tensions in play for each context, open to discussion across the company and Hugging Face community. While many models can amplify harm, especially discriminatory content, we are taking a series of steps to identify highest risk models and what action to take. Importantly, active perspectives from many backgrounds is key to understanding, measuring, and mitigating potential harms that affect different groups of people.

We are crafting tools and safeguards in addition to improving our documentation practices to ensure open source science empowers individuals and continues to minimize potential harms.

## Ethical Categories
The first major aspect of our work to foster good open ML consists in promoting the tools and positive examples of ML development that prioritize values and consideration for its stakeholders. This helps users take concrete steps to address outstanding issues, and present plausible alternatives to de facto damaging practices in ML development.

To help our users discover and engage with ethics-related ML work, we have compiled a set of tags. These 6 high-level categories are based on our analysis of Spaces that community members had contributed. They are designed to give you a jargon-free way of thinking about ethical technology:

- Rigorous work pays special attention to developing with best practices in mind. In ML, this can mean examining failure cases (including conducting bias and fairness audits), protecting privacy through security measures, and ensuring that potential users (technical and non-technical) are informed about the project's limitations.
- Consentful work [supports](https://www.consentfultech.io/) the self-determination of people who use and are affected by these technologies.
- Socially Conscious work shows us how technology can support social, environmental, and scientific efforts.
- Sustainable work highlights and explores techniques for making machine learning ecologically sustainable.
- Inclusive work broadens the scope of who builds and benefits in the machine learning world.
- Inquisitive work shines a light on inequities and power structures which challenge the community to rethink its relationship to technology.

Read more at https://huggingface.co/ethics

Look for these terms as we’ll be using these tags, and updating them based on community contributions, across some new projects on the Hub!

## Safeguards
Taking an “all-or-nothing” view of open releases ignores the wide variety of contexts that determine an ML artifact’s positive or negative impacts. Having more levers of control over how ML systems are shared and re-used supports collaborative development and analysis with less risk of promoting harmful uses or misuses; allowing for more openness and participation in innovation for shared benefits.

We engage directly with contributors and have addressed pressing issues. To bring this to the next level, we are building community-based processes. This approach empowers both Hugging Face contributors, and those affected by contributions, to inform the limitations, sharing, and additional mechanisms necessary for models and data made available on our platform. The three main aspects we will pay attention to are: the origin of the artifact, how the artifact is handled by its developers, and how the artifact has been used. In that respect we:
- launched a [flagging feature](https://twitter.com/GiadaPistilli/status/1571865167092396033) for our community to determine whether ML artifacts or community content (model, dataset, space, or discussion) violate our [content guidelines](https://huggingface.co/content-guidelines),
- monitor our community discussion boards to ensure Hub users abide by the [code of conduct](https://huggingface.co/code-of-conduct),
- robustly document our most-downloaded models with model cards that detail social impacts, biases, and intended and out-of-scope use cases,
- create audience-guiding tags, such as the “Not For All Audiences” tag that can be added to the repository’s card metadata to avoid un-requested violent and sexual content,
- promote use of [Open Responsible AI Licenses (RAIL)](https://huggingface.co/blog/open_rail) for [models](https://www.licenses.ai/blog/2022/8/26/bigscience-open-rail-m-license), such as with LLMs ([BLOOM](https://huggingface.co/spaces/bigscience/license), [BigCode](https://huggingface.co/spaces/bigcode/license)),
- conduct research that [analyzes](https://arxiv.org/abs/2302.04844) which models and datasets have the highest potential for, or track record of, misuse and malicious use.

**How to use the flagging function:**
Click on the flag icon on any Model, Dataset, Space, or Discussion:
<p align="center">
<br>
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/blog/ethics_soc_3/flag2.jpg" alt="screenshot pointing to the flag icon to Report this model" />
</p>

Share why you flagged this item:
<p align="center">
<br>
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/blog/ethics_soc_3/flag1.jpg" alt="screenshot showing the text window where you describe why you flagged this item" />
</p>

In prioritizing open science, we examine potential harm on a case-by-case basis. When users flag a system, developers can directly and transparently respond to concerns. Moderators are able to disengage from discussions should behavior become hateful and/or abusive (see [code of conduct](https://huggingface.co/code-of-conduct)).

Should a specific model be flagged as high risk by our community, we consider:
- Downgrading the ML artifact’s visibility across the Hub in the trending tab and in feeds,
- Requesting that the models be made private,
- Gating access to ML artifacts (see documentation for [models](https://huggingface.co/docs/hub/models-gated) and [datasets](https://huggingface.co/docs/hub/datasets-gated)),
- Disabling access.

**How to add the “Not For All Audiences” tag:**

Edit the model/data card → add `not-for-all-audiences` in the tags section → open the PR and wait for the authors to merge it. Once merged, the following tag will be displayed on the repository:

<p align="center">
<br>
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/blog/ethics_soc_3/nfaa_tag.png" alt="screenshot showing where to add tags" />
</p>

Any repository tagged `not-for-all-audiences` will display the following popup when visited:

<p align="center">
<br>
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/blog/ethics_soc_3/nfaa2.png" alt="screenshot showing where to add tags" />
</p>

Clicking "View Content" will allow you to view the repository as normal. If you wish to always view `not-for-all-audiences`-tagged repositories without the popup, this setting can be changed in a user's [Content Preferences](https://huggingface.co/settings/content-preferences)

<p align="center">
<br>
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/blog/ethics_soc_3/nfaa1.png" alt="screenshot showing where to add tags" />
</p>

Open science requires safeguards, and one of our goals is to create an environment informed by tradeoffs with different values. Hosting and providing access to models in addition to cultivating community and discussion empowers diverse groups to assess social implications and guide what is good machine learning.

## Are you working on safeguards? Share them on Hugging Face Hub!

The most important part of Hugging Face is our community. If you’re a researcher working on making ML safer to use, especially for open science, we want to support and showcase your work!

Here are some recent demos and tools from researchers in the Hugging Face community:
- [A Watermark for LLMs](https://huggingface.co/spaces/tomg-group-umd/lm-watermarking) by John Kirchenbauer, Jonas Geiping, Yuxin Wen, Jonathan Katz, Ian Miers, Tom Goldstein ([paper](https://arxiv.org/abs/2301.10226))
- [Generate Model Cards Tool](https://huggingface.co/spaces/huggingface/Model_Cards_Writing_Tool) by the Hugging Face team
- [Photoguard](https://huggingface.co/spaces/RamAnanth1/photoguard) to safeguard images against manipulation by Ram Ananth

Thanks for reading! 🤗

~ Irene, Nima, Giada, Yacine, and Elizabeth, on behalf of the Ethics and Society regulars

If you want to cite this blog post, please use the following:
```
@misc{hf_ethics_soc_blog_3,
author = {Irene Solaiman and
Giada Pistilli and
Nima Boscarino and
Yacine Jernite and
Elizabeth Allendorf and
Margaret Mitchell and
Carlos Muñoz Ferrandis and
Nathan Lambert and
Alexandra Sasha Luccioni
},
title = {Hugging Face Ethics and Society Newsletter 3: Ethical Openness at Hugging Face},
booktitle = {Hugging Face Blog},
year = {2023},
url = {https://doi.org/10.57967/hf/0487},
doi = {10.57967/hf/0487}
}

```

原始信息汇总

数据集概述

许可证

• 该数据集遵循 cc-by-4.0 许可证。

伦理分类

• 数据集包含六个高级别的伦理分类标签，旨在帮助用户发现和参与与伦理相关的机器学习工作：
  • 严谨的工作：注重最佳实践，包括审查失败案例、保护隐私和确保用户了解项目限制。
  • 同意的工作：支持使用和受这些技术影响的人的自决。
  • 社会意识的工作：展示技术如何支持社会、环境和科学努力。
  • 可持续的工作：强调和探索使机器学习生态可持续的技术。
  • 包容的工作：扩大机器学习世界中构建者和受益者的范围。
  • 好奇的工作：揭示不平等和权力结构，挑战社区重新思考与技术的关系。

安全措施

• 数据集提供了多种安全措施，包括：
  • 标记功能：允许社区成员标记违反内容指南的机器学习制品或社区内容。
  • 监控社区讨论：确保用户遵守行为准则。
  • 模型卡片：详细记录最受欢迎模型的社会影响、偏见和使用范围。
  • 受众导向标签：如“不适合所有观众”标签，用于避免未经请求的暴力和色情内容。
  • 开放负责任AI许可证（RAIL）：推广使用RAIL许可证，如LLMs（BLOOM、BigCode）。
  • 研究分析：分析哪些模型和数据集具有最高的滥用或恶意使用潜力。

标记功能使用方法

• 点击任何模型、数据集、空间或讨论中的标记图标：
  • 分享标记原因：在文本窗口中描述标记原因。

“不适合所有观众”标签使用方法

• 编辑模型/数据卡片 → 在标签部分添加 not-for-all-audiences → 提交PR并等待作者合并。

高风险模型处理

• 如果模型被社区标记为高风险，可能采取以下措施：
  • 降低模型在热门标签和动态中的可见性。
  • 请求将模型设为私有。
  • 限制对模型的访问。
  • 禁用访问。