Spaces:
Runtime error
Runtime error
import gradio as gr | |
import os | |
hf_writer = gr.HuggingFaceDatasetSaver( | |
os.getenv('HUGGING_FACE_HUB_TOKEN'), | |
organization="society-ethics", | |
dataset_name="featured-spaces-submissions", | |
private=True | |
) | |
principles = [ | |
{ | |
"title": "π€ Consentful", | |
"content": """ | |
[What is consentful tech?](https://www.consentfultech.io) | |
Consentful technology supports the self-determination of people who use and are affected by these technologies. | |
For Spaces, some examples of this can include: | |
- Demonstrating a commitment to acquiring data from willing, informed, and appropriately compensated sources. | |
- Designing systems that respect end-user autonomy, e.g. with privacy-preserving techniques. | |
- Avoiding extractive, chauvinist, ["dark"](https://www.deceptive.design), and otherwise "unethical" patterns of engagement. | |
""" | |
}, | |
{ | |
"title": "π Sustainable", | |
"content": """ | |
These are Spaces that highlight and explore techniques for making machine learning ecologically sustainable. | |
Examples could include: | |
- Tracking emissions from training and running inferences on large language models. | |
- Quantization and distillation methods to reduce carbon footprints without sacrificing model quality. | |
""" | |
}, | |
{ | |
"title": "ποΈβπ¨οΈ Socially Conscious", | |
"content": """ | |
"Socially Conscious" Spaces show us how machine learning can be applied as a force for *good*! | |
This is quite broad, but some examples could be: | |
- Using machine learning as part of an effort to tackle climate change. | |
- Building tools to assist with medical research and practice. | |
- Developing models for text-to-speech, image captioning, and other tasks aimed at increasing accessibility. | |
- Creating systems for the digital humanities, such as for Indigenous language revitalization. | |
""" | |
}, | |
{ | |
"title": "π§βπ€βπ§ Inclusive", | |
"content": """ | |
These are projects which broaden the scope of who *builds* and *benefits* in the machine learning world. | |
This could mean things like: | |
- Curating diverse datasets that increase the representation of underserved groups. | |
- Training language models on languages that aren't yet available on the Hugging Face Hub. | |
- Creating no-code frameworks that allow non-technical folk to engage with AI. | |
""" | |
}, | |
{ | |
"title": "βοΈ Rigorous", | |
"content": """ | |
Among the many concerns that go into creating new models is a seemingly simple question: "Does it work?" | |
Rigorous projects pay special attention to examining failure cases, protecting privacy through security | |
measures, and ensuring that potential users (technical and non-technical) are informed of the project's | |
limitations. | |
For example: | |
- Projects built with models that are well-documented with [Model Cards](https://huggingface.co/docs/hub/model-cards). | |
- Models that are evaluated against cutting-edge benchmarks, with results reported against disaggregated sets. | |
- Demonstrations of models failing across ["gender, skin type, ethnicity, age or other attributes"](http://gendershades.org/overview.html). | |
- Techniques for mitigating issues like over-fitting and training data memorization. | |
""" | |
}, | |
{ | |
"title": "π€ Inquisitive", | |
"content": """ | |
Some projects take a radical new approach to concepts which may have become commonplace. These projects, often | |
rooted in critical theory, shine a light on inequities and power structures which challenge the community to | |
rethink its relationship to technology. | |
For example: | |
- [Reframing AI and machine learning from Indigenous perspectives](https://jods.mitpress.mit.edu/pub/lewis-arista-pechawis-kite/release/1). | |
- [Highlighting LGBTQIA2S+ marginalization in AI](https://edri.org/our-work/computers-are-binary-people-are-not-how-ai-systems-undermine-lgbtq-identity/). | |
- [Critiquing the harms perpetuated by AI systems](https://www.ajl.org). | |
""" | |
}, | |
] | |
def toggle_description(title, content): | |
with gr.Accordion(label=title, open=False): | |
gr.Markdown(content, elem_id="margin-top") | |
def submit_entry(URL, tags, suggestions, comments): | |
hf_writer.flag( | |
flag_data=[URL, tags, suggestions, comments] | |
) | |
return [ | |
gr.Markdown.update( | |
visible=True, | |
value="Thank you for your submission! π€" | |
), | |
gr.Button.update( | |
visible=False | |
) | |
] | |
with gr.Blocks(css="#margin-top {margin-top: 15px}") as demo: | |
gr.Markdown("## Call for submissions! π’") | |
gr.Markdown(""" | |
Hugging Face is collecting examples of [Spaces](https://huggingface.co/spaces) that are ethically mindful to highlight and encourage these kinds of projects βΒ and we would love your input! | |
If you have built a Space that you think should be featured, or if you would like to nominate someone else's, paste the URL in the form below π€ | |
The current set of tags reflect our initial categorization from going through Hugging Face Spaces: π€ consentful, π sustainable, ποΈβπ¨οΈ socially conscious, π§βπ€βπ§ inclusive, βοΈ rigorous, and π€ inquisitive. | |
Let us know other relevant categories and examples that you find! | |
Want to learn more? Join us over at **#ethics-and-society** on the [Hugging Face Discord](https://hf.co/join/discord)! | |
""") | |
with gr.Row(): | |
with gr.Column(): | |
gr.Markdown("π‘ Click on the terms below to view their description and some examples.") | |
with gr.Column(): | |
[toggle_description(x["title"], x["content"]) for x in principles] | |
with gr.Column(): | |
URL = gr.Text(label="URL") | |
tags = gr.Checkboxgroup( | |
label="Tags - Pick as many as you like!", | |
choices=[ | |
"Consentful", | |
"Sustainable", | |
"Socially Conscious", | |
"Inclusive", | |
"Rigorous", | |
"Inquisitive", | |
] | |
) | |
suggestions = gr.Text(label="[Optional] Do you have suggestions for other tags?") | |
comments = gr.TextArea(label="[Optional] Any extra comments?") | |
submit = gr.Button(value="Submit") | |
thank_you = gr.Markdown(visible=False) | |
submit.click( | |
fn=submit_entry, | |
inputs=[URL, tags, suggestions, comments], | |
outputs=[thank_you, submit] | |
) | |
hf_writer.setup( | |
components=[URL, tags, suggestions, comments], | |
flagging_dir="flagged" | |
) | |
demo.launch() | |