Spaces:
Runtime error
Runtime error
| import gradio as gr | |
| import os | |
| hf_writer = gr.HuggingFaceDatasetSaver( | |
| os.getenv('HF_API_TOKEN'), | |
| organization="society-ethics", | |
| dataset_name="featured-spaces-submissions", | |
| private=True | |
| ) | |
| principles = [ | |
| { | |
| "title": "Consentful", | |
| "content": """ | |
| Consentful technology is built with [consent at its core](https://www.consentfultech.io). | |
| 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", and otherwise "unethical" patterns of engagement. | |
| """ | |
| }, | |
| { | |
| "title": "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": "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": "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": "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": "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! 🤗" | |
| ), | |
| gr.Button.update( | |
| visible=False | |
| ) | |
| ] | |
| with gr.Blocks(css="#margin-top {margin-top: 15px}") as demo: | |
| gr.Markdown("## Call for submissions! 📢") | |
| gr.Markdown(""" | |
| Hugging Face is collecting examples of Spaces that are ethically mindful. What does that mean? There are a lot of ways to define this, so we've suggested a couple categories to get started 🤗 | |
| """) | |
| with gr.Row(): | |
| with gr.Column(): | |
| gr.Markdown("💡 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", | |
| "Critical", | |
| ] | |
| ) | |
| 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() | |