Link to our App!

http://countergambit.herokuapp.com/

Inspiration

In 2020, a popular YouTube channel was blocked for hate speech. Although terms like “black”, “white”, “attack”, “capture” were used, it was still mistakenly flagged by AI due to not understanding the context — chess. Inspired by the paper “Are Chess Discussions Racist? An Adversarial Hate Speech Data Set” (Sarker & Khudabukhsh), we improved upon an existing hate speech detection model utilizing BERT to improve its accuracy in regards to chess comments.

What it does

We now have a model that identifies chess comments as non-hate-speech that can be applied to (or reapplied to) Youtube comments, Twitter feeds, and more to protect the freedoms of law-abiding citizens while keeping them safe from harmful speech.

We're improving the ethical implications of AI as leaders in the field who create and have an understanding of the behind the scenes. This extends to other debiasing work in the field, especially for minorities who can realize their dreams when they're placed in models that represent the world for what it should be. Each small step we take towards helping our community has a big impact on the future we have. We took a small step today for the chess community, but this has a meaningful impact on how AI treats the world.

How we built it

We improved upon a prebuilt BERT model from HuggingFace on Hate Speech detection. We found multiple hate-speech databases and combined them with correctly labeled (inoffensive) chess comments and retrained the model to see its improvements.

Challenges we ran into

  • Getting the model run on the GPU instead of CPU
  • Tuning different hyperparameters
  • Storage & RAM on our devices kept running out
  • Figuring out how to translate model output into API calls

Accomplishments that we're proud of

  • As a team meeting each other for the first time, we worked well together and had trust in our work
  • We are proud of getting the model to work as only 1 person on the team had taken an ML class before

What we learned

  • NLP basics (train, validation, test sets are different ahh!)
  • Data Visualization tools (Tensorboard, Chart.js)
  • Training a model takes an ~e x t r e m e l y~ long time to run...

What's next for Counter Gambit

  • Training on more examples for better accuracy
  • Tuning our hyperparameters
  • Switch to Google cloud's AutoML instead of a compute engine for training and deployment of the model

Updates

We now have the API running fully on the cloud! Our data was collected using Google Cloud's YouTube API (to get data for chess comments), a model was trained using a Compute Engine, and now we have an HTTP/SSL server hosted on the Compute Engine, and the backend supports API calls to the machine learning model for a fully independent frontend design.

Resources and References

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Updates

posted an update

We now have the API running fully on the cloud! Our data was collected using Google Cloud's YouTube API (to get data for chess comments), a model was trained using a Compute Engine, and now we have an HTTP/SSL server hosted on the Compute Engine, and the backend supports API calls to the machine learning model for a fully independent frontend design.

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