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import streamlit as st |
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title = "Key Takeaways" |
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description = "Review of the information from previous pages." |
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date = "2022-01-26" |
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thumbnail = "images/raised_hand.png" |
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__KEY_TAKEAWAYS = """ |
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# Key Takeaways and Review |
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Here are some of the main ideas we have conveyed in this exploration: |
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- Defining hate speech is hard and changes depending on your context and goals. |
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- Capturing a snapshot of what you've defined to be hate speech in a dataset is hard. |
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- Models learn lots of different things based on the data it sees, and that can include things you didn't intend for them to learn. |
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Next, please answer the following questions about the information presented in this demo: |
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""" |
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def run_article(): |
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st.markdown(__KEY_TAKEAWAYS) |
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st.text_area( |
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"Did you click on any of the links provided in the **Hate Speech in ACM** page? If so, which one did you find most surprising?" |
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) |
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st.text_area( |
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"Of the datasets presented in the **Dataset Exploration** page, which one did you think best represented content that should be moderated? Which worst?" |
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) |
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st.text_area( |
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"Of the models presented in the **Model Exploration** page, which one did you think performed best? Which worst?" |
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) |
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st.text_area( |
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"Any additional comments about the materials?" |
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) |
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st.text_area( |
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"How would you describe your role? E.g. model developer, dataset developer, domain expert, policy maker, platform manager, community advocate, platform user, student" |
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) |
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st.text_area( |
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"Why are you interested in content moderation?" |
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) |
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st.text_area( |
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"Which modules did you use the most?" |
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) |
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st.text_area( |
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"Which module did you find the most informative?" |
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) |
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st.text_area( |
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"Which application were you most interested in learning more about?" |
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) |
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st.text_area( |
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"What surprised you most about the datasets?" |
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) |
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st.text_area( |
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"Which models are you most concerned about as a user?" |
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) |
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st.text_area( |
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"Do you have any comments or suggestions?" |
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) |
