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| import streamlit as st | |
| from .streamlit_utils import ( | |
| make_multiselect, | |
| make_selectbox, | |
| make_text_area, | |
| make_text_input, | |
| make_radio, | |
| ) | |
| N_FIELDS_PREVIOUS = 3 | |
| N_FIELDS_UNDERSERVED_COMMUNITIES = 2 | |
| N_FIELDS_BIASES= 3 | |
| N_FIELDS = N_FIELDS_PREVIOUS + N_FIELDS_UNDERSERVED_COMMUNITIES + N_FIELDS_BIASES | |
| def context_page(): | |
| st.session_state.card_dict["context"] = st.session_state.card_dict.get( | |
| "context", {} | |
| ) | |
| with st.expander("Previous Work on the Social Impact of the Dataset", expanded=False): | |
| key_pref = ["context", "previous"] | |
| st.session_state.card_dict["context"]["previous"] = st.session_state.card_dict[ | |
| "context" | |
| ].get("previous", {}) | |
| make_radio( | |
| label="Are you aware of cases where models trained on the task featured in this dataset ore related tasks have been used in automated systems?", | |
| options=["no", "yes - related tasks", "yes - other datasets featuring the same task", "yes - models trained on this dataset"], | |
| key_list=key_pref + ["is-deployed"], | |
| help="", | |
| ) | |
| if "yes" in st.session_state.card_dict["context"]["previous"]["is-deployed"]: | |
| make_text_area( | |
| label="Did any of these previous uses result in observations about the social impact of the systems? " + \ | |
| "In particular, has there been work outlining the risks and limitations of the system? Provide links and descriptions here.", | |
| key_list=key_pref + ["described-risks"], | |
| help="", | |
| ) | |
| if st.session_state.card_dict["context"]["previous"]["is-deployed"] == "yes - models trained on this dataset": | |
| make_text_area( | |
| label="Have any changes been made to the dataset as a result of these observations?", | |
| key_list=key_pref + ["changes-from-observation"], | |
| help="", | |
| ) | |
| else: | |
| st.session_state.card_dict["context"]["previous"]["changes-from-observation"] = "N/A" | |
| else: | |
| st.session_state.card_dict["context"]["previous"]["described-risks"] = "N/A" | |
| st.session_state.card_dict["context"]["previous"]["changes-from-observation"] = "N/A" | |
| with st.expander("Impact on Under-Served Communities", expanded=False): | |
| key_pref = ["context", "underserved"] | |
| st.session_state.card_dict["context"]["underserved"] = st.session_state.card_dict[ | |
| "context" | |
| ].get("underserved", {}) | |
| make_radio( | |
| label="Does this dataset address the needs of communities that are traditionally underserved in language technology, and particularly language generation technology?" + \ | |
| "Communities may be underserved for exemple because their language, language variety, or social or geographical context is underepresented in NLP and NLG resources (datasets and models).", | |
| options=["no", "yes"], | |
| key_list=key_pref+["helps-underserved"], | |
| ) | |
| if st.session_state.card_dict["context"]["underserved"]["helps-underserved"] == "yes": | |
| make_text_area( | |
| label="Describe how this dataset addresses the needs of underserved communities.", | |
| key_list=key_pref+["underserved-description"], | |
| ) | |
| else: | |
| st.session_state.card_dict["context"]["underserved"]["underserved-description"] = "N/A" | |
| with st.expander("Discussion of Biases", expanded=False): | |
| key_pref = ["context", "biases"] | |
| st.session_state.card_dict["context"]["biases"] = st.session_state.card_dict[ | |
| "context" | |
| ].get("biases", {}) | |
| make_radio( | |
| label="Are there documented social biases in the dataset? " + \ | |
| "Biases in this context are variations in the ways members of different social categories are represented that can have harmful downstream consequences for members of the more disadvantaged group.", | |
| options=["yes", "unsure", "no"], | |
| key_list=key_pref + ["has-biases"], | |
| help="For a more extensive definition of social biases, see [Language (Technology) is Power: A Critical Survey of “Bias” in NLP ](https://aclanthology.org/2020.acl-main.485.pdf)", | |
| ) | |
| if st.session_state.card_dict["context"]["biases"]["has-biases"] == "yes": | |
| make_text_area( | |
| label="Provide links to and summaries of works analyzing these biases.", | |
| key_list=key_pref + ["bias-analyses"], | |
| help="The analyses can take the form of academic papers or news articles, or even blog posts.", | |
| ) | |
| else: | |
| st.session_state.card_dict["context"]["biases"]["bias-analyses"] = "N/A" | |
| make_text_area( | |
| label="Does the distribution of language producers in the dataset accurately represent the full distribution of speakers of the language world-wide? If not, how does it differ?", | |
| key_list=key_pref + ["speaker-distibution"], | |
| help="For example, are most speakers in the dataset of a certain gender or located in a certain county?", | |
| ) | |
| def context_summary(): | |
| total_filled = sum( | |
| [len(dct) for dct in st.session_state.card_dict.get("context", {}).values()] | |
| ) | |
| with st.expander( | |
| f"Broader Social Context Completion - {total_filled} of {N_FIELDS}", expanded=False | |
| ): | |
| completion_markdown = "" | |
| completion_markdown += ( | |
| f"- **Overall completion:**\n - {total_filled} of {N_FIELDS} fields\n" | |
| ) | |
| completion_markdown += f"- **Sub-section - Previous Work on the Social Impact of the Dataset:**\n - {len(st.session_state.card_dict.get('context', {}).get('previous', {}))} of {N_FIELDS_PREVIOUS} fields\n" | |
| completion_markdown += f"- **Sub-section - Impact on Under-Served Communities:**\n - {len(st.session_state.card_dict.get('context', {}).get('underserved', {}))} of {N_FIELDS_UNDERSERVED_COMMUNITIES} fields\n" | |
| completion_markdown += f"- **Sub-section - Discussion of Biases:**\n - {len(st.session_state.card_dict.get('context', {}).get('biases', {}))} of {N_FIELDS_BIASES} fields\n" | |
| st.markdown(completion_markdown) | |