datacards/context.py

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)