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What should a dataset user know before training a model?

A dataset’s collection, composition and intended use affect what conclusions a model evaluation can support.

AI & computationThinker · Selected readingBeginner friendly
Background and goal

No prior study required. Read the assigned passage; the route is an introduction, not a survey of the whole subject.

Write a datasheet for a fictional dataset, including collection conditions, exclusions, consent questions and inappropriate uses.

Your pathway

4 steps · 3 hours
  1. 01

    Begin in fifteen minutes

    0.25h · week 1
    Reading notes and task
    First fifteen minutesWrite your initial answer to “What should a dataset user know before training a model?” in three sentences. Keep it to compare with your final answer.
    Your outputThree-sentence starting answer
  2. 02

    Read the argument

    1h · week 1
    Datasheets for Datasets

    Timnit Gebru · Jamie Morgenstern · Briana Vecchione · Jennifer Wortman Vaughan · Hanna Wallach · Hal Daumé III · Kate Crawford · paper

    Opening overview and the first section that addresses the pathway question. Stop after 30–45 minutes; this is selected reading.

    Free full text

    Open the reading
    Reading notes and task

    Datasheets for Datasets. Research on dataset documentation, evaluation and harms in machine learning. Begin with how data was collected and which uses its documentation supports.

    Try thisRead Opening overview and the first section that addresses the pathway question. Stop after 30–45 minutes; this is selected reading.. Mark one claim, its reason and a passage you do not yet understand.
    Your outputAnnotated argument
    Check your understanding

    Answer this question in your own words: What should a dataset user know before training a model?

    Hint

    Name the claim, then explain why a reader might accept it.

    Compare with an example answer

    A dataset’s collection, composition and intended use affect what conclusions a model evaluation can support.

    What to look for

    • States a claim that addresses the question
    • Connects the claim to a passage or observation
    • Separates the author’s argument from personal agreement

    Common mistake: Summarising a biography instead of explaining the argument.

    Start this pathway to keep your answers in My learning.

  3. 03

    Try the idea

    1h · week 1
    Reading notes and task
    Try thisWrite a datasheet for a fictional dataset, including collection conditions, exclusions, consent questions and inappropriate uses.
    Your outputWorked example
    Check your understanding

    What would make your activity a convincing example?

    Hint

    Write a datasheet for a fictional dataset, including collection conditions, exclusions, consent questions and inappropriate uses.

    Compare with an example answer

    A useful response carries out the task: Write a datasheet for a fictional dataset, including collection conditions, exclusions, consent questions and inappropriate uses. It records the observation or passage used and explains how it bears on the question.

    What to look for

    • Completes the specific task
    • Shows evidence or reasoning rather than only a conclusion
    • Identifies an alternative explanation or reading

    Common mistake: Presenting an opinion without showing the example it rests on.

    Start this pathway to keep your answers in My learning.

  4. 04

    Explain what changed

    0.75h · week 1
    Reading notes and task
    Try thisRevise your initial answer. Include one passage, one reason and one limit: Documentation improves scrutiny but does not by itself eliminate bias or authorise every reuse.
    Your outputRevised answer with a reading reference
    Check your understanding

    What is one conclusion this reading does not establish?

    Hint

    Test the boundary: Documentation improves scrutiny but does not by itself eliminate bias or authorise every reuse.

    Compare with an example answer

    Documentation improves scrutiny but does not by itself eliminate bias or authorise every reuse.

    What to look for

    • States a specific limit
    • Explains why the reading does not warrant the stronger conclusion
    • Distinguishes a limitation from dismissing the work

    Common mistake: Treating a useful interpretation as an unrestricted fact.

    Start this pathway to keep your answers in My learning.

All 1 resources

Background, different viewpoints, and further reading.

  • Datasheets for Datasets

    Timnit Gebru · Jamie Morgenstern · Briana Vecchione · Jennifer Wortman Vaughan · Hanna Wallach · Hal Daumé III · Kate Crawfordpapercore

    Research on dataset documentation, evaluation and harms in machine learning. Begin with how data was collected and which uses its documentation supports.

    Opening overview and the first section that addresses the pathway question. Stop after 30–45 minutes; this is selected reading.

Continue learning

  • Study Timnit Gebru

    Introduces the question and vocabulary used in the longer selected reading.

    AI & computationSome background