Develop Bias Identification Mechanisms

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Assessment

Difficulty
5/5
Estimated time
Over a week
Newbie friendliness
25/100
Issue type
Feature
Clarity
Needs clarification
Activity status
Stale
Tech stack
python

Research direction

No files, tests, or entry points are named. Start by reviewing the toolkit's existing data-loading, statistical-calculation, outlier-detection, cleaning, and visualization functionality, then define how sampling and algorithmic bias should be measured and reported; done means both bias types are identified, reported, and accompanied by mitigation guidance.

Written by the indexing model from the issue text.

Description

Data Quality Development

Implement methods to identify and report biases in data, focusing on sampling biases and algorithmic biases. Provide guidance on mitigating identified biases.

Dominant language
Jupyter Notebook
Stars
7
Forks
4
PR merge metrics
No merged PRs in 30d

Contributor guide

Open the contributing guide

First steps

  1. Read the whole issue, then the project's contributing guide.
  2. Comment on the issue to say you are picking it up — it saves two people doing the same work.
  3. Fork the repository and make your change on a branch.
  4. Open a pull request that references the issue number.

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