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Abstract algorithmic Bias: on the implicit biases of social technology by Gabrielle M Johnson gmjohnson@humnet.ucla.edu UCLA, Philosophy December 18th, 2017word count: 912Algorithmic Bias: on the

How to fill out review of algorithmic bias

How to fill out review of algorithmic bias

1. Begin by identifying the algorithm or system being reviewed.
2. Collect relevant data and information about the algorithm's purpose and impact.
3. Evaluate the algorithm for potential biases based on factors such as race, gender, or socio-economic status.
4. Document any biases or concerns discovered during the review process.
5. Provide recommendations for addressing and mitigating algorithmic bias in future iterations.

Who needs review of algorithmic bias?

1. Researchers studying the impact of algorithms on marginalized communities
2. Policy makers developing regulations around algorithmic decision-making
3. Companies implementing algorithms for automated decision-making processes

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What is review of algorithmic bias?

The review of algorithmic bias is an assessment of potential biases present in algorithms used in decision-making processes.

Who is required to file review of algorithmic bias?

Companies and organizations that use algorithms in their decision-making processes are required to file a review of algorithmic bias.

How to fill out review of algorithmic bias?

The review of algorithmic bias can be filled out by collecting data on the algorithm's decision outcomes and assessing for any potential biases based on protected characteristics such as race, gender, or age.

What is the purpose of review of algorithmic bias?

The purpose of the review of algorithmic bias is to ensure that algorithms are not discriminating against individuals based on protected characteristics and to promote fairness and transparency in decision-making processes.

What information must be reported on review of algorithmic bias?

The review of algorithmic bias must include data on the algorithm's decision outcomes, any potential biases identified, and any steps taken to mitigate those biases.