Algorithmic Bias Detection Tool
Algorithmic Bias Detection Tool
technology application

Algorithmic Bias Detection Tool

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SDGs
updatedMar 31, 2021
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By using statistical methods, this tool spots when groups of people are treated unfairly by an algorithm in institutional and business programs, helping to identify, mitigate and avoid systematic prejudice.
By using statistical methods, this tool spots when groups of people are treated unfairly by an algorithm in institutional and business programs, helping to identify, mitigate and avoid systematic prejudice.

To help prevent algorithmic and systematic injustice, this tool can detect underlying unfairness in institutional and business programs. By using statistical methods and machine learning, the tool is integrated into the development process and flags biases within algorithmic structures, thus helping data scientists to test and improve their code.

By examining several public models, the amount of influence that sensitive variables — race, gender, class, and religion — have on data can be measured along with an estimated correlation between said variables. This solution is expected to assist in reducing unfair outcomes in risk-assessment tools and recommend specific changes to how the mathematical models interpret data.

Gender Equality

Challenges

  • Even a bias detection tool may have bias encoded within their systems. Therefore, it is crucial to have a diversified team of professionals working together to avoid possible bias towards vulnerable and underprivileged groups.

Opportunities

  • Helps design appropriate pre and in-service teacher training courses to tackle traditional gender roles and gender-specific disadvantages inflicted on women-identified, girls, non-binary, and transgender individuals regarding their educational opportunities.

  • Prevents unethical credit scoring (based on many preconceived notions ranging from sexual orientation to political beliefs).

  • Gives people relief from advertising overload, which could be beneficial, especially for women of color since ad targeting algorithms are shown to align with race and gender stereotypes.

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