Integrating AI into diversity initiatives poses risks as well as opportunities

Integrating AI into diversity initiatives poses risks as well as opportunities

In many cases, the models that AI tools rely on are built based on historical data, and if these data include biases they can propagate into future decision making. For example, models that are used to automate the process of screening resumes have been found to be biased against women applying for jobs, while models used to assist judges in criminal sentence reviews have been found to be biased against black defendants and have been withdrawn. Both illustrate how the design of both the data to be used and the application of the resulting solution need careful human attention.
If, for example, an organisation is looking to hire a new worker, the data may recommend a white male – not necessarily because other candidates don’t have the right skills, but because the historic data would indicate that this group performed well. The reality, naturally, is that data on other workers is less readily available.

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Automation and machine learning have one primary limitation: context. While automated analysis is extremely effective, it is hollow without knowing how and where to apply the learning most efficiently, as well as understanding what may have caused the trends found.
The context here is that due to an historic lack of diversity and inclusion, other demographics are not as well represented by the available data. Even Amazon mothballed its own AI recruitment algorithm, as the legacy CVs it used were found to be heavily originated from men, resulting in an inadvertent bias against women.

Integrating AI into diversity initiatives risks as well as opportunities.

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