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What Is the Foundational Step to Enhance DEI in AI Recruitment?
Learn the most critical step for a manager aiming to enhance DEI in hiring with AI. Discover why training the algorithm with diverse data sets is the fundamental action to counteract potential AI biases from the very beginning.
Question
Jasmine knows that AI can help her company enhance DEI in its hiring practices. What should she do to counteract potential AI biases when training AI for hiring?
A. Train using diverse data sets.
B. Train on past hiring practices.
C. Train with traditional hiring tools.
Answer
A. Train using diverse data sets.
Explanation
Jasmine should use diverse data sets when training AI and regularly update algorithms based on diverse feedback to foster hiring diversity.
Artificial intelligence models learn to make predictions based on the data they are trained on. If the training data reflects historical biases, the AI will learn and perpetuate those same biases. For example, if past hiring favored candidates from specific backgrounds, training an AI on that data will teach it to favor similar candidates in the future, regardless of their qualifications.
To build a fairer AI hiring tool, it is essential to train it on a data set that is intentionally diverse and representative of the talent pool the company wishes to attract. This means ensuring the data includes a balanced representation across different genders, ethnicities, educational backgrounds, and other demographic characteristics. By starting with high-quality, diverse data, a manager is taking the most critical step to mitigate bias from the outset.
Option B is incorrect because training on past hiring practices is a direct cause of algorithmic bias, not a solution to it. This method simply automates any existing, often unconscious, biases present in a company’s historical hiring decisions.
Option C is incorrect because “traditional hiring tools” are often the source of the biases that AI seeks to overcome. Training an AI on the inputs and outputs of traditional, human-led hiring processes would likely incorporate those human biases into the model. The goal is to create a more objective system, not to replicate the potential flaws of the old one.
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