Final answer:
Modeling personas helps in identifying biases in AI models, by comparing how different groups are treated by the system. Approaches such as professionalizing HR functions, and anonymizing personal information can also reduce discrimination in human decision-making. Nonetheless, while machine learning algorithms hold the potential to reduce biases, they must be carefully designed and tested to prevent perpetuating existing biases. Option a.
Step-by-step explanation:
Modeling personas can help identify risks to fairness and non-discrimination in several ways option a. By comparing personas to an AI model's results, we can clearly see if the model is treating individuals equitably across various groups. This process can uncover biases in the model, allowing for necessary adjustments. For example, risk assessments used in the criminal justice system, if not designed properly, could perpetuate racial disparities. Likewise, recruitment can benefit from professionalizing human resources functions, as demonstrated by research showing lesser discrimination from large employers who invest in such strategies. Another approach to reduce discrimination is changing the information available in the decision-making process to remove markers of protected categories. Blind auditions in symphony orchestras are a classic example where removing visual cues about gender led to more equitable hiring practices. Removing names or other indicators of race, gender, or ethnicity can also be effective in models such as those used by Uber and Lyft or in anonymizing resumes. However, this approach has limitations and can still allow for discrimination post-interview or fail to account for potentially positive discrimination that might be necessary in certain contexts.
The use of machine learning algorithms has the potential to reduce biases if they are programmed to account for these nuances, although such systems also risk replicating existing societal biases. Hence, balancing the information provided to AI systems and continuously testing them against personas can inform more equitable and non-discriminatory practices.