Loan approval algorithms! also known as credit Google Shopping scoring algorithms! are often used by financial institutions to assess the creditworthiness of loan applicants — and if the algorithm assigns higher risk scores based on factors associated with minority groups! individuals in these communities may have difficulty accessing loans or be subject to unfavorable lending terms! perpetuating systemic inequalities and limiting economic opportunity.
The quality of the data
On this matter! Aracely Panameño! director of Latino phone number list affairs for the Center for Responsible Lending! says that “ that you’re putting into the underwriting algorithm is crucial. (…) If the data that you’re putting in is based on historical discrimination! then you’re basically cementing the discrimination at the other end.”
Google’s job search algorithm
And when it comes to job search algorithms! the concern is that biases in the algorithm could lead to unfair advantages or disadvantages for certain groups of candidates. Another investigation revealed that displayed gender bias! favoring higher-paying executive positions in search results for male candidates — so! if a job search algorithm consistently ranks higher-paying executive positions predominantly for male candidates! it could perpetuate existing gender disparities in the job market.
How to mitigate AI bias?
Artificial Intelligence is already a reality in the daily life of marketers and content creators! and avoiding it is not a good decision. In addition to checking all the material provided by machine learning! some points are essential to avoid and mitigate AI bias:
Provide diverse and representative training data
it is crucial to ensure that AI systems are trained keeping the manual up to date on diverse and representative datasets to mitigate biases! including data from various demographics! backgrounds! and perspectives. By broadening the dataset! AI models can learn to make fairer and more inclusive decisions.
Conduct constant evaluations and rigorous testing:
AI systems must undergo frequent and thorough australia database directory checks and tests to identify and correct possible biases. Independent audits can be performed to assess the performance and possible biases of AI models! which helps identify any unintended discriminatory patterns and take corrective action. This monitoring should involve reviewing feedback! user reports! and performance data to ensure fair results and correct information.