Some artificial intelligence (AI) systems have a hard time adapting to this . The Black Lives Matter movement has rais public awareness of historical and entrench biases in AI technologies for prictive policing and facial recognition . Although this insight is nothing new to researchers and ethicists, more and more news reports appear in the press. As a result, more attention is paid to this topic. It is more difficult, but also more important than ever that AI is right. Things won’t change until companies commit to developing and deploying AI in a responsible, ethical way.
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Prejudices are wrong For the success of businesses and the well-being of society, AI nes to be accurate, so biases should be eliminat as much as possible. Let’s consider the example of a financial institution attempting to gauge a customer’s “ability to repay” before approving a loan. Suppose this institution’s AI system bases someone’s Portugal Phone Number List repayment capacity on sensitive or protect variables, such as ethnic origin or gender (which are prohibit by the way), or on proxy variables (such as postal code, which can be relat to ethnic origin). In that case, there is a chance that a loan will be allocat to certain people who cannot pay it off, and that a loan will be refus to people who can pay it off.
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If a system gives more weight to ethnicity than income, a high-income black family may be exclud from a loan, while a low-income white family is offer a loan. Because AI makes decisions bas on the wrong factors, biases in the model are perpetuat. As a result, the financial institution may lose both money and customers, while potentially DV Leads forcing a customer segment to turn to lenders with very unfavorable terms. If the institution includes ethnic origin, gender, and proxy variables in the model, but chooses not to make a decision bas on these variables, accuracy greatly increases and the customer base grows. For example, if the institution finds that certain communities are not receiving loans, it may offer an alternative product that better meets their nes, such as microloans.