Capacity on sensitive or protected variables, such as ethnic origin or gender (which are prohibited by the way), or on proxy variables (such as postal code, which can be related to ethnic origin). In that case, there is a chance that a loan will be allocated to certain people who cannot pay it off, and that a loan will be refused to people who can pay it off. If a system gives more weight to ethnicity than income, a high-income black family may be excluded from a loan, while a low-income white family is offered a loan. Because AI makes decisions based on the wrong factors, biases in the model are perpetuated.
As a result, the financial institution may lose both money and customers, while potentially 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 based on these Israel phone number list 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 needs, such as microloans. This method creates an efficient interaction: the financial strength of customers improves so that they may eventually qualify for traditional credit products from the bank.
Organizations are responsible for fair, accurate AI solutions. awareness and commitment. We don't have a ready-made solution, but here are four strategies to get you started: 1. Check whether your systems and processes contain underlying biases Recent discussions about AI and biases question the notion of 'unbiased data' . Since all data has biases in it, you need to take a step back and assess the systems and processes with biases maintained . Examine the decisions your systems make based on sensitive variables: Do certain factors have too much weight, such as ethnic origin or gender? Are there differences by region or even by decision maker.