Did you know that AI can be biased in appraisal valuations, voice mirroring, and credit decisioning?
This happens when the data used to train the AI algorithm is biased, leading to unfair results. Or in simple terms the input is steered.
For example, if the data set only includes homes in white neighborhoods, the AI algorithm may value homes in white neighborhoods higher than homes in minority neighborhoods. Similarly, AI mirroring your voice to hack your bank account is a potential security risk.
Attackers can record your voice and use AI to mimic it, potentially gaining access to your accounts.
Additionally, AI credit decisioning in mortgage loans may not be democratic if the algorithm is not trained on a diverse data set. Awareness of these potential biases is key to mitigating them.
Read more about regulation coming regarding AI bias here: https://archive.ph/fpcZ7
- AI bias in appraisal valuations can occur when the data used to train the AI algorithm is biased. For example, if the data set only includes homes in white neighborhoods, the AI algorithm may be more likely to value homes in white neighborhoods higher than homes in minority neighborhoods.
- AI mirroring your voice to be used to hack your bank account is a potential security risk. If an attacker can record your voice and then use AI to mimic your voice, they could potentially use this to gain access to your bank account or other sensitive accounts.
- AI credit decisioning not democratic can occur when the AI algorithm is not trained on a diverse data set. For example, if the data set only includes people with high credit scores, the AI algorithm may be more likely to approve loans for people with high credit scores, even if people with lower credit scores would be just as likely to repay the loan.
These are just a few examples of how AI can be biased. It is important to be aware of these potential biases so that we can take steps to mitigate them.
Here are some things that can be done to mitigate AI bias:
- Use a diverse data set when training AI algorithms. This will help to ensure that the algorithm is not biased towards any particular group of people.
- Use algorithms that are transparent. This will allow people to understand how the algorithm works and identify any potential biases.
- Use algorithms that are auditable. This will allow people to check the algorithm's output for any potential biases.
Can AI be used in a fair and equitable way?
