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AI tenant-screening tool SafeRent pays $2.28 million settlement for being massively racist (pivot-to-ai.com)

submitted 1 day ago by ForgottenFlux@lemmy.world to c/technology@lemmy.world

22 comments fedilink hide all child comments

SafeRent is a machine learning black box for landlords. It gives landlords a numerical rating of potential tenants and a yes/no result on whether to rent to them.

In May 2022, Massachusetts housing voucher recipients and the Community Action Agency of Somerville sued the company, claiming SafeRent gave Black and Hispanic rental applicants with housing vouchers disproportionately lower scores.

The tenants had no visibility into how the algorithm scored them. Appeals were rejected on the basis that this was what the computer output said.

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[–] 11111one11111@lemmy.world 1 points 3 hours ago* (last edited 3 hours ago)

-Edit:

Adding Edit to the beginning to stop the replies from people who read the scenario for context and can't fight their compulsion to reply by nitpicking my completely made up list of "unbiased" metrics. To these peeps I say, "Fucking no. Bad dog. No!" I don't fucking care about your commentary to a quickly made up scenario. Whatever qualms you have, just fuckin change the imaginary scenario so it fits the purpose of what the purpose of the story is serving.

-Preface of actual comment:

Completely made up scenario to give context to my question. This is not me defending anything referenced to the article.

-Actual scenario with read, write, edit permissions to all users:

What if the court order the release of the AI code and training methods for this tenant analysis AI bot and found the metrics used were simply credit score, salary, employer and former rental references. No supplied data for race, name, background check or anything else that would tip the boy toward or away from any bias results. So this pure as it could be bot still produces the same results as seen in the article. Again, imaginary scenario that is likely no foundation of truth.

-My questions for the provided context:

Are there studies that compare methods of training LLMs with results showing differences in results ranging from less or no racist bias and more racist bias?
Are there ways of training LLMs to perform without bias or is the problem with the LLM's code and no matter how you train them there will always be a bias presence?
In the exact imaginary scenario, would the pure, unbiased angel version of rhe AI bot but producing equally racist results as biased trained AI bots see different court rulings that the AI that shows it's flawed design caused the biased results?

-I'm using bias over racist to reach broader area beyond race related issues. My driving purposes is:

To better understand how courts are handling AI related cases and if they give a fuck about the framework and design of the AI or if none of that matters and the courts are just looking at the results;
Wondering if there are ways to make or already made LLMs that aren't biased and what about their design makes them biased, is it the doing of the makers of the LLM or is it the training and implication of the LLM by the enduser/training party that is to blame?