111
Microsoft's LinkedIn: If our AI gets it wrong, that's your problem
(www.theregister.com)
you are viewing a single comment's thread
view the rest of the comments
view the rest of the comments
this post was submitted on 13 Oct 2024
111 points (100.0% liked)
Technology
37702 readers
46 users here now
A nice place to discuss rumors, happenings, innovations, and challenges in the technology sphere. We also welcome discussions on the intersections of technology and society. If it’s technological news or discussion of technology, it probably belongs here.
Remember the overriding ethos on Beehaw: Be(e) Nice. Each user you encounter here is a person, and should be treated with kindness (even if they’re wrong, or use a Linux distro you don’t like). Personal attacks will not be tolerated.
Subcommunities on Beehaw:
This community's icon was made by Aaron Schneider, under the CC-BY-NC-SA 4.0 license.
founded 2 years ago
MODERATORS
I wouldn't say that - there's nothing preventing them from building in (stronger) guardrails and retraining the model based on input.
If it turns out the model suggests someone killing themselves based on very specific input, do you not think they should be held accountable to retrain the model and prevent that from happening again?
From an accountability perspective, there's no difference from a text generator machine and a soda generating machine.
The owner and builder should be held accountable and thereby put a financial incentive on making these tools more reliable and safer. You don't hold Tesla not accountable when their self driving kills someone because they didn't test it enough or build in enough safe guards – that'd be insane.
Stronger guardrails can help, sure. But getting new input and building a new model is the equivalent of replacing the entire vending machine with a different model by the same company if one is failing (by the old analogy).
The problem is that if you do the same thing with a llm for hiring or job systems, then the failure and bias instead is from the model being bigoted, which while illegal, is hidden in a model that is basically trained on how to be a more effective bigot.
You can't hide your race from the llm that was accidentally trained to know what job histories are traditionally black, or anything else.