Programming

17522 readers

379 users here now

Welcome to the main community in programming.dev! Feel free to post anything relating to programming here!

Cross posting is strongly encouraged in the instance. If you feel your post or another person's post makes sense in another community cross post into it.

Hope you enjoy the instance!

Rules

Follow the programming.dev instance rules
Keep content related to programming in some way
If you're posting long videos try to add in some form of tldr for those who don't want to watch videos

Wormhole

Follow the wormhole through a path of communities [email protected]

founded 1 year ago

MODERATORS

[email protected]

Do Users Write More Insecure Code with AI Assistants? (chaos.social)

submitted 10 months ago by [email protected] to c/[email protected]

29 comments fedilink hide all child comments

cross-posted from: https://programming.dev/post/8121843

~n (@[email protected]) writes:

This is fine...

"We observed that participants who had access to the AI assistant were more likely to introduce security vulnerabilities for the majority of programming tasks, yet were also more likely to rate their insecure answers as secure compared to those in our control group."

[Do Users Write More Insecure Code with AI Assistants?](https://arxiv.org/abs/2211.03622?

you are viewing a single comment's thread
view the rest of the comments

[–] [email protected] 28 points 10 months ago (1 children)

LLMs amplify biases by design, so this tracks.

[–] [email protected] 5 points 10 months ago (1 children)

What do you mean? Sounds to me like any other tool, it takes skill to use it well. Same as stack overflow, built in code suggestions or IDE generated code.

Not to detract from the usefulness of it just in terms of the fact that it requires knowledge to use well.

[–] [email protected] 1 points 10 months ago

As someone currently studying machine learning thoery and how these models are built, I'm explaining that built into the models at their core are functions that amplify the bias of the training data by identifying and using mathematical associations within the training data to create output. Because of that design, a naive approach to its use would result in amplified bias of not only the training data but also the person using the tool.