this post was submitted on 12 Jun 2023
17 points (100.0% liked)

Technology

38106 readers
558 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 3 years ago
MODERATORS
top 10 comments
sorted by: hot top controversial new old
[–] thebardingreen@lemmy.starlightkel.xyz 5 points 2 years ago* (last edited 2 years ago)

"Particularly underrepresented groups include Mormons and those over 65..."

What a disaster! I hope someone gets on that ASAP! /s

[–] RobertJCross@lemmy.one 2 points 2 years ago (1 children)

Bias shouldn’t exist in a language model. Human beings continue to complicate reality because of boredom.

[–] Gaywallet@beehaw.org 4 points 2 years ago (1 children)

I'm lost here, what are you trying to say

[–] RobertJCross@lemmy.one 1 points 2 years ago

Given a model is given a complete set of human data, there should be equal amounts of leaning in all directions. Therefore, no bias. If bias is found then the data set is incomplete and/or the person or persons creating the model are only feeding the model their own selection of data. A perfect AI is like Switzerland.

[–] Hexorg@beehaw.org 2 points 2 years ago (1 children)

I wonder if it's possible to bring public opinion into the error function - find weights for ChatGPT such that the next token is predicted correctly but also such that the overall output falls within the public average opinion.... But then - is that a "good enough" metric?

[–] Gaywallet@beehaw.org 4 points 2 years ago (1 children)

The ways to control for algorithmic bias are typically through additional human developed layers to counteract bias present when you ingest large datasets to train. But that's extremely work intensive. I've seen some interesting hypotheticals where algorithms designed specifically to identify bias can be used to tune layers with custom weighting to attempt to pull bias back down to acceptable levels, but even then we'll probably need to watch how this changes language about groups for which there is bias.

[–] Hexorg@beehaw.org 3 points 2 years ago (1 children)

I think the trouble with human oversight is that it’s still going to keep whatever bias the overseer has.

[–] Gaywallet@beehaw.org 3 points 2 years ago

AI is programmed by humans or trained on human data. Either we're dealing in extremes where it's impossible to not have bias (which is important framing to measure bias) or we're talking about how to minimize bias not make it perfect.

[–] bear_delune@beehaw.org 1 points 2 years ago

I don't see how misaligning to public opinion = bias.

The public is already hugely biased; we surrender the general education of the entire adult population to news media, social media, and the entertainment industry. Which all sway public perception for their own financial and political gains.

Tbh the "public" as a mass entity is going to be more wrong than a language model; I see who y'all vote for, I wouldn't trust you with anything 🫠