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Dmitri Kalintsev

@skysailor @inthehands true, you can test the output. I suspect tho that the model will respond differently to the same input fed to it multiple times if the seed varies. And if you don't vary the seed, how do you know that a different one won't produce the results you don't want? Do you then iterate through the entire seed space?

6 comments
Dmitri Kalintsev

@skysailor @inthehands thinking a bit more about it, I suppose you could test for a specific random seed and then always use that value..

Sky, Cozy Goth Prince of Cats

@dkalintsev @inthehands Hmm.

Thinking from a tech POV, I guess what I would want to know is:
Did the algorithm incorporate a random seed during post-training use? (Since, as far as I can tell, they're often just used during training/testing before deployment.)

If so, which seed settings did the vendor/employer recommend/use when making the sued-over hiring decisions?

Sky, Cozy Goth Prince of Cats

@dkalintsev @inthehands Thinking from a court POV, you'd probably (1) be looking at what seed(s) the vendor/employer actually used, and (2) have the opposing sides' attorneys trying out different seeds to see what favored their arguments best, and the court being left to decide what to make of that.

Sky, Cozy Goth Prince of Cats

@dkalintsev @inthehands There's a huge human element here in terms of the ability of the attorneys and their experts to explain that part of the tech and its relevance, the ability of the judge/jury to understand and interpret that, and how persuasive those explanations are as to convincing the judge/jury to favor one side or another.

Dmitri Kalintsev

@skysailor @inthehands oh, I can see that.

Regarding the seed, there would be one used for training and then another for inference.

From my admittedly limited and slightly orthogonal experience - I've played with image gen models, not language ones: you can get the same output from a given trained model if you feed it the same prompt and the same seed. But, you can't train another copy of that model, even using the same source data, training parameters, and seed. Your "supposedly same" model will generate completely different outputs, even with the same prompt and inference seed. Sigh. This is all such an alchemy. :(

@skysailor @inthehands oh, I can see that.

Regarding the seed, there would be one used for training and then another for inference.

From my admittedly limited and slightly orthogonal experience - I've played with image gen models, not language ones: you can get the same output from a given trained model if you feed it the same prompt and the same seed. But, you can't train another copy of that model, even using the same source data, training parameters, and seed. Your "supposedly same" model will...

Paul Cantrell

@dkalintsev @skysailor I suspect all this is a bit of a red herring. With a machine model, you can do things you could •never• do with an HR dept: Run it on 10 million resumes. Run it on repeatedly on the same resumes, altering on variable. Random? Run it on each 1000x. It’s a kind of broad testing that, should a court allow, would make many of the questions above evaporate.

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