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Labelled responses from remote datasets can't reach `HumanLabeledDataset`

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Assessment

Difficulty
4/5
Estimated time
3-5 days
Newbie friendliness
48/100
Issue type
Feature
Clarity
Mostly clear
Activity status
Active
Tech stack
huggingface, python
Domain
data, machine-learning, testing-qa

Research direction

Start with datasets/seed_datasets/remote/aegis_ai_content_safety_dataset.py and the HumanLabeledDataset path, then compare the existing HARM_CATEGORY_ALIAS_OVERRIDES mapping and loader behavior with datasets/scorer_evals/refusal.csv. Add focused tests for the narrow Aegis 2.0 case and generate a usable scorer_evals CSV retaining assistant_response and the selected human label.

Written by the indexing model from the issue text.

Description

There's no path from the remote dataset loaders to the labelled-data side of scoring.

What's there now. HumanLabeledDataset is only constructible via from_csv(), so scorer evaluation data has to be authored by hand. That's reflected in datasets/scorer_evals/ — refusal.csv is ~105 rows and several of the objective sets are single digits.

What's being dropped. datasets/seed_datasets/remote/ has 61 loaders, and several of the underlying HuggingFace datasets ship human labels alongside model responses. Those are discarded at load time because SeedDataset models prompts only. beaver_tails_dataset.py states it directly:

This loader extracts only the prompts (not the responses) and filters to unsafe entries by default.

aegis_ai_content_safety_dataset.py is the same shape. wildguardmix_dataset.py at least retains its classifier labels in metadata, but still produces a SeedDataset.

So labelled (response, score) pairs are already flowing through the loaders and being dropped, while the one class that needs them can only be fed by hand.

Proposal. A second output path off the existing remote loaders producing HumanLabeledDataset — reusing the fetch logic and the HARM_CATEGORY_ALIAS_OVERRIDES mapping that's already there, but retaining assistant_response and the human label.

Suggest starting narrow: Aegis 2.0 only (CC-BY-4.0, so no licence friction for a repo shipping under MIT) and one harm category that maps cleanly, with tests and a generated scorer_evals CSV so it's usable on merge.

Happy to build it if the shape is right — is HumanLabeledDataset the correct target here, or is there a reason the loaders are prompts-only that I'm missing?

Dominant language
Python
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Avg merge
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Merged PRs (30d)
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