Encoder Decoder, Jonathan Drechsel, Steffen Herbold, ∇ -Wm Wm
We lifted 11 functions out of this paper's own repositories and ran 6 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| aieng-lab/gradiend-bias | canonical | 4 of 8 |
| aieng-lab/gradiend | — | 2 of 3 |
| Function | Status | Where it lives |
|---|---|---|
| LargeLinear | Ran | aieng-lab/gradiend-bias/gradiend/model.py code served (permissive licence) · get_code("2fce88cdf9c23568") |
| LargeLinear | Ran | aieng-lab/gradiend/gradiend/model/model.py code served (permissive licence) · get_code("f0ae40797e7c4249") |
| convert_tuple_keys_recursively | Ran | aieng-lab/gradiend-bias/gradiend/model.py code served (permissive licence) · get_code("20e1c499fda1de6c") |
| get_activation | Ran | aieng-lab/gradiend-bias/gradiend/model.py code served (permissive licence) · get_code("c056e040a07e6c67") |
| get_activation | Ran | aieng-lab/gradiend/gradiend/model/model.py code served (permissive licence) · get_code("b5d368803b6b5a6c") |
| hash_it | Ran | aieng-lab/gradiend-bias/gradiend/model.py code served (permissive licence) · get_code("e9a1e0ae7afb92c6") |
| GradiendModel | Not yet run | aieng-lab/gradiend-bias/gradiend/model.py code served (permissive licence) · get_code("f3498d317263f1a7") |
| GradiendModel | Not yet run | aieng-lab/gradiend/gradiend/model/model.py code served (permissive licence) · get_code("0b09e27b255b08fa") |
| compute_bias_score | Not yet run | aieng-lab/gradiend-bias/gradiend/evaluation/analyze_decoder.py code served (permissive licence) · get_code("7c76d06aaa408115") |
| compute_bias_score_v1 | Not yet run | aieng-lab/gradiend-bias/gradiend/evaluation/analyze_decoder.py code served (permissive licence) · get_code("63880b46142f096d") |
| compute_bias_score_v2 | Not yet run | aieng-lab/gradiend-bias/gradiend/evaluation/analyze_decoder.py code served (permissive licence) · get_code("4f6a9dbc21fa4d20") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
AI systems frequently exhibit and amplify social biases, leading to harmful consequences in critical areas. This study introduces a novel encoder-decoder approach that leverages model gradients to learn a feature neuron encoding societal bias information such as gender, race, and religion. We show that our method can not only identify which weights of a model need to be changed to modify a feature, but even demonstrate that this can be used to rewrite models to debias them while maintaining other capabilities. We demonstrate the effectiveness of our approach across various model architectures and highlight its potential for broader applications.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2502.01406")
get_code_for_paper("2502.01406")
have("2502.01406")
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