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update readme #33

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# Jurity: Fairness & Evaluation Library

Jurity ([LION'23](https://link.springer.com/chapter/10.1007/978-3-031-44505-7_29), [ICMLA'21](https://ieeexplore.ieee.org/document/9680169)) is a research library
Jurity ([ACM'24](https://dl.acm.org/doi/pdf/10.1145/3700145), [ArXiv'24](https://arxiv.org/pdf/2403.12069), [LION'23](https://link.springer.com/chapter/10.1007/978-3-031-44505-7_29), [ICMLA'21](https://ieeexplore.ieee.org/document/9680169)) is a research library
that provides fairness metrics, recommender system evaluations, classification metrics and bias mitigation techniques.
The library adheres to PEP-8 standards and is tested heavily.

Expand Down Expand Up @@ -81,7 +81,7 @@ An easy baseline is to convert these probabilities back to the deterministic set

Taking this a step further, while we do not have membership information at the individual level, consider access to _surrogate membership_ at _group level_. We can then infer the fairness metrics directly.

Jurity offers both options to address the case where membership data is missing. We provide an in-depth study and formal treatment in [Surrogate Membership for Inferred Metrics in Fairness Evaluation (LION 2023)]().
Jurity offers both options to address the case where membership data is missing. We provide an in-depth study and formal treatment in [ACM'24](https://dl.acm.org/doi/pdf/10.1145/3700145) and [LION'23](https://link.springer.com/chapter/10.1007/978-3-031-44505-7_29). For the analogous scenario of the lack of ground truth data, see [ArXiv'24](https://arxiv.org/pdf/2403.12069) and [ICMLA'21](https://ieeexplore.ieee.org/document/9680169).

```python
from jurity.fairness import BinaryFairnessMetrics
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If you use Jurity in a publication, please cite it as:

```bibtex
@article{10.1145/3700145,
author = {Kad\i{}o\u{g}lu, Serdar and Thielbar, Melinda},
title = {Surrogate Modeling to Address the Absence of Protected Membership Attributes in Fairness Evaluation},
year = {2024},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3700145},
doi = {10.1145/3700145},
journal = {ACM Trans. Evol. Learn. Optim.},
}

@article{DBLP:conf/lion/Melinda23,
author = {Melinda Thielbar, Serdar Kadioglu, Chenhui Zhang, Rick Pack, and Lukas Dannull},
title = {Surrogate Membership for Inferred Metrics in Fairness Evaluation},
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