Fairness and causality in machine learning

Counterfactual fairness formulates fair machine learning within a causal inference perspective, motivated by the intuition that defining or verifying fairness is a similar challenge to determining causality.

Joshua Loftus true
2022-02-17

Causal inference in fairness

This work approaches fair machine learning from a causal inference perspective, arguing that determining what is fair is a similar challenge to determining causality.

Press

Some of this work was covered in the New Scientist and 36Kr.

Publications

Reuse

Text and figures are licensed under Creative Commons Attribution CC BY 4.0. The figures that have been reused from other sources don't fall under this license and can be recognized by a note in their caption: "Figure from ...".

Citation

For attribution, please cite this work as

Loftus (2022, Feb. 17). neurath's speedboat: Fairness and causality in machine learning. Retrieved from http://joshualoftus.com/research/fairness-and-causality-in-machine-learning/

BibTeX citation

@misc{loftus2022fairness,
  author = {Loftus, Joshua},
  title = {neurath's speedboat: Fairness and causality in machine learning},
  url = {http://joshualoftus.com/research/fairness-and-causality-in-machine-learning/},
  year = {2022}
}