Search Results for author: Joshua R. Loftus

Found 8 papers, 3 papers with code

A New Paradigm for Counterfactual Reasoning in Fairness and Recourse

no code implementations25 Jan 2024 Lucius E. J. Bynum, Joshua R. Loftus, Julia Stoyanovich

The traditional paradigm for counterfactual reasoning in this literature is the interventional counterfactual, where hypothetical interventions are imagined and simulated.

counterfactual Counterfactual Reasoning +1

Causal Dependence Plots

no code implementations7 Mar 2023 Joshua R. Loftus, Lucius E. J. Bynum, Sakina Hansen

Since people often think causally about input-output dependence, CDPs can be powerful tools in the xAI or interpretable machine learning toolkit and contribute to applications like scientific machine learning and algorithmic fairness.

Explainable Artificial Intelligence (XAI) Fairness +1

Counterfactuals for the Future

no code implementations7 Dec 2022 Lucius E. J. Bynum, Joshua R. Loftus, Julia Stoyanovich

Counterfactuals are often described as 'retrospective,' focusing on hypothetical alternatives to a realized past.


Disaggregated Interventions to Reduce Inequality

1 code implementation1 Jul 2021 Lucius E. J. Bynum, Joshua R. Loftus, Julia Stoyanovich

We develop a disaggregated approach to tackling pre-existing disparities that relaxes the typical set of assumptions required for the use of social categories in structural causal models.

Causal intersectionality for fair ranking

2 code implementations15 Jun 2020 Ke Yang, Joshua R. Loftus, Julia Stoyanovich

In this paper we propose a causal modeling approach to intersectional fairness, and a flexible, task-specific method for computing intersectionally fair rankings.

Causal Inference Fairness

Causal Interventions for Fairness

no code implementations6 Jun 2018 Matt J. Kusner, Chris Russell, Joshua R. Loftus, Ricardo Silva

Most approaches in algorithmic fairness constrain machine learning methods so the resulting predictions satisfy one of several intuitive notions of fairness.


Causal Reasoning for Algorithmic Fairness

no code implementations15 May 2018 Joshua R. Loftus, Chris Russell, Matt J. Kusner, Ricardo Silva

In this work, we argue for the importance of causal reasoning in creating fair algorithms for decision making.

Decision Making Fairness

Counterfactual Fairness

3 code implementations NeurIPS 2017 Matt J. Kusner, Joshua R. Loftus, Chris Russell, Ricardo Silva

Machine learning can impact people with legal or ethical consequences when it is used to automate decisions in areas such as insurance, lending, hiring, and predictive policing.

BIG-bench Machine Learning Causal Inference +2

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