This repository houses the code for reproducing the experimental results from the paper:

Esther Rolf, Max Simchowitz, Sarah Dean, Lydia T. Liu, Daniel Björkegren, Moritz Hardt, and Joshua Blumenstock. Balancing Competing Objectives with Noisy Data: Score-Based Classifiers for Welfare-Aware Machine Learning. Proceedings of the 37th International Conference on Machine Learning (ICML). 2020.

Populate the data folder with the abalone dataset and the credit score data from the links below. Once done, the structure should look like:

multi-objective-impact

multi-objective-impact  
├── code  
├── data  
│   ├── abalone  
│   │   └── abalone.data  
│   ├── fico  
│   │   ├── totals.csv  	
│   │   ├── transrisk_cdf_by_race_ssa.csv	  
│   │   └── transrisk_performance_by_race_ssa.csv  

The outside datasets to reproduce our results are:

• The dataset for the abalone experiments is available for download at https://archive.ics.uci.edu/ml/datasets/Abalone.
• The dataset for the credit score example is available for download at https://github.com/fairmlbook/fairmlbook.github.io/tree/master/code/creditscore/data.
• The dataset an intermediate result from A Longitudinal Analysis of YouTube's promotion of Conspiracy Videos by authors Marc Fadoul, Guillaume Chaslot and Hany Farid. (we used it with explicit permission from the authors). For the most up-to-date data from that study, please see https://github.com/youtube-dataset/conspiracy.

We recommend using this code with a 3.7.1 standard Anaconda install. The following packages are required to run our code (we tested our code with the versions in parenthesis):

numpy 1.15.4  
pandas 0.23.4  
sklearn 0.20.1  
matplotlib 3.0.2  
seaborn 0.9.0