Towards Human-AI Complementarity with Predictions Sets

This repository is the official implementation of Towards Human-AI Complementarity with Predictions Sets.

Requirements

This code was tested on Python 3.8 and on a Linux machine. The main prerequisite to run our experiments is to have a working MPI implementation (see here for additional instructions), since our scripts natively support multiprocessing. You will need a working Latex installation to instead run the scripts generating the plots available in the manuscript. On a Linux machine, please run the following to install the packages within a conda environment:

conda create --name hai-psets python=3.8
conda activate hai-psets
pip install -r requirements.txt

Evaluation

In order to run our scripts, make sure to position yourself in the base directory and to activate the correct environment.

cd towards-human-ai-complementarity-predictions-sets
conda activate hai-psets
export PYTHONPATH=.

Please have a look to the following bash script running all of our experiments and analysis:

• run_all_synthetic.sh: run the synthetic experiment detailed in Section 5 (it might take some time).
• run_all_real_data.sh: run the real data experiment detailed in Section 6 (it might take some time).
• generate_all_plots.sh: generate all plots presented in the paper.
• generale_all_results.sh: generate the Latex code for the various tables presented in the paper.

Running synthetic experiments

The script run_synthetic_experiment.py runs the synthetic experiments detailed in Section 5. Below you can find its invocation for all the prediction tasks with 10 labels where we apply calibration to the model. The mpirun -n 2 invocation parallelizes the execution over 2 physical cores.

mpirun -n 2 python run_synthetic_experiment.py --calibrate --labels 10

Running experiments with real data

The script run_real_experiment.py runs instead the experiments with the real data. For example, the following command generates the results for the experiment presented in Section 6.

mpirun - n 2 python run_real_experiment.py --model-epochs epoch10 --calibrate top-k --ranks 5 --calibration-size 800

Repository Structure

The directories have the following content:

• analytics/: it contains python scripts to parse and analyze the results of the main paper.
• data/: it contains the ImageNet-16H data needed for the experiments and some python utility functions to preprocess them.
• results/: it contains a series of pickle files with our experimental results in a raw format.
• utils/: it contains utility functions and code to run the experiments.

Citation

If you use parts of the code in this repository for your research purposes, please consider citing:

@article{detoni2024humanai,
      title={Towards Human-AI Complementarity with Predictions Sets}, 
      author={Giovanni De Toni and Nastaran Okati and Suhas Thejaswi and Eleni Straitouri and Manuel Gomez-Rodriguez},
      year={2024},
      journal={arXiv preprint arXiv:2405.17544}
}