Updates that may improve an AI system’s accuracy can also introduce new and unanticipated errors that damage user trust. Updates that introduce new errors can also break trust between software components and machine learning models, as these errors are propagated and compounded throughout larger integrated AI systems. The Backward Compatibility ML library is an open-source project for evaluating AI system updates in a new way for increasing system reliability and human trust in AI predictions for actions.

The Backward Compatibility ML project has two components:

• A series of loss functions in which users can vary the weight assigned to the dissonance factor and explore performance/capability tradeoffs during machine learning optimization.

• Visualization widgets that help users examine metrics and error data in detail. They provide a view of error intersections between models and incompatibility distribution across classes.

1. Setup a Python virtual environment or Conda environment and activate it.
2. From within the root folder of this project do pip install -r requirements.txt
3. From within the root folder do npm install
4. From within the root folder of this project do npx webpack && pip install -e .
5. You should now be able to import the backwardcompatibilityml module and use it.

Start your Jupyter Notebooks server and load in the example notebook under the examples folder to see how the backwardcompatibilityml module is used.

To demo the widget, open the notebook compatibility-analysis.ipynb.

To run tests, make sure that you are in the project root folder and do:

1. pip install -r dev-requirements.txt
2. pytest tests/

Check CONTRIBUTING page.

This project materializes and implements ideas from ongoing research on Backward Compatibility in Machine Learning and Model Comparison. Here is a list of development and research contributors:

Current Project Leads: Xavier Fernandes, Juan Lema, Besmira Nushi

Research Contributors: Gagan Bansal, Megha Srivastava, Besmira Nushi, Ece Kamar, Eric Horvitz, Dan Weld, Shital Shah

References

"Updates in Human-AI Teams: Understanding and Addressing the Performance/Compatibility Tradeoff." Gagan Bansal, Besmira Nushi, Ece Kamar, Daniel S Weld, Walter S Lasecki, Eric Horvitz; AAAI 2019. Pdf

@inproceedings{bansal2019updates,
  title={Updates in human-ai teams: Understanding and addressing the performance/compatibility tradeoff},
  author={Bansal, Gagan and Nushi, Besmira and Kamar, Ece and Weld, Daniel S and Lasecki, Walter S and Horvitz, Eric},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={33},
  pages={2429--2437},
  year={2019}
}

"An Empirical Analysis of Backward Compatibility in Machine Learning Systems." Megha Srivastava, Besmira Nushi, Ece Kamar, Shital Shah, Eric Horvitz; KDD 2020. Pdf

@inproceedings{srivastava2020empirical,
  title={An Empirical Analysis of Backward Compatibility in Machine Learning Systems},
  author={Srivastava, Megha and Nushi, Besmira and Kamar, Ece and Shah, Shital and Horvitz, Eric},
  booktitle={Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery \& Data Mining},
  pages={3272--3280},
  year={2020}
}

"Towards Accountable AI: Hybrid Human-Machine Analyses for Characterizing System Failure." Besmira Nushi, Ece Kamar, Eric Horvitz; HCOMP 2018. Pdf

@article{nushi2018towards,
  title={Towards accountable ai: Hybrid human-machine analyses for characterizing system failure},
  author={Nushi, Besmira and Kamar, Ece and Horvitz, Eric},
  journal={ Proceedings of the Sixth AAAI Conference on Human Computation and
               Crowdsourcing},
  pages = {126--135},
  year={2018}
}

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