System Analysis for Responsible Design of Modern AI/ML Systems
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Abstract (arXiv)
The irresponsible use of ML algorithms in practical settings has received a lot of deserved attention in the recent years. We posit that the traditional system analysis perspective is needed when designing and implementing ML algorithms and systems. Such perspective can provide a formal way for evaluating and enabling responsible ML practices. In this paper, we review components of the System Analysis methodology and highlight how they connect and enable responsible practices of ML design.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 9 of its 23 references that are in the map. First posted on arXiv 19 Apr 2022.
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9 of 23 references.
References · 9
- 2017A Unified Approach to Interpreting Model Predictionscited by 6,555
- 2018Model Cards for Model Reportingcited by 1,080
- 2017Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networkscited by 744
- 2016Inherent Trade-Offs in the Fair Determination of Risk Scorescited by 586
- 2021AI and the Everything in the Whole Wide World Benchmarkcited by 349
- 2018Improving fairness in machine learning systems: What do industry practitioners need?cited by 262
- 2021Beyond Expertise and Roles: A Framework to Characterize the Stakeholders of Interpretable Machine Learning and their Needscited by 64
- 2022Measuring Disparate Outcomes of Content Recommendation Algorithms with Distributional Inequality Metricscited by 8
- 2021Principles for Evaluation of AI/ML Model Performance and Robustnesscited by 2
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