Towards AI Transparency and Accountability: A Global Framework for Exchanging Information on AI Systems
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Abstract (arXiv)
We propose that future AI transparency and accountability regulations are based on an open global standard for exchanging information about AI systems, which allows co-existence of potentially conflicting local regulations. Then, we discuss key components of a lightweight and effective AI transparency and/or accountability regulation. To prevent overregulation, the proposed approach encourages collaboration between regulators and industry to create a scalable and cost-efficient mutually beneficial solution. This includes using automated assessments and benchmarks with results transparently communicated through AI cards in an open AI register to facilitate meaningful public comparisons of competing AI systems. Such AI cards should report standardized measures tailored to the specific high-risk applications of AI systems and could be used for conformity assessments under AI transparency and accountability policies such as the European Union's AI Act.
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References · 10
- 2017A Unified Approach to Interpreting Model Predictionscited by 6,555
- 2018Datasheets for Datasetscited by 1,386
- 2017Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPRcited by 1,089
- 2018Model Cards for Model Reportingcited by 1,080
- 2018A Survey Of Methods For Explaining Black Box Modelscited by 973
- 2019Fooling LIME and SHAP: Adversarial Attacks on Post hoc Explanation Methodscited by 443
- 2022The Road to Explainability is Paved with Bias: Measuring the Fairness of Explanationscited by 41
- 2022Marrying Fairness and Explainability in Supervised Learningcited by 23
- 2023Use case cards: a use case reporting framework inspired by the European AI Actcited by 15
- 2019Learning from Discriminatory Training Datacited by 1
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