Catalogue of Tools & Metrics for Trustworthy AI

These tools and metrics are designed to help AI actors develop and use trustworthy AI systems and applications that respect human rights and are fair, transparent, explainable, robust, secure and safe.

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Data Banzhaf

4 citations of this metric
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The Banzhaf power index is a power index defined by the probability of changing an outcome of a vote where voting rights are not necessarily equally divided among the voters. Data Banzhaf uses this notion to measure data points' "voting powers" towards algorithmic decisions.

References

1. Nikulin, M.S., 2001. Hellinger distance. In Encyclopedia of Mathematics. EMS Press. 2. Hellinger, E., 1909. Neue Begründung der Theorie quadratischer Formen von unendlichvielen Veränderlichen. Journal für die reine und angewandte Mathematik, 136, pp.210–271. 3. Jeffreys, H., 1946. An invariant form for the prior probability in estimation problems. Proceedings of the Royal Society of London. Series A. Mathematical and Physical Sciences, 186(1007), pp.453–461. 4. Liese, F. and Miescke, K.-J., 2008. Statistical Decision Theory: Estimation, Testing, and Selection. Springer. 5. Pardo, L., 2006. Statistical Inference Based on Divergence Measures. Chapman and Hall/CRC.

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Disclaimer: The tools and metrics featured herein are solely those of the originating authors and are not vetted or endorsed by the OECD or its member countries. The Organisation cannot be held responsible for possible issues resulting from the posting of links to third parties' tools and metrics on this catalogue. More on the methodology can be found at https://oecd.ai/catalogue/faq.