Published by Cyberspace Administration of China in Global AI Governance Initiative, October 18, 2023

We should develop comprehensive methods and techniques to operationalize these red lines prior to there being a meaningful risk of them being crossed. To ensure red line testing regimes keep pace with rapid AI development, we should invest in red teaming and automating model evaluation with appropriate human oversight. The onus should be on developers to convincingly demonstrate that red lines will not be crossed such as through rigorous empirical evaluations, quantitative guarantees or mathematical proofs.

Published by IDAIS (International Dialogues on AI Safety) in IDAIS-Beijing, May 10, 2024

We are fully determined to combat all types of reducible bias in data collection, derivation, and analysis. Our teams are trained to identify and challenge biases in our own decision making and in the data we use to train and test our models. All data sets are evaluated for fairness, possible inclusion of sensitive data and implicitly discriminatory collection models. We execute statistical tests to look for imbalance and skewed datasets and include methods to augment datasets to combat these statistical biases. We pressure test our decisions by performing peer review of model design, execution, and outcomes; this includes peer review of model training and performance metrics. Before a model is graduated from one development stage to the next, a review is conducted with required acceptance criteria. This review includes in sample and out of sample testing to mitigate the risk of model overfitting to the training data, and biased outcomes in production. We subscribe to the principles laid out in the Department of Defense’s AI ethical principles: that AI technologies should be responsible, equitable, traceable, reliable, and governable.

Published by Rebelliondefense in AI Ethical Principles, January 2023

As with any of our products, our AI software is subject to our quality assurance process, which we continuously adapt when necessary. Our AI software undergoes thorough testing under real world scenarios to firmly validate they are fit for purpose and that the product specifications are met. We work closely with our customers and users to uphold and further improve our systems’ quality, safety, reliability, and security.

Published by SAP in SAP's Guiding Principles for Artificial Intelligence, Sep 18, 2018

1 To develop a sound and functional AI system that is both reliable and safe, the AI system’s technical construct should be accompanied by a comprehensive methodology to test the quality of the predictive data based systems and models according to standard policies and protocols. 2 To ensure the technical robustness of an AI system rigorous testing, validation, and re assessment as well as the integration of adequate mechanisms of oversight and controls into its development is required. System integration test sign off should be done with relevant stakeholders to minimize risks and liability. 3 Automated AI systems involving scenarios where decisions are understood to have an impact that is irreversible or difficult to reverse or may involve life and death decisions should trigger human oversight and final determination. Furthermore, AI systems should not be used for social scoring or mass surveillance purposes.

Published by SDAIA in AI Ethics Principles, Sept 14, 2022