Safe and Ethical AI (SEA) Platform Network · Linking Artificial Intelligence Principles (LAIP)

Uphold high standards of scientific and technological excellence

Rebellion is committed to scientific excellence as we advance the development, testing, and deployment of artificial intelligence and other technologies. We believe AI should be interpretable and explainable to its human users. Because AI models are evolving so quickly, we will always seek out best practices as they evolve in artificial intelligence and throughout the software and defense industries. We strive for scientific rigor such that all scientific investigations, research, and practices are conducted with the highest level of precision and accuracy. This includes strict adherence to protocols, accurate data collection and analysis, and careful interpretation of results. Our team communicates research findings and methodologies clearly and openly in a manner that allows for the replication of results by independent researchers. Black box systems are antithetical to these standards. We build our technology to be intuitive and explainable in simple terms. In addition, we ensure the safety and security of the research, development, and production environments.

Principle: AI Ethical Principles, January 2023

Published by Rebelliondefense

Related Principles

6. We set the framework.

Our AI solutions are developed and enhanced on grounds of deep analysis and evaluation. They are transparent, auditable, fair, and fully documented. We consciously initiate the AI’s development for the best possible outcome. The essential paradigm for our AI systems’ impact analysis is “privacy und security by design”. This is accompanied e.g. by risks and chances scenarios or reliable disaster scenarios. We take great care in the initial algorithm of our own AI solutions to prevent so called “Black Boxes” and to make sure that our systems shall not unintentionally harm the users

Published by Deutsche Telekom in Deutsche Telekom’s guidelines for artificial intelligence, May 11, 2018

· Safety Assurance Framework

Frontier AI developers must demonstrate to domestic authorities that the systems they develop or deploy will not cross red lines such as those defined in the IDAIS Beijing consensus statement. To implement this, we need to build further scientific consensus on risks and red lines. Additionally, we should set early warning thresholds: levels of model capabilities indicating that a model may cross or come close to crossing a red line. This approach builds on and harmonizes the existing patchwork of voluntary commitments such as responsible scaling policies. Models whose capabilities fall below early warning thresholds require only limited testing and evaluation, while more rigorous assurance mechanisms are needed for advanced AI systems exceeding these early warning thresholds. Although testing can alert us to risks, it only gives us a coarse grained understanding of a model. This is insufficient to provide safety guarantees for advanced AI systems. Developers should submit a high confidence safety case, i.e., a quantitative analysis that would convince the scientific community that their system design is safe, as is common practice in other safety critical engineering disciplines. Additionally, safety cases for sufficiently advanced systems should discuss organizational processes, including incentives and accountability structures, to favor safety. Pre deployment testing, evaluation and assurance are not sufficient. Advanced AI systems may increasingly engage in complex multi agent interactions with other AI systems and users. This interaction may lead to emergent risks that are difficult to predict. Post deployment monitoring is a critical part of an overall assurance framework, and could include continuous automated assessment of model behavior, centralized AI incident tracking databases, and reporting of the integration of AI in critical systems. Further assurance should be provided by automated run time checks, such as by verifying that the assumptions of a safety case continue to hold and safely shutting down a model if operated in an out of scope environment. States have a key role to play in ensuring safety assurance happens. States should mandate that developers conduct regular testing for concerning capabilities, with transparency provided through independent pre deployment audits by third parties granted sufficient access to developers’ staff, systems and records necessary to verify the developer’s claims. Additionally, for models exceeding early warning thresholds, states could require that independent experts approve a developer’s safety case prior to further training or deployment. Moreover, states can help institute ethical norms for AI engineering, for example by stipulating that engineers have an individual duty to protect the public interest similar to those held by medical or legal professionals. Finally, states will also need to build governance processes to ensure adequate post deployment monitoring. While there may be variations in Safety Assurance Frameworks required nationally, states should collaborate to achieve mutual recognition and commensurability of frameworks.

Published by IDAIS (International Dialogues on AI Safety) in IDAIS-Venice, Sept 5, 2024

Design for human control, accountability, and intended use

Humans should have ultimate control of our technology, and we strive to prevent unintended use of our products. Our user experience enforces accountability, responsible use, and transparency of consequences. We build protections into our products to detect and avoid unintended system behaviors. We achieve this through modern software engineering and rigorous testing on our entire systems including their constituent data and AI products, in isolation and in concert. Additionally, we rely on ongoing user research to help ensure that our products function as expected and can be appropriately disabled when necessary. Accountability is enforced by providing customers with insight into the provenance of data sources, methodologies, and design processes in easily understood and transparent language. Effective governance — of data, models, and software — is foundational to the ethical and accountable deployment of AI.

Published by Rebelliondefense in AI Ethical Principles, January 2023

Ensure fairness

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

3 Ensure transparency, explainability and intelligibility

AI should be intelligible or understandable to developers, users and regulators. Two broad approaches to ensuring intelligibility are improving the transparency and explainability of AI technology. Transparency requires that sufficient information (described below) be published or documented before the design and deployment of an AI technology. Such information should facilitate meaningful public consultation and debate on how the AI technology is designed and how it should be used. Such information should continue to be published and documented regularly and in a timely manner after an AI technology is approved for use. Transparency will improve system quality and protect patient and public health safety. For instance, system evaluators require transparency in order to identify errors, and government regulators rely on transparency to conduct proper, effective oversight. It must be possible to audit an AI technology, including if something goes wrong. Transparency should include accurate information about the assumptions and limitations of the technology, operating protocols, the properties of the data (including methods of data collection, processing and labelling) and development of the algorithmic model. AI technologies should be explainable to the extent possible and according to the capacity of those to whom the explanation is directed. Data protection laws already create specific obligations of explainability for automated decision making. Those who might request or require an explanation should be well informed, and the educational information must be tailored to each population, including, for example, marginalized populations. Many AI technologies are complex, and the complexity might frustrate both the explainer and the person receiving the explanation. There is a possible trade off between full explainability of an algorithm (at the cost of accuracy) and improved accuracy (at the cost of explainability). All algorithms should be tested rigorously in the settings in which the technology will be used in order to ensure that it meets standards of safety and efficacy. The examination and validation should include the assumptions, operational protocols, data properties and output decisions of the AI technology. Tests and evaluations should be regular, transparent and of sufficient breadth to cover differences in the performance of the algorithm according to race, ethnicity, gender, age and other relevant human characteristics. There should be robust, independent oversight of such tests and evaluation to ensure that they are conducted safely and effectively. Health care institutions, health systems and public health agencies should regularly publish information about how decisions have been made for adoption of an AI technology and how the technology will be evaluated periodically, its uses, its known limitations and the role of decision making, which can facilitate external auditing and oversight.

Published by World Health Organization (WHO) in Key ethical principles for use of artificial intelligence for health, Jun 28, 2021