II. Technical robustness and safety
Trustworthy AI requires algorithms to be secure, reliable and robust enough to deal with errors or inconsistencies during all life cycle phases of the AI system, and to adequately cope with erroneous outcomes. AI systems need to be reliable, secure enough to be resilient against both overt attacks and more subtle attempts to manipulate data or algorithms themselves, and they must ensure a fall back plan in case of problems. Their decisions must be accurate, or at least correctly reflect their level of accuracy, and their outcomes should be reproducible.
In addition, AI systems should integrate safety and security by design mechanisms to ensure that they are verifiably safe at every step, taking at heart the physical and mental safety of all concerned. This includes the minimisation and where possible the reversibility of unintended consequences or errors in the system’s operation. Processes to clarify and assess potential risks associated with the use of AI systems, across various application areas, should be put in place.
VI. Societal and environmental well being
For AI to be trustworthy, its impact on the environment and other sentient beings should be taken into account. Ideally, all humans, including future generations, should benefit from biodiversity and a habitable environment. Sustainability and ecological responsibility of AI systems should hence be encouraged. The same applies to AI solutions addressing areas of global concern, such as for instance the UN Sustainable Development Goals.
Furthermore, the impact of AI systems should be considered not only from an individual perspective, but also from the perspective of society as a whole. The use of AI systems should be given careful consideration particularly in situations relating to the democratic process, including opinion formation, political decision making or electoral contexts. Moreover, AI’s social impact should be considered. While AI systems can be used to enhance social skills, they can equally contribute to their deterioration.
Mechanisms should be put in place to ensure responsibility and accountability for AI systems and their outcomes, both before and after their implementation. Auditability of AI systems is key in this regard, as the assessment of AI systems by internal and external auditors, and the availability of such evaluation reports, strongly contributes to the trustworthiness of the technology. External auditability should especially be ensured in applications affecting fundamental rights, including safety critical applications.
Potential negative impacts of AI systems should be identified, assessed, documented and minimised. The use of impact assessments facilitates this process. These assessments should be proportionate to the extent of the risks that the AI systems pose. Trade offs between the requirements – which are often unavoidable – should be addressed in a rational and methodological manner, and should be accounted for. Finally, when unjust adverse impact occurs, accessible mechanisms should be foreseen that ensure adequate redress.
AI systems should not be able to autonomously hurt, destroy or deceive humans
1. AI systems should be built to serve and inform, and not to deceive and manipulate
2. Nations should collaborate to avoid an arms race in lethal autonomous weapons, and such weapons should be tightly controlled
3. Active cooperation should be pursued to avoid corner cutting on safety standards
4. Systems designed to inform significant decisions should do so impartially
Second, the principle of security.
AI shall not harm human beings. AI systems must be secure, applicable and controllable. Personal privacy should be protected and data breach and abuse prevented. AI algorithms must be traceable and transparent and there should be no algorithm discrimination;