Summarize the Ethics Guidelines for Trustworthy AI by the European Commission
The Ethics Guidelines for Trustworthy AI by the European Commission seek to foster responsible and sustainable AI innovation in Europe. They aim to make ethics a core pillar for developing a unique approach to AI that benefits, empowers, and protects both individual human flourishing and the common good of society. The guidelines were created by the High-Level Expert Group on Artificial Intelligence and present an overall framework for Trustworthy AI. The document includes a Trustworthy AI assessment list that will undergo a piloting phase by stakeholders to gather practical feedback, with a revised version of the assessment list presented to the European Commission in early 2020.
Conditions for Trusworthy AI
According to the Ethics guideline for trustworthy AI follwing conditions need to be fullfilled by the AI-System:
Lawful – AI should respect all applicable laws and regulations
Ethical – AI should respect ethical principles and values
Robust – AI should be robust from a technical perspective while taking into account it’s social environment
Key Requirements for Trustworthy AI
Human agency and oversight: AI systems should empower human beings, allowing them to make informed decisions and fostering their fundamental rights. At the same time, proper oversight mechanisms need to be ensured, which can be achieved through human-in-the-loop, human-on-the-loop, and human-in-command approaches
Technical Robustness and safety: AI systems need to be resilient and secure. They need to be safe, ensuring a fall back plan in case something goes wrong, as well as being accurate, reliable and reproducible. That is the only way to ensure that also unintentional harm can be minimized and prevented.
Privacy and data governance: besides ensuring full respect for privacy and data protection, adequate data governance mechanisms must also be ensured, taking into account the quality and integrity of the data, and ensuring legitimised access to data.
Transparency: the data, system and AI business models should be transparent. Traceability mechanisms can help achieving this. Moreover, AI systems and their decisions should be explained in a manner adapted to the stakeholder concerned. Humans need to be aware that they are interacting with an AI system, and must be informed of the system’s capabilities and limitations.
Diversity, non-discrimination and fairness: Unfair bias must be avoided, as it could could have multiple negative implications, from the marginalization of vulnerable groups, to the exacerbation of prejudice and discrimination. Fostering diversity, AI systems should be accessible to all, regardless of any disability, and involve relevant stakeholders throughout their entire life circle.
Societal and environmental well-being: AI systems should benefit all human beings, including future generations. It must hence be ensured that they are sustainable and environmentally friendly. Moreover, they should take into account the environment, including other living beings, and their social and societal impact should be carefully considered.
Accountability: Mechanisms should be put in place to ensure responsibility and accountability for AI systems and their outcomes. Auditability, which enables the assessment of algorithms, data and design processes plays a key role therein, especially in critical applications. Moreover, adequate an accessible redress should be ensured.
