PRESS RELEASE: EU Ethics Guidelines for Trustworthy AI presented at the EU Digital Day
The three founders of ALLAI, Catelijne Muller, Aimee van Wynsberghe and Virginia Dignum are proud to have contributed to the EU Ethics Guidelines for Trustworthy AI, released on April 9.
After an intensive 9 month process, united in diversity, the High Level Expert Group on AI, today released its first Deliverable: The Ethics Guidelines for Trustworthy AI: a 3-step guidance on how develop, implement and use AI in a Trustworthy manner throughout its entire life cycle.
Trustworthy AI has three components, which should be met throughout the system’s entire life cycle:
I. Lawful: complying with all applicable laws and regulations
II. Ethical: ensuring adherence to ethical principles and values and
III. Technically and Socially Robust
7 key requirements for Trustworthy AI
The guidelines put forward a set of 7 key requirements that AI systems should meet in order to be deemed trustworthy. A specific assessment list aims to help verify the application of each of the key requirements:
- 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 date 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.