EU-U.S. Terminology and Taxonomy for Artificial Intelligence. First Edition Publication 31 May 2023
Why AI Terminology Matters
...The EU and U.S. understanding is based on the term “Trustworthy AI.” According to the EU HLEG Trustworthy AI has three components: (1) it should be lawful, ensuring compliance with all applicable laws and regulations (2) it should be ethical, demonstrating respect for, and ensure adherence to, ethical principles and values and (3) it should be robust, both from a technical and social perspective, since, even with good intentions, AI systems can cause unintentional harm. According to the NIST AI Risk Management Framework (AI RMF), characteristics of trustworthy AI systems include: valid and reliable, safe, secure and resilient, accountable and transparent, explainable and interpretable, privacy enhanced, and fair with their harmful biases managed. Trustworthy AI concerns not only the trustworthiness of the AI system itself but also comprises the trustworthiness of all processes and actors that are part of the AI system’s life cycle.
List of Terms
Data poisoning - A type of security attack where malicious users inject false training data with the aim of corrupting the learned model, thus making the AI system learn something that it should not learn.
Profiling - ‘Profiling’ means any form of automated processing of personal data consisting of the use of personal data to evaluate certain personal aspects relating to a natural person, in particular to analyse or predict aspects concerning that natural person's performance at work, economic situation, health, personal preferences, interests, reliability, behaviour, location or movements.
Human values for AI - Values are idealised qualities or conditions in the world that people find good.
AI systems are not value-neutral. The incorporation of human values into AI systems requires that we identify whether, how and what we want AI to mean in our societies. It implies deciding on ethical principles, governance policies, incentives, and regulations. And it also implies that we are aware of differences in interests and aims behind AI systems developed by others according to other cultures and principles.
The EU and U.S. are committed to the development of Trustworthy AI systems based on shared democratic values including the respect for the rule of law and human rights.
...The EU and U.S. understanding is based on the term “Trustworthy AI.” According to the EU HLEG Trustworthy AI has three components: (1) it should be lawful, ensuring compliance with all applicable laws and regulations (2) it should be ethical, demonstrating respect for, and ensure adherence to, ethical principles and values and (3) it should be robust, both from a technical and social perspective, since, even with good intentions, AI systems can cause unintentional harm. According to the NIST AI Risk Management Framework (AI RMF), characteristics of trustworthy AI systems include: valid and reliable, safe, secure and resilient, accountable and transparent, explainable and interpretable, privacy enhanced, and fair with their harmful biases managed. Trustworthy AI concerns not only the trustworthiness of the AI system itself but also comprises the trustworthiness of all processes and actors that are part of the AI system’s life cycle.
List of Terms
Data poisoning - A type of security attack where malicious users inject false training data with the aim of corrupting the learned model, thus making the AI system learn something that it should not learn.
Profiling - ‘Profiling’ means any form of automated processing of personal data consisting of the use of personal data to evaluate certain personal aspects relating to a natural person, in particular to analyse or predict aspects concerning that natural person's performance at work, economic situation, health, personal preferences, interests, reliability, behaviour, location or movements.
Human values for AI - Values are idealised qualities or conditions in the world that people find good.
AI systems are not value-neutral. The incorporation of human values into AI systems requires that we identify whether, how and what we want AI to mean in our societies. It implies deciding on ethical principles, governance policies, incentives, and regulations. And it also implies that we are aware of differences in interests and aims behind AI systems developed by others according to other cultures and principles.
The EU and U.S. are committed to the development of Trustworthy AI systems based on shared democratic values including the respect for the rule of law and human rights.