Transparency Requirement
AI systems should be designed and operated as transparently as possible. The logic behind the AI’s decision-making process should be understandable by humans. This is particularly important for AI systems used in critical areas like healthcare, finance, or criminal justice.
Data Protection and Privacy
AI systems often rely on large amounts of data, which can include sensitive personal information. Strict data protection measures should be in place to ensure the privacy of individuals. This includes obtaining informed consent before data collection and ensuring data is anonymized and securely stored.
Accountability and Liability
Clear lines of accountability should be established for AI systems. If an AI system causes harm, it should be possible to determine who is legally responsible. This could be the developer of the AI, the operator, or the owner, depending on the circumstances.
Fairness and Non-Discrimination
AI systems should not perpetuate or amplify bias and discrimination. They should be tested for bias and fairness, and measures should be in place to correct any identified bias.
Safety and Robustness
AI systems should be safe to use and robust against manipulation. This includes ensuring the AI behaves as intended, even when faced with unexpected situations or adversarial attacks.
Human Oversight
There should always be a human in the loop when it comes to critical decisions made by AI. This ensures that decisions can be reviewed and, if necessary, overridden by a human.
Public Participation
Stakeholders, including the public, should be involved in decision-making processes about AI regulation. This ensures that a wide range of perspectives are considered and that regulations align with societal values and expectations.
Continuous Monitoring
AI systems should be continuously monitored to ensure they are operating as intended and not causing harm. This includes regular audits and evaluations.
Ethical Considerations
AI systems should adhere to ethical guidelines, respecting human rights and dignity. This includes considerations like respect for autonomy, beneficence, non-maleficence, and justice.
Education and Training
There should be a focus on education and training to ensure that those working with AI understand the ethical, legal, and societal implications. This includes training in ethical AI design and use for developers, operators, and decision-makers.