In the world of artificial intelligence, one of the biggest challenges is developing AI systems that are not only intelligent, but also act according to ethical standards and values that match those of humans. One approach to this is to train AI using law books and case law as a basis. This article explores this method and looks at additional strategies to create an AI with human-like norms and values. I also made this suggestion on behalf of the Dutch AI coalition to the Ministry of J&V in a strategy paper that we wrote on behalf of the ministry.
The Basic Approach: Legislation as a Foundation
The idea of training an AI based on law books and case law is based on the concept that laws are a reflection of the collective norms and values within a society. By having an AI analyze these legal texts, the system can gain insight into what is socially acceptable and what behavior is prohibited.
Using GANs to Identify Gaps
Generative Adversarial Networks (GANs) can serve as an instrument to discover gaps in legislation. By generating scenarios that fall outside existing laws, GANs can reveal potential ethical dilemmas or unaddressed situations. This allows developers to identify and address these gaps, giving the AI a more complete ethical data set to learn from.
Capabilities and Limitations of this Approach
While law training provides a solid starting point, there are some important considerations:
- Limited View of Norms and Values Laws do not cover all aspects of human ethics. Many norms and values are culturally determined and not recorded in official documents. An AI trained solely on legislation may miss these subtle but crucial aspects.
- Interpretation and Context Legal texts are often complex and subject to interpretation. Without the human ability to understand context, an AI may struggle to apply laws to specific situations in a way that is ethically sound.
- Dynamic Nature of Ethics Social norms and values continuously evolve. What is acceptable today may be considered unethical tomorrow. So an AI must be flexible and adaptable to deal with these changes.
- Ethics vs. Legality It is important to recognize that not everything that is legal is ethically correct, and vice versa. An AI must have the ability to see beyond the letter of the law and understand the spirit of ethical principles.
Complementary Strategies for Human Norms and Values in AI
To develop an AI that truly resonates with human ethics, a more holistic approach is needed.
1. Integration of Cultural and Social Data
By exposing the AI to literature, philosophy, art and history, the system can gain a deeper understanding of the human condition and the complexity of ethical issues.
2. Human Interaction and Feedback
Involving experts from ethics, psychology and sociology in the training process can help refine the AI. Human feedback can provide nuance and correct where the system falls short.
3. Continuous Learning and Adaptation
AI systems must be designed to learn from new information and adapt to changing norms and values. This requires an infrastructure that allows for constant updates and retraining.
4. Transparency and Explainability
It is crucial that AI decisions are transparent and explainable. This not only facilitates user trust, but also allows developers to evaluate ethical considerations and adjust the system where necessary.
Conclusion
Training an AI based on law books and case law is a valuable step towards developing systems with an understanding of human norms and values. However, to create an AI that truly acts ethically in a manner comparable to humans, a multidisciplinary approach is needed. By combining legislation with cultural, social and ethical insights, and by integrating human expertise into the training process, we can develop AI systems that are not only intelligent, but also wise and empathetic.
Additional Resources:
- Ethical principles and (non-)existing legal rules for AI : This article discusses the ethical requirements that AI systems must meet to be reliable. Data and Society
- AI Governance Explained : An overview of how AI governance can contribute to the ethical and responsible implementation of AI within organizations. Personal training
- The three pillars of responsible AI: how to comply with the European AI law : This article covers the core principles of ethical AI applications under the new European law. Emerce
- Training Ethically Responsible AI Researchers: a Case Study : An academic study on training AI researchers with a focus on ethical responsibility. ArXiv
