Ethical and preventive legal technology
Abstract Preventive Legal Technology (PLT) is a new field of Artificial Intelligence (AI) investigating the intelligent prevention of disputes. The concept integrates the theories of preventive law and legal technology. Our goal is to give ethics a place in the new technology. By explaining the decisions of PLT, we aim to achieve a higher degree of trustworthiness because explicit explanations are expected to improve the level of transparency and accountability. Trustworthiness is an urgent topic in the discussion on doing AI research ethically and accounting for the regulations. For this purpose, we examine the limitations of rule-based explainability for PLT. Hence, our Problem Statement reads: to what extent is it possible to develop an explainable and trustworthy Preventive Legal Technology? After an insightful literature review, we focus on case studies with applications. The results describe (1) the effectivity of PLT and (2) its responsibility. The discussion is challe
Abstract Preventive Legal Technology (PLT) is a new field of Artificial Intelligence (AI) investigating the intelligent prevention of disputes. The concept integrates the theories of preventive law and legal technology. Our goal is to give ethics a place in the new technology. By explaining the decisions of PLT, we aim to achieve a higher degree of trustworthiness because explicit explanations are expected to improve the level of transparency and accountability. Trustworthiness is an urgent topic in the discussion on doing AI research ethically and accounting for the regulations. For this purpose, we examine the limitations of rule-based explainability for PLT. Hence, our Problem Statement reads: to what extent is it possible to develop an explainable and trustworthy Preventive Legal Technology? After an insightful literature review, we focus on case studies with applications. The results describe (1) the effectivity of PLT and (2) its responsibility. The discussion is challenging and multivariate, investigating deeply the relevance of PLT for LegalTech applications in light of the development of the AI Act (currently still in its final phase of process) and the work of the High-Level Expert Group (HLEG) on AI. On the ethical side, explaining AI decisions for small PLT domains is clearly possible, with direct effects on trustworthiness due to increased transparency and accountability.
Executive Summary
The article explores the emerging field of Preventive Legal Technology (PLT), which combines Artificial Intelligence (AI) with preventive law to enhance dispute prevention. The authors emphasize the importance of ethical considerations in AI development, focusing on transparency and accountability to build trustworthiness. The study examines the limitations of rule-based explainability in PLT and investigates its effectiveness and responsibility through case studies. The discussion is framed within the context of the AI Act and the High-Level Expert Group (HLEG) on AI, highlighting the ethical feasibility of explaining AI decisions in small PLT domains to improve trustworthiness.
Key Points
- ▸ Preventive Legal Technology (PLT) integrates AI with preventive law to enhance dispute prevention.
- ▸ Ethical considerations, particularly transparency and accountability, are crucial for trustworthiness in PLT.
- ▸ The study examines the limitations of rule-based explainability in PLT and its effectiveness and responsibility through case studies.
- ▸ The discussion is contextualized within the AI Act and the work of the HLEG on AI.
- ▸ Explaining AI decisions in small PLT domains is ethically feasible and improves trustworthiness.
Merits
Comprehensive Approach
The article provides a thorough exploration of PLT, integrating ethical considerations with technological advancements, which is essential for developing trustworthy AI systems.
Practical Insights
The use of case studies to demonstrate the effectiveness and responsibility of PLT offers practical insights that can guide future implementations.
Timely Relevance
The discussion within the context of the AI Act and HLEG on AI ensures the relevance and applicability of the findings to current regulatory frameworks.
Demerits
Limited Scope
The focus on small PLT domains may limit the generalizability of the findings to larger, more complex legal domains.
Ethical Challenges
While the article highlights the ethical feasibility of explaining AI decisions, it does not fully address the potential ethical dilemmas that may arise in more complex scenarios.
Regulatory Uncertainty
The AI Act is still in its final phase, which may lead to uncertainties in the applicability of the findings once the regulations are fully implemented.
Expert Commentary
The article makes a significant contribution to the field of Preventive Legal Technology by integrating ethical considerations with technological advancements. The emphasis on transparency and accountability is particularly noteworthy, as these are critical for building trust in AI systems. The use of case studies provides practical insights that can guide future implementations, although the focus on small PLT domains may limit the generalizability of the findings. The discussion within the context of the AI Act and HLEG on AI ensures the relevance and applicability of the findings to current regulatory frameworks. However, the article could benefit from a more in-depth exploration of the ethical dilemmas that may arise in more complex scenarios. Overall, the study offers valuable insights that can inform both practical applications and policy developments in the field of AI and law.
Recommendations
- ✓ Expand the scope of research to include larger and more complex legal domains to enhance the generalizability of the findings.
- ✓ Conduct further studies on the ethical dilemmas that may arise in more complex PLT scenarios to provide a more comprehensive understanding of the ethical implications.
- ✓ Encourage collaboration between developers, practitioners, and policymakers to ensure that ethical considerations are integrated into the development and deployment of AI technologies.