Many decision-making scenarios in modern life benefit from the decision
support of artificial intelligence algorithms, which focus on a data-driven
philosophy and automated programs or systems. However, crucial decision issues
related to security, fairness, and privacy should consider more human knowledge
and principles to supervise such AI algorithms to reach more proper solutions
and to benefit society more effectively. In this work, we extract
knowledge-based logic that defines risky driving formats learned from public
transportation accident datasets, which haven’t been analyzed in detail to the
best of our knowledge. More importantly, this knowledge is critical for
recognizing traffic hazards and could supervise and improve AI models in
safety-critical systems. Then we use automated verification methods to verify
the robustness of such logic. More specifically, we gather 72 accident datasets
from and organize them by state. Further, we train Decision Tree and
XGBoost models on each state’s dataset, deriving accident judgment logic.
Finally, we deploy robustness verification on these tree-based models under
multiple parameter combinations.

AI Algorithms and Decision-Making

In today’s society, artificial intelligence (AI) algorithms have become an integral part of decision-making processes. These algorithms, driven by data and automation, provide decision support in a wide range of scenarios. However, it is crucial to consider the multidisciplinary nature of decision issues and incorporate human knowledge and principles to ensure the security, fairness, and privacy of these decisions.

This article presents a unique approach to decision-making by combining AI algorithms with human knowledge. The authors focus on the specific domain of risky driving formats learned from public transportation accident datasets. This domain has not been extensively analyzed, making it an ideal candidate for extracting valuable knowledge to improve AI models.

Integrating Human Knowledge into AI Algorithms

By extracting logic from accident datasets, the authors aim to identify risky driving formats that can contribute to recognizing traffic hazards. This knowledge-based approach serves as a critical supervisory mechanism for AI models in safety-critical systems. By infusing human expertise into the decision-making process, the authors aim to enhance the effectiveness of AI algorithms in benefiting society.

Furthermore, the article introduces automated verification methods to ensure the robustness of the extracted logic. This verification process is conducted on Decision Tree and XGBoost models trained on each state’s accident dataset. By deploying robustness verification under multiple parameter combinations, the authors seek to validate the reliability and accuracy of their findings.

Implications and Future Directions

The multidisciplinary nature of this research becomes evident through the integration of various components. The combination of AI algorithms, public transportation accident datasets, human knowledge, and automated verification methods creates a comprehensive framework for addressing crucial decision issues pertaining to safety and risk assessment.

This work has significant implications for improving the performance of AI models in safety-critical domains. The incorporation of human knowledge adds an additional layer of supervision, ensuring the fairness and privacy of decisions. Moreover, the robustness verification process strengthens the reliability of the extracted logic, enabling more accurate hazard recognition.

As for future directions, this research could serve as a foundation for developing advanced AI algorithms that adapt and learn from continuously updated accident datasets. Additionally, exploring the integration of other relevant datasets, such as traffic congestion or road infrastructure, could further enhance decision support systems. The possibilities for incorporating multi-disciplinary concepts are vast, and continued research in this area could shape the future of decision-making in various domains.


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