## 6 The Role of AI and Its Subsets, ML, and DL in Artifact Analysis
### AI: Discussion of Actual Cases and Limitations
\begin{table}
\begin{tabular}{p{113.8 pt} p{284.5 pt}} \hline \hline \multicolumn{2}{c}{**Limitations of Current AI Technologies**} \\ \hline Data Biases & AI systems learn from large datasets, but these datasets often contain biases reflecting historical inequalities, societal prejudices, or underrepresented groups. Such biases can lead to unfair or discriminatory outcomes, especially in sensitive applications like hiring, lending, or criminal justice. \\ \hline Interpretability & Many AI models, especially deep learning systems, operate as “black boxes,” making it difficult for humans to understand how they arrive at specific decisions. This lack of transparency hampers trust, accountability, and the ability to diagnose errors or biases within the system. \\ \hline Robustness & AI models can be fragile and susceptible to adversarial attacks or unexpected inputs, which can lead to incorrect outputs or system failures. Ensuring robustness is critical for deploying AI in safety-critical environments such as autonomous vehicles or medical diagnostics. \\ \hline Ethical Concerns & The deployment of AI raises numerous ethical issues, including privacy violations, surveillance concerns, consent, and the potential for misuse. Ensuring that AI adheres to ethical standards and respects human rights is essential for societal acceptance. \\ \hline \multicolumn{2}{p{113.8 pt}}{**Addressing Current Limitations**} \\ \hline To develop AI responsibly, it is vital to actively work on mitigating these limitations through techniques such as bias detection and correction, explainable AI (XAI), adversarial training, and establishing ethical guidelines and oversight mechanisms. \\ \hline \multicolumn{2}{p{113.8 pt}}{**Future Challenges and Planning**} \\ As AI continues to evolve, several emerging issues must be proactively addressed: \\ \hline Scalability & AI systems need to be scalable to handle increasing data volumes and complexity without sacrificing performance or interpretability. Developing efficient algorithms that can operate at large scales is essential. \\ \hline Privacy & Protecting individual privacy while utilizing vast datasets for training is a major concern. Techniques such as federated learning, differential privacy, and encrypted computation can help ensure data security and user confidentiality. \\ \hline Open-access Datasets & Creating and sharing open-access datasets for AI training promotes transparency, reproducibility, and democratization of AI development. Such datasets should be diverse, well annotated, and ethically sourced to prevent biases and ensure broad applicability. \\ \hline Spectral Libraries in Art and Cultural Heritage & Beyond conventional applications, AI-driven spectral libraries—collections of spectral data used in techniques like spectroscopy—are increasingly valuable in art conservation, authentication, and archaeology. They enable detailed analysis of artworks and artifacts, revealing hidden layers, materials, or provenance information. Developing comprehensive spectral libraries enhances our ability to preserve and understand cultural heritage through AI-assisted analysis. \\ \hline \hline \end{tabular}
\end{table} 
Table 24: Key Limitations and Future Directions for AI Technologies.