Accessibility: Ensuring that AI tools are accessible to researchers with varying technical backgrounds.
Bias awareness: Be mindful of potential biases embedded within AI algorithms and training data, which can influence analysis results and affect proper data representation and interpretation.
Data quality: Training AI models requires high-quality, well-annotated data which can be challenging to acquire in humanities research.
Ethical implications: Consider the ethical implications of using AI to analyze sensitive cultural data, including privacy concerns.
Human expertise needed: While AI can analyze large datasets, critical interpretation and contextualization still require human expertise in the humanities field.
Interpretability: Understanding how AI models arrive at their conclusions is crucial for validating research findings.