Yes. We can help you assess data readiness for artificial intelligence (AI) using six key pillars outlined by Hiniduma et al. (2025):
- Data Quality
- Is the data accurate, complete, and consistent?
- Understanding and Usability of Data
- Is the data well-documented, with clear metadata, definitions, and accessibility?
- Data Structure and Data Organization
- Is the dataset logically structured, with appropriate formats, and divided properly for training, validation, and testing?
- Data Governance
- Are privacy risks minimized? Is sensitive information protected? Are ethical and legal standards met?
- Impact of Data on AI
- Do individual data points or features meaningfully influence the model? Are the most relevant features selected?
- Fair and Unbiased Data
- Does the dataset avoid discriminatory patterns, representation bias, or imbalances that harm minority classes?
Contact the Digital Scholarship and Publishing team to ask a question, set up a consultation, or learn more about the library's research data support services.
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