AI and the transformation of social science research

Key Points

• To outline the fundamental requirements of bias management and data fidelity in the adoption of artificial intelligence for social science research.
• Conceptual analysis of artificial intelligence integration across social science research methodologies.
• Evaluation of frameworks for maintaining data integrity and mitigating algorithmic and sampling bias.
• Rigorous management of bias is identified as an essential prerequisite for valid social science inferences.
• Ensuring high data fidelity is established as critical for maintaining reliability and reproducibility in computational research.