Artificial Intelligence (AI) for Social Sciences and Humanities
This special session is organised and supported by the following educational partner
College of Arts, University of Baghdad, UK
Session Chair:
- Prof. Dr. Ali Abdulameer Sajit, Dean of the College of Arts, University of Baghdad, Iraq.
Session Co-Chair:
- Assist. Prof. Dr. Khalid Hantoosh Sajit, University of Baghdad, Iraq.
Scientific Committee:
- Prof. Khamis Daham Muslih (Ph.D.)
- Assist Prof. Anfal Saeed (Ph.D.)
- Prof. Anmar Abdulilah (Ph.D.)
- Prof. Thaer Ahmad Hason (Ph.D.)
Synopsis:
AI has made significant contributions to the field of social sciences, revolutionising research and analysis in various ways.
It is important to note that while AI can provide valuable insights and tools for social scientists, it does not replace the need for human expertise and critical thinking. Social scientists play a crucial role in interpreting AI-generated results, contextualizing findings, and ensuring that the ethical implications of AI are carefully considered in their research.
There are different areas with social sciences and humanities that have been benefited from this development including:
Data Analysis and Prediction: AI algorithms can analyse large volumes of social data, such as social media posts, surveys, and government records, to identify patterns, trends, and correlations. This helps social scientists gain insights into human behavior, public opinion, and societal trends. AI-based predictive models can also forecast social phenomena, such as election outcomes or disease spread, based on historical data.
Natural Language Processing (NLP): NLP techniques enable AI systems to understand and interpret human language, including written text and spoken words. Social scientists can use NLP to analyse textual data, such as interviews, articles, and social media posts, to uncover themes, sentiment, and discourse patterns. NLP also facilitates automated content analysis, topic modelling, and sentiment analysis.
Social Network Analysis (SNA): AI algorithms can analyse social network data to understand the structure, dynamics, and influence within social systems. SNA helps social scientists study social relationships, information diffusion, and social influence processes. It can identify key actors, communities, and information hubs in a network, providing insights into social interactions and behaviour.
Recommender Systems: AI-powered recommender systems are widely used in social sciences to personalize and recommend relevant content to individuals. In research, recommender systems can suggest relevant articles, papers, or research materials based on a user’s interests, reading history, or social network connections. This enhances information discovery and access for social scientists.
Ethical Considerations: AI raises important ethical considerations in the social sciences. Issues such as privacy, bias, fairness, and transparency need to be carefully addressed when collecting, analyzing, and interpreting social data using AI. Researchers must ensure that AI systems are designed and used in a responsible and accountable manner, taking into account the potential social implications and unintended consequences of AI-driven research.