The Futures of Doing Research In the Social Sciences In the Era of Generative Artificial Intelligence (GenAI): What Can We Do? What Should We Do?

Generative Artificial Intelligence (GenAI) has changed and continues to change how research in the social sciences is conducted and published. In empirical research, GenAI has affected established practices from hypotheses generation to data collection, data analysis, model building, and interpretation. Whereas some changes seem minor, others, such as the use of GenAI in inductive coding, pose unresolved questions of reliability and validity. For writing, text-generating tools such as ChatGPT have raised wide-ranging questions regarding originality, authorship, and slop. GenAI is affecting also the review and editing process, for example, when “AI Assistants” help journal editors and publishers with copyediting, index creation, and more.

Many developments seem rapid, with researchers struggling to keep up. Hopes, promises and opportunities go hand in hand with uncertainties, risks, and fears. Many challenges continue to remain unresolved while the stream of new challenges seems to grow. This session aims to address the challenges related to GenAI in the social science research process. What innovations of GenAI uses for research are on the horizon? Is our research and writing still our “own”? How can or should we (re-)organize our research, writing, editing, and teaching practices? How does the rise of GenAI change the scientific profession, its self-image, and standing in society? What should be done about alienation of intellectual work from the scientific process? What kind of new policies and collective interventions do we need? What can academic organizations do to meet the challenges? What methodological innovations shall we embrace, avoid, and seek to develop?