A university research team is developing a study on the impact of social media usage on mental health, a growing concern in today’s digital age. The team —comprising social scientists, psychologists, and data analysts — has been tasked with designing a research framework that will allow for an in-depth understanding of the relationship between time spent on social media and the mental well-being of young adults. Given the vast amount of available data and the complexity of the topic, they decide to incorporate artificial intelligence (AI) to streamline their research design process.
The AI system they choose is capable of conducting literature reviews, identifying research gaps, suggesting potential hypotheses, and even recommending appropriate methodologies. The appeal of this tool lies in its ability to process vast amounts of academic papers and data points in a fraction of the time that a human researcher could, potentially offering fresh insights that would otherwise go unnoticed. The team envisions that AI will not only make their work more efficient but also contribute to a more objective and data-driven research design.
However, as the AI begins to play an increasingly central role in shaping the study, the researchers face unexpected challenges, particularly around the issues of transparency. The AI generates a set of hypotheses that, on the surface, seem plausible and supported by existing literature. Yet, when team members question how the AI arrived at these suggestions, they struggle to find clear explanations. The AI’s algorithm is proprietary and operates as a “black box,” making it difficult for the researchers to fully understand or verify its internal logic.
As the study progresses, the AI recommends a methodology that excludes certain demographic groups due to data limitations. While this makes the study more manageable, it could introduce bias by underrepresenting key populations. Since AIs have been trained on primarily Western texts, some researchers are particularly concerned about this. But others on the team are less concerned, since they know the AI developer is aware of these issues and has built the system to avoid such bias.
One thing is sure. Using the AI as a research assistant has certainly made the team more productive. They are far ahead of where they had been in previous studies and have uncovered some really interesting trends using research designs they had not considered. But the team still has some open questions and is not sure how to proceed.
— Discussion Questions —
Is it okay for the team to use the AI research assistant? Are there things the team needs to change about how the AI’s work is incorporated into the project?
Who is ultimately responsible for any bias that the AI introduces into the project: the research team or the AI developer?
What makes “black box” AI concerning? Why not trust that the AI is simply better at synthesizing these large amounts of data and making connections that humans can’t?