Artificial Intelligence (AI) researchers are deploying Machine Learning (ML) algorithms to assist in diverse tasks such as diagnosing medical conditions, driving cars, and screening job candidates. Such deployment of applications is giving rise to new ethical and social issues. Social scientists are trying to think about the effect such deployments have on the people, economy, and society. On the other hand, are the engineers who are trying to handle the ethical and social dilemmas their inventions create.

AI in Society Raises New Issues

Society as a whole has been trying to deal with the philosophical and social consequences of technology. More recently, AI researchers are facing real-life predicaments that technology produces. Dashun Wang, an associate professor of management and organizations quotes an example where Amazon created ML tools to search for job candidates. As the software utilized data on past applicants to scout for the best-suited candidates for the company, a new problem emerged: as most previous applicants for the job were men, the program automatically fined candidates whose resumes mentioned the word “women’s”. In such a scenario, AI has magnified the already existing bias, states Wang – this Amazon program has been discontinued.

Artificial Intelligence is Getting Isolated

Just as AI is turning more relevant, it is also getting isolated. It is not very often that AI researchers engage with other disciplines such as philosophy, psychology, and economics that could help them address such ethical issues. 

To measure the interactions between AI and other disciplines, Wang and his team created a measure that captured how often papers in one discipline cited papers from another discipline. It was found that AI researchers today cite psychology, philosophy, and economics papers less than half of the percentage of what they used to. Similarly, psychology, philosophy, economics, and art hardly cite any AI topics. Not surprisingly, AI papers cite math and computer science the most.

The overall conclusion: “AI has become more and more cliquish,” Wang states. One possible explanation for this is that AI has just become tougher for social scientists to keep up with, due to the advances in complex AI research. Furthermore, the development of the Artificial Intelligence cloud could also help explain its isolation.

Bridging the Artificial Intelligence Divide

Despite the swift growth of AI, new technology will eventually fall short of its full capabilities if it does incorporate understandings from social science and other fields.

To bridge this gap, Wang recommends that universities encourage more collaborations between AI and other disciplines. For example, Northwestern University has incorporated a program termed CS+X, that actively fosters transformational relationships between computer science and other fields to create connections across the University and potentially enable new fields of study.

Some existing research also hints at how AI developers can efficiently combine findings from other fields. For instance, AI can probably study how self-driven cars can reflect human morality researched topics such as moral philosophy, psychology, economics, and also science fiction.


Just as computer scientists need to reach out to experts from other disciplines, social scientists also need to understand the developments in AI. As machines modify the way we live, it is vital that philosophers, economists, and psychologists stay up to date with the latest developments in computer science.