Beginning with a sense of discomfort at the authoritarian remark, “This must be right because Professor ○○ says so,” this essay explores the definition of scholarship, research ethics, political neutrality, and the potential of AI-based document analysis.
It is a provocative piece that seeks to bring scientific methods into the humanities and make visible the preconceptions and political biases embedded in academia.

2019-04-13
I still vividly remember being astonished by a certain remark I encountered there.
“This must be right because Professor ○○ says so.”
It was the moment when I felt disillusioned with authoritarianism.

What follows is a continuation of the previous chapter.

Striking back at academic preconceptions with artificial intelligence.

Many of the results obtained in this survey accord with my own empirical observations.
As an undergraduate, the author studied biochemistry in the Faculty of Science, and in graduate school worked on research related to artificial intelligence in a Graduate School of Engineering, and from that time he had a strong interest in the theory of scholarship and the philosophy of science.
Accordingly, he read many books on the philosophy of science and also took part in study groups in related laboratories, and I still vividly remember being astonished by a certain remark I encountered there.

“This must be right because Professor ○○ says so.”

It was the moment when I felt disillusioned with authoritarianism.
Such a remark would be unthinkable in a science or engineering laboratory.
But that incident was only the prologue.

After taking up a teaching position at a university, I came to realize that there exists a kind of “scholarship” that operates on values entirely different from the education we had received.
In the natural sciences, one is rigorously trained to interpret experimental results while excluding subjectivity as much as possible.
Naturally, it is out of the question to tamper with experimental results for political reasons or to distort their interpretation.
That is precisely why scholarship must remain independent from politics.
And yet, within the humanities, I noticed that there was a proliferation of “scholarship” that, from the standpoint of someone in the sciences, made it impossible to tell whether one was engaging in scholarship or in politics.

Though all of them call themselves scholarship, activities based on entirely different norms are being carried out.
To resolve this confusion, one must begin by defining scholarship itself.
That is why I wrote What Is Scholarship? (Daigaku Kyoiku Shuppan) in 2005.
In that book, I argued that disciplines bearing the name of “science,” such as the humanities, social sciences, and natural sciences, must satisfy the requirement of being “systematic knowledge possessing the power to predict.”
A discipline that conveniently manipulates experimental results naturally cannot possess predictive power.

In recent years, research ethics have come under stricter scrutiny, prompted by the misconduct scandal involving Ms. Obokata, but the other day, at a symposium on promoting education in research ethics, I again encountered a shocking scene.
A man from a medical background who had served for many years as president of a national university made the following remark from the stage as he discussed the background to research misconduct.

“When people fall into groupthink, misconduct occurs.
It is just like the Liberal Democratic Party.”

When I think that a person who cannot perceive the inappropriateness of making such a political remark in a setting like this stands at the center of research ethics education, I cannot help feeling utterly disheartened.
Of course, it would be another matter if he were saying this on the basis of objective evidence that the Liberal Democratic Party is more prone to groupthink than other parties.
But in my own research, at least, I have obtained results showing the opposite.

For about ten years, our research group has been conducting document analysis using information engineering, in order to bring scientific methods into the humanities.
Generally, this field is called big data, data mining, or text mining.
Much of this research assumes business applications, but I have worked chiefly on the analysis of political documents, such as the minutes of the National Diet.
As one part of that work, we created an artificial intelligence system that identifies from which party a statement made in the Diet came. “2”
To be continued.