Technological and economic forces are radically restructuring our ecosystem of knowledge, and opening our information space increasingly to forms of digital disruption and manipulation that are scalable, difficult to detect, and corrosive of the trust upon which vigorous scholarship and liberal democratic practice depend. Using an illustrative case from the people’s republic of china, this talk shows how a determined actor can exploit those vulnerabilities to tamper dynamically with the historical record. It furthermore demonstrates that machine learning models can now accurately reproduce the choices made by human censors, and warns that we are on the cusp of a new, algorithmic paradigm of information control and censorship that poses an existential threat to the foundations of all empirically-grounded disciplines. At a time of ascendant illiberalism around the world, robust, collective safeguards are urgently required to defend the integrity of our source base, and the knowledge we derive from it.

Glenn Tiffert is a Visiting Fellow at the Hoover Institution. Tiffert earned his Ph.D. in History from the University of California, Berkeley. From 2015-2017, he was the Distinguished Postdoctoral Fellow in Residence at the Lieberthal-Rogel Center for Chinese Studies at the University of Michigan, Ann Arbor, where he also held faculty appointments in the History Department and Asian Languages & Cultures Department, and taught undergraduate and graduate courses on modern China. He has taught at Berkeley, Harvard, and UCLA, and currently serves on the Projects and Proposals Committee of the American Society for Legal History.

For further information, contact Minghui Hu: mhu@ucsc.edu