(I'm looking to research historical examples with implications for AI Risk specifically. If you want think this could be useful to you, consider filling out this form)

TLDR; This post announces the trial period of Rhyme, a history consultancy for longtermists. It seems like longtermists can profit from historical insights and the distillation of the current state of historical literature on a particular question, both because of its use as an intuition pump and for information about the historical context of their work. So, if you work on an AI Governance project (research or policy) and are interested in augmenting it with a historical perspective, consider registering your interest and the cruxes of your research here.  During this trial period of three to six months, the service is free. 

"History doesn’t repeat, but it rhymes." - Mark Twain

What Problem is Rhyme trying to solve? 

When we try to answer a complicated question like “how would a Chinese regime change influence the international AI Landscape”, it can be hard to know where to start. We need to come up with a hypothesis, a scenario. But what should we base this hypothesis, this scenario on? How would we know which hypotheses are most plausible? Game theoretical analysis provides one possible inspiration. But we don't just need to know what a rational actor would do, given particular incentives. We also need intuitions for how actors would act irrationally, given specific circumstances.

• Would we have thought of considering the influence of close familial ties between European leaders when trying to predict the beginning of the first world war? (Clark, 2014)
• Would we have considered Lyndon B. Johnson's training as a tutor for disadvantaged children as a student when trying to predict his success in convincing congresspeople effectively? (Caro, 1982)
• Would we have considered the Merino-Wool-Business of a certain diplomat from Geneva when trying to predict whether Switzerland would be annexed by its neighbouring empires in 1815? (E. Pictet, 1892).

In summary: A lot of pivotal actions and developments depend on circumstances we wouldn’t expect them to. Not because we’d think them to be implausible, but because we wouldn’t think of considering them. We need inspiration and orientation in this huge space of possible hypotheses to avoid missing out on the ones which are actually true.

In an ideal world, AI governance researchers would know about a vast amount of historical literature that is written with enough detail to analyse important decisions, as well as multiple biographies of the same people, so they see where scholars currently disagree. This strategy has two main problems: Firstly, the counterfactual impact these people could have with their time is potentially very big. Secondly, detailed historical literature (which is, often, biographies or primary sources) tends to be written for entertainment, among other things. Biographers have an interest in highlighting maybe irrelevant, but spicy details about romantic relationships, quirky fun jokes told by the person or the etymological origins of the name of a friend. This makes biographies longer than they’d need to be for the goal of analysing the relevant factors in pivotal decisions of a particular person. It takes training to filter through this information to find the actually important stuff. Skills that require training are more efficiently done when a part of an ecosystem specializes in them. Rhyme is an attempt at this specialization.
 

Who could actually use this? 

The following examples should illustrate who could use this service: 

• Alice is writing a report on the possibilities for the state of California to regulate possible AI uses. They wonder how big the influence of the Governor's advisors was in past regulation attempts of other technologies.
• Bob wants a brief history of the EU’s regulation of general-purpose technologies to have more context on the norms to follow. 
• Connor is trying to forecast how the American public might react to a weaponized AI used in combat against civilians. 
• Dora is trying to convince an Austrian and a German diplomat to cooperate on a specific piece of legislation and look at past successful strategies. 
• Edith is trying to forecast how the Chinese government might try to import talent to make faster progress on building better chip factories. They are looking for past cases where the Chinese government looked for talent and tacit knowledge in other countries in the past. 
   

Where is this going? 

Depends on the feedback! I’d first like to test this in the field of AI Governance because of my own familiarity and the urgency of the problem. If there turns out to be enough of a demand for the service this project provides, I stay passionate about doing it and there is no fundamental flaw in the concept, I’d be happy to continue this service or scale it to other areas of the longtermist landscape. (E.g. Biosecurity, nuclear security community building, cause-specific outreach). 
 

About me: 

I am a recent graduate in history and philosophy, having done several independent research projects in history over the last 7 years (Some of them academic papers, most of them side projects). I mostly focused on the history of technology after 1800, but also trained in medieval and renaissance political history and its methodology. I care about preventing anthropogenic existential risks and improving democratic decisions. This is my website if you would like to know more about me personally.

Questions? Contact me here: thurnherr.lara@gmail.com or comment on this post. 

Sources

​​Caro, Robert A.: The Years of Lyndon Johnson: The Path to Power, New York 1982. P. 315.

Clark, Christopher: The Sleepwalkers: How Europe Went to War in 1914, 2014. Pp. 50.

Pictet, E.: Biographie, travaux et correspondance diplomatique de Charles Pictet de Rochemont , 1892. P.345. 

Thanks to Violet Buxton-Walsh, Elia Heer, Michel Justen and Hannes Thurnherr for feedback and support.