Lulu Shi氏講演会
「The Future(s) of Unpaid Work: How Susceptible Do Experts from Different Backgrounds Think the Domestic Sphere Is to Automation?」

B’AIグローバル・フォーラムでは、英オックスフォード大学のLulu Shi氏お招きし、家事労働のような無報酬労働が自動化されることについてAI専門家たちはどのように考え、その未来をいかに予測しているのか、さらに、そうした予測が専門家のバックグラウンドや社会的要因に左右されうるのかなど、「無報酬労働の未来」についてご講演いただきます。ご関心のある方はぜひご参加ください。








東京大学Beyond AI研究推進機構 本郷拠点(本郷キャンパス附属病院内)







Lulu Shi(オックスフォード大学 社会学科 ポストドクトラルリサーチャー)

Dr. Lulu Shi is a sociologist and her research spans technology, education, work and employment and organisations. She works on the project DomesticAI as a postdoctoral research fellow. In this project she focuses on the transformation of paid and unpaid work in the age of AI and robotics. With her team she designs a cross-national harmonised factorial survey experiment.

She also leads a project funded by the British Academy, which investigates how educational technology (EdTech) transforms education. Specifically, the project studies the role of EdTech firms—who can be seen as the architects behind the technology—in shaping education by considering the socio-political contexts they are embedded in.




The Future(s) of Unpaid Work:

How Susceptible Do Experts from Different Backgrounds Think the Domestic Sphere Is to Automation?


The future of work has emerged as a prominent topic for research and policy debate. However, the debate has focused entirely on paid work, even though people in industrialized countries on average spend comparable amounts of time on unpaid work. The objectives of this study are therefore (1) to expand the future of work debate to unpaid domestic work and (2) to critique the main methodology used in previous studies. To these ends, we conducted a forecasting exercise in which 65 AI experts from the UK and Japan estimated how automatable are 17 housework and care work tasks. Unlike previous studies, we paid attention to how experts’ diverse backgrounds may shape their estimates. On average our experts predicted that 39 percent of the time spent on a domestic task will be automatable within ten years. Japanese male experts were notably pessimistic about the potentials of domestic automation, a result we interpret through gender disparities in the Japanese household. Our contributions are to provide the first quantitative estimates concerning the future of unpaid work and to demonstrate how such predictions are socially contingent, with implications to forecasting methodology.



久野愛(東京大学大学院情報学環・学際情報学府 准教授)



東京大学Beyond AI研究推進機構B’AIグローバル・フォーラム



東京大学Beyond AI研究推進機構