Divij Joshi


Divij Joshi is an independent lawyer and researcher. He graduated with a B.A., LL.B.(Hons.) degree from the National Law School of India University, Bangalore, in 2016. Subsequently, he joined a litigation practice in Bombay, and then as a research fellow, conducting legal and policy research on issues of technology, urban governance, and environmental laws.


He is presently a Mozilla Technology Policy Fellow, where he studies technology policy in India, with a focus on automated decision-making technologies. He is interested in studying the intersection of technology, law and political economy.


Too Big to Regulate: Competition Law and the Structure of Information Markets

October 22, 2020

The US Department of Justice recently sued the tech giant Google, claiming that Google abused a dominant position in the search engine and advertisement market, in a way which systematically harmed competition and negatively affected consumers. This is a major development given the historical reticence of the US anti-trust regime to curtail monopoly power in the digital age. This post briefly explains the links between the market structures of the digital economy and legal regulation.

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Privacy Theory 101: Must Reads

October 16, 2020

In the last few blogs, we discussed some fundamentals of privacy theory – spanning the historical origins of the ‘right to privacy’ in legal jurisprudence in the USA, to contemporary scholarship delving into the implications of data-driven and machine learning environments for our understandings of privacy. This week, we list out some critical scholarship on the theoretical foundations of privacy, and its relationship with regulatory practice in India (apart from the readings already listed).

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Privacy Theory 101: Profiling, Prediction, and Hildebrandt’s Theory of Privacy as Protection of the Incomputable Self

October 9, 2020

In this series of blogs, we have been exploring different conceptual and theoretical approaches to information privacy. In the last post, we explored an influential, historical argument by Warren and Brandeis in their paper on the ‘Right to Privacy’, written in a time when anxieties about photographic and print technologies were prevalent. In this post, we examine some of the anxieties and concerns that contemporary data science methods and technologies like machine learning pose to privacy, and theoretical responses to these anxieties in Mireille Hildebrandt’s 2019 paper, ‘Privacy as Protection of the Incomputable Self: From Agnostic to Agonistic Machine Learning’. (Theoretical Inquiries in Law, 20, 83 – 121)

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