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)
CLPR represented the intervenor Vimochana in the Supreme Court and challenged the constitutionality of the offence of adultery under Section 497 of the IPC. We argued against adultery as an offence by invoking the fundamental right to privacy and argued that the right to intimate association is a facet of privacy which is protected under the Constitution. The Supreme Court unanimously struck down Section 497 of the Indian Penal Code as being violative of Articles 14. 15 & 21 of the Constitution.
CLPR has endorsed a set of international principles against unchecked surveillance. The 13 Principles set out for the first time an evaluative framework for assessing surveillance practices in the context of international human rights obligations.
The Central Monitoring System (CMS) project of India, which was designed to allow the government…