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).
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)
A few months ago, two Uber drivers from the United Kingdom, Azeem Hanif and Alfie Wellcoat filed a case against Uber alleging discrimination by its algorithm. They brought the case in a District Court in Amsterdam, where Uber’s headquarters is located. One of their central claims is that Uber’s algorithmic interference determines the nature of their rides: which drivers get the short ride or the nice ride, and the other way round. The automated decision-making process lacks transparency and is based on arbitrary factors, they allege, and drivers are in the dark about how the AI decides these questions.
In the last few posts, we posed some questions for algorithms as an artefact for governance, including the implications of different forms of algorithms embedded in computing and information infrastructure, their relationships with governing and administration, and their relevance for specific legal domains. Here, we share some readings to critically study, understand and critique algorithmic systems are particularly relevant for lawyers.
Image Source: Wikimedia commons; Image only for representation purpose In the last post,…
