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Machine Learning and Artificial Intelligence
Artificial intelligence and big data present new challenges to protecting our right to privacy both online and offline. Predictive algorithms and online social networks add complex nuances to the idea of informed consent. A global, cross-disciplinary effort is underway to make predictive algorithms fair, accountable, and transparent as these algorithms come to mediate more and more the areas of civic life and public discourse. But these tools and ideas operate inside of existing socio-technical systems that need to balance conflicting goals and tensions.
Machine learning, artificial intelligence, and big data have become such buzzwords, that the prevailing wisdom is that data is the new fuel, and “datafication” is the panacea for most problems. Artificial intelligence (AI) is a broad term that refers to the science and technology of building machines with intelligence at par with that of humans. One branch of AI that has become ubiquitous of late is machine learning, a branch of computer science that deals with creating algorithms that learn to generalise by inferring patterns from data. The popularity and widespread use of machine learning algorithms have been driven by large volumes of data (often referred to as big data) that is generated and collected by companies via their interaction with their consumers online, and increasingly, offline too. Companies apply machine learning algorithms on text, image, voice, and transactional data to predict consumer behaviour, show targeted ads, recommend content, products and services, and manage social networks.
Recently, the Supreme Court of India asserted that privacy is a fundamental right in a landmark judgment (K Puttaswamy v Union of India 2017). But how do we implement privacy in practice in software, and how is the idea of privacy mutating in the landscape of socio-technical systems powered by big data and machine learning algorithms?
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