Evaluating Privacy Policies And Consent Mechanism In Artificial Intelligence (AI) Driven Applications: A User-Centric Perspective
Manoj Yadav, LL.B., School of Legal Studies, Vikrant University, Gwalior
Shital, LL.B., School of Legal Studies, Vikrant University, Gwalior
ABSTRACT
Machine learning is a computer science technique that allows computers to ‘learn’ on their own. It is often characterised as AI, but that is only one element of it. The characteristic that separates machine learning from other forms of AI is its dynamic ability to modify itself when exposed to more data. Through ingesting data, the machine is training itself by developing its own logic according to the data it has analysed.
There are two main types of machine learning: supervised and unsupervised. Supervised learning requires a human to provide both the data and the solution, letting the machine determine the connection between the two. Unsupervised learning allows the machine to learn more freely by ingesting a large amount of data (often big data) and iterating over it to find patterns and insights.
In the longer term, AI has the potential to go beyond merely enhancing established processes and alter government operations altogether.
Most AI that we experience today is ‘narrow’. This means that it has been deliberately programmed to be competent in one specific area. It is sometimes also referred to as augmented intelligence to highlight its ability to enhance (but not necessarily replace) human intelligence.
In Victoria and more broadly, information privacy law is generally based on the 1980 OECD Guidelines on the Protection of Privacy and Transborder Flows of Personal Data. These guidelines contain eight key principles that continue to be enshrined in privacy law around the world, including the Privacy and Data Protection Act 2014 (PDP Act).
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