Abstract
The word “deepfake” is a portmanteau of “deep learning” and “fake” (Rouse,2020).It refers to a type of artificial intelligence (AI) technology that incorporates a machine learning technique called generative adversarial networks (GANs) (Rouse, 2020). GANs was first introduced in 2014 by Ian Goodfellow and other researchers at the University of Montreal . The idea is to use a pair of neural networks – one of which is called the “generator,” and the other, the “discriminator” – to synthesize artificial media or multimedia content that is indistinguishable from its authentic counterpart (Brownlee, 2019). One of the most striking features of this algorithmic architecture is its ability to use as little as one image of a person to create a video clip of that person saying or doing things they never said or did in real life1. In recent years, deepfake technology has earned its reputation as a threat to our already vulnerable information ecosystem (Schwartz, 2018). Until late 2017, the use of this machine learning technique was mostly confined to the area of AI research (Schwartz, 2018). It was only when a Reddit user who, under the moniker “Deepfakes, began posting digitally altered pornographic videos in which celebrities’ faces were super imposed on to the bodies of women in pornographic movies, that this technology became widely known in the public domain (Schwartz,2018).BythetimeRedditlaterbannedthepostinganddisseminationofdeepfakes from its platform, the creator of the videos had released “FakeApp,” an easy-to-use platform for making forged media. With the help of FakeApp, deepfake technology became widely knownandavailabletothepublic,resultinginadramaticincreaseinthenumberofindividuals who utilized this technology to generate and disseminate deepfakes online, mainly through social media platforms. In September 2019, the AI firm Deeptrace found approximately 15,000 deepfake videos online, 96% of which werepornographic.
Introduction
2.1 INTRODUCTION
The ability of the government to ensure efficient, effective, transparent, and responsive administration is essential to governance, which is widely defined as the “activity or manner of managing a state.” Given the size and diversity of India, governing it presents particularly difficult challenges. Government in India has always been constrained by slow, out-of-date procedures and bureaucratic obstacles, but recent efforts to integrate newer technologies are giving the system new life. To this end, there has been ongoing discussion in recent years on how to best employ AI to promote effective governance.
Three major trends came to light during the analysis made in this research. First, while interest in the idea of applying algorithms across all states has been high, technological capabilities and implementation vary widely. In adopting the use of algorithms in industries like education and agriculture, Andhra Pradesh and Karnataka appear to be more aggressive than other states. Second, the commercial sector, which collaborates with the government through partnerships or contracts, is responsible for developing the majority of the AI technology that is currently in use. And last, much of the technology that is at the center of discussions about AI and governance in India has already been put into practice in other nations, especially the United States, the United Kingdom, and China. Even if India might try to adopt some of this technology, it would be a good idea to first analyze some of the technological, legal, and ethical issues that have emerged in these nations and find ways to overcome them before implementing the technology in Indian administration. In order to chart the trajectory of technology development in India in the near future and make a regulatory model readily available after the technology is in use, this paper, unlike the other case studies, pays a significant lot of attention to uses of AI in other jurisdictions.