Artificial Intelligence in Legal Practice: Implications for the Legal Profession’s Future

The phrase Artificial Intelligence (AI) has gained popularity more than 50 years after it was first used.Artificial intelligence (AI) technologies influence our consumption of social media content, criminal defendants' risk assessments before sentencing, people's creditworthiness, and even the best route to take when driving home from work.The majority of AI systems are trained using previous data, and they can identify trends, gain knowledge from instances, and forecast future results to aid in decision-making.These categorizations and forecasts are generalisations derived from big datasets that are beyond the pace and scale of human analysis.AI is thought to have such profound effects that it has been dubbed the "new electricity".The development and implementation of these technologies by governments and industry has brought attention to the sociological and ethical consequences.By concentrating on the three key phases of deploying machine learning, the most widely used subset of AI techniques, this article offers a framework for comprehending the consequences of AI: the data, model, and application stages.I propose that the social, ethical, and technical constraints of data-driven decision-making should be taken into serious account when developing AI policy, setting this against the backdrop of AI policy in India.In order to help with this, I also apply the framework to sector-specific issues seen in Indian policy-making procedures.To put it briefly, I concentrate on the risks and constraints that can result from data-driven decisions generally and specifically in the Indian context.First off, it provides an alternative to the way that policies are currently being implemented to address moral and social difficulties that are now categorised as "challenges to adoption".The current strategy is unproductive and short-sighted.AI applications function in cultures that are disorderly, prejudiced, uneven, and deeply ingrained with injustice and discrimination from the past.Treating these significant social realities as afterthoughts, extra features, or even as defects that need to be rectified implies that the foundation these institutions are constructed upon is intrinsically flimsy.I provide an outline for this approach and illustrate the social and ethical factors that need to be taken into account when developing AI systems and policies.It also encourages a multidisciplinary conversation about AI policy in India.Getting stakeholders and sectors to communicate using a common language and understanding has proven to be a basic issue in this area thus far.I try to express policy concerns in technical terms by putting forth this framework, and vice versa.Lastly, it broadens debates on artificial intelligence, ethics, law, and policy around the world, which are presently mostly based on Western contexts.The AI systems we use today are socio-technical systems that rely on the context in which they operate, much beyond being straightforward mathematical puzzles.For several reasons, India is a significant jurisdiction to take into account.It is a powerful force due to its immense size and developing AI sector.The Indian government's emphasis on developing technologies in the context of the digital economy means that in the coming years, AI policy will change and advance quickly.Aadhaar, the largest biometric identity project in the world, is located in the nation and, depending on how it is applied, may serve as the hub for AI applications there.Additionally, India is at a turning point in the development of data protection laws, which will significantly impact how AI technology may and will operate in India.The problem this article aims to solve is covered in the introduction, however the next section will go over its intended scope and provide definitions and conceptual clarifications for technical terms used in the article.Before guiding the reader through each step of the framework, it will first address the existing state of India's AI policy landscape in order to offer contextual information for understanding the proposed framework.Lastly, the sectors that are currently taken into account in India's policy-making processes are covered by this framework.The paper will conclude with some thoughts and conclusions.First off, it provides an alternative to the way that policies are currently being implemented to address moral and social difficulties that are now categorised as "challenges to adoption".The current strategy is unproductive and short-sighted.AI applications function in cultures that are disorderly, prejudiced, uneven, and deeply ingrained with injustice and discrimination from the past.