Artificial intelligence (AI) is rapidly transforming industries, but its implementation raises important questions of fairness, transparency, and social impact. A new research paper from the Centre for Responsible AI at IIT Madras and the Vidhi Centre for Legal Policy explores how participatory approaches can address these concerns. The paper delves into two case studies: the use of facial recognition technology (FRT) in law enforcement and the application of large language models (LLMs) in healthcare. These case studies highlight both the potential benefits and risks of these technologies and emphasize how including diverse stakeholders in decision-making processes is crucial for responsible AI development. The traditional model of AI development, often driven by technologists and corporations, can lead to systems that perpetuate existing biases or create new forms of discrimination. This paper argues that by bringing together stakeholders like lawmakers, citizens, technology providers, and those directly impacted by AI systems (such as patients or individuals subject to surveillance), we can create AI that is not only more effective but also more equitable and trustworthy. For FRT in law enforcement, the research explores how public participation can help establish clear standards for accuracy and transparency, addressing concerns about misidentification and potential for discriminatory policing. In the case of LLMs in healthcare, the focus is on ensuring patient safety and data privacy while harnessing the power of AI for tasks like generating patient summaries and powering advisory chatbots. The paper proposes a "decision sieve" framework that guides the iterative process of gathering input from diverse stakeholders across different phases of AI development and deployment. This framework emphasizes the importance of horizontal and vertical translations to bridge the communication gaps between experts and the public. The research concludes that participatory approaches are not a panacea, but a vital step in building trust and ensuring AI benefits all members of society. While not eliminating the need to critically evaluate whether AI is the appropriate solution in the first place, participatory design creates a more inclusive and responsible process when AI deployment is deemed necessary. In India, where regulatory frameworks for AI are still evolving, this research offers valuable insights into how principles of responsible AI can be put into practice, aligning with the goals outlined in NITI Aayog's "Responsible AI for All" discussion paper and the ICMR's ethical guidelines for AI in healthcare.