Protecting Privacy and Ethics in AI-driven Conversational Systems using Laplace Mechanism

Abstract

AI-powered analytics and conversational systems designed for data mining and other manipulative tasks raise concerns about who can access data and how to utilize it. This data progression has brought forth significant ethical considerations concerning privacy and ethics. AI-powered conversational agents, such as chatbots and virtual assistants, collect and process vast amounts of personal data to enhance user experience and provide tailored responses. Many industries have been transformed by artificial intelligence (AI)-based analytics systems, and some have unthinkable analytical data in terms of better decision-making, highly personalized customer experiences, and improved operational efficiency. While conversational AI offers convenience for users, this technology is also associated with privacy and data protection threats. This research seeks to understand the ethical issues with AI-driven analytics, focusing on data privacy and ethics in verbal and written interactions. This work will look at the current state and potential threats in depth and demonstrate the implementation of differential privacy using the Laplace mechanism in the query output so that the document has no special meaning and does not distort the released results significantly.

Keywords

• Algorithms
• Analytics
• Artificial Intelligence
• Data Privacy

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