prasanth AI

The speaker discusses the moral ethics of artificial intelligence (AI) and machine ethic laws. They highlight the importance of ethical principles and values in the development and deployment of AI systems. The concept of machine ethic laws is crucial in managing AI conduct and establishing standards for human interaction. Privacy, bias, and accountability are identified as key ethical issues in AI. The need for regulations and collaboration between technologists, ethicists, and policymakers is emphasized to ensure a balance between technological innovation and moral integrity. Awareness campaigns and a normative framework for AI decision-making are also suggested. I am Prashant Nalakoditya, Cambridgeshire University, Subject Ethical Issues for AI. Professor Amir Afzal, the topic of podcast is on Moral Ethics of Artificial Intelligence and Machine Ethic Laws. With the advent of complex technologies and AI, issues concerning laws that govern machine ethics becomes increasingly clear. These ethical issues of artificial intelligence concern machines and human interactions, as well as the possibility of negativeness in community. Artificial intelligence has experienced an unmatched growth in the past decade and they have all seen it, felt its effects on their lives. As AI systems are increasingly complex and make automated decisions, they should meet ethical principles and values. In addition, the deployment of AI involves such vital issues in terms of accountability, transparency, bias, privacy and decision-making process. Moreover, the concept of machine ethics laws is vital in managing AI conduct and establishing standards for interpersonal relationships with people. The laws are designed to govern the manner in which AI should operate on a social level because there is something required when it misuses itself or hurts anyone. The aspect of machine ethics laws that are given great importance is because it touches on the issues of norms, technology, implementation. Furthermore, such laws are aimed to address both organizational aspects related with governing AI performance and ethical issues concerning morality and technology. With the advent of artificial intelligence systems that collect and analyze such gigantic volumes of data about a person, there is an idea whether they are used properly and protected. For instance, in relation to privacy and surveillance issues, facial recognition technology is used by law enforcement. Ethical standards of AI should be established so that the privacy rights are maintained despite social needs for safety and effective social intervention. Other major ethical issues would be bias in AI algorithms. And these laws are approached to accountability and liability compared to basic tools of machinery. So not by living agents, autonomous machines involves decisions, functions that can act without human intervention. Consequently, assuming responsibilities involves an inherently conditional process, wherever such machines have accidents or unwanted consequences for automated medical diagnosis to self-driving. A confusing picture of moral behaviors adopted by the AI systems can be detrimental to human beings and therefore should painstakingly be debated upon its regulation. On the other hand, developing a normative framework for decisions made by AI at a system, people can believe and believe the standards. This leads to trust in technologies that adopt artificial intelligence. It is evident that the sphere of AI governance has not yet attained maturity to warrant independent functions, and hence there should be continuous collaboration among technologists, ethicists, and policy makers. However, while researchers try to better understand machine ethics legislation campaigns that create awareness of moral problems, these are associated with technological development, should also be pursued. It will allow people to make informal decisions about their relationship with them. What should be emphasized here is the preserving the balancedness between technological innovativeness and moral integrity requires long-term effects by regulators, developers, organizations, as well as academia. Thank you.