Explainable artificial intelligence (XAI) is a set of processes and methods that allows human users to comprehend and trust the results and output created by machine learning algorithms.
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Stahl BC, Andreou A, Brey P, Hatzakis T, Kirichenko A, Macnish K, Shaelou SL, Patel A, Ryan M, Wright D (2021) Artificial intelligence for human flourishing—beyond principles for machine learning. J Bus Res 124:374–388
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Furthermore, visual representations like the one above help us fully grasp the concept of Ai Fairness And Transparancy.
This systematic literature review (SLR) explores the ethical dimensions of Artificial Intelligence (AI) development, with a particular emphasis on the principles of Fairness, Accountability, Transparency, and Ethics (FATE). Ethical AI development is vital in today’s world as AI systems are steadily increasing. This study reveals the significance of adhering to these ethical principles to aid the responsible creation of AI systems by taking key points from past research to provide an in-depth review of ethical AI creation. Fairness is presented as morally imperative, as discriminatory AI decisions can enable societal injustices and diminish public trust. Explainable AI (XAI) is highlighted as a powerful tool for identifying and rectifying algorithmic biases and, in turn, promoting fairness. Accountability and transparency are crucial for understanding the procedures and decisions of AI algorithms, especially in cases where AI affects human interests. The global scale of AI underscores the require for international collaboration and consistent standards for accountability. Transparent AI systems enhance user trust by promoting visibility and understandability. The paper concludes that adherence to the ethical pillars of Fairness, Accountability, and Transparency (FAT) is essential for responsible AI development. Additionally, the research identifies pressing concerns in unethical AI development, including bias, discrimination, and privacy breaches, which necessitate further attention. Recommended future research includes addressing the abovementioned issues and promoting ethical AI development, ensuring the well-being of individuals and society.
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Explainable AI is used to describe an AI model, its expected impact and potential biases. It helps characterize model accuracy, fairness, transparency and outcomes in AI-powered decision making. Explainable AI is crucial for an organization in building trust and confidence when putting AI models into production. AI explainability as well helps an organization adopt a responsible approach to AI development.
