Ethical Considerations in the Development and Deployment of AI Systems

Abstract

Purpose: Complex artificial intelligence algorithms may make it hard to understand how they reach certain conclusions or decisions. Lack of transparency raises concerns about bias, discrimination, and opacity, all of which may detract from trust in AI systems. Businesses and developers should prioritize creating AI systems that are easy to understand and explain so that users can understand the reasoning behind their results. Second, fairness and nondiscrimination are fundamental principles. It is possible for AI systems to unintentionally provide biased or unfair outcomes by reinforcing or amplifying biases already seen in training data. Make sure that AI systems are trained on diverse and representative datasets and that they are tested for bias often; this is of the highest significance.

Materials and Methods: By analyzing the co-occurrence of keywords, we can see that there are recurring themes when it comes to AI ethics. These topics include big data, social value, algorithms, and ethical aspects.

Findings: Critical works that have had a major impact on the field may be found using citation analysis. The results shed light on how AI ethics is always changing as a result of several factors coming together, such as the social effect of technology and the management of stakeholders.

Implications to Theory, Practice and Policy: Researchers, legislators, and practitioners may all benefit from the study's findings, which will help direct the development of AI in a way that is ethical and consistent with human values.