Ethical Challenges and Bias in Real-World AI: A Fairness-Oriented Approach to Resume Screening Systems

Artificial Intelligence (AI) is rapidly integrating into decision-making across fields like finance, healthcare, and criminal justice. However, its widespread use brings ethical challenges, especially the potential for bias in AI algorithms. This paper examines these ethical concerns, emphasizing three main types of bias: data bias, algorithmic bias, and interaction biasThrough real-world case studies, including biased facial recognition systems, hiring algorithms, and criminal justice AI tools, this paper highlights the far-reaching societal impact of these biases. These case studies demonstrate how bias in AI systems can result in unequal access to resources, unjust treatment in legal systems, and perpetuation of stereotypes in employment practices. This paper highlights the pressing need for robust strategies to address the ethical risks associated with AI. It delves into established frameworks such as Ethically Aligned AI, the EU Guidelines on Trustworthy AI, and the Toronto Declaration, offering essential principles for creating AI systems that prioritize fairness, transparency, and accountability. By evaluating current applications across different industries, this paper proposes actionable solutions to reduce bias in AI systems and also a sample model has been developed to represent how the system has to be under ethics and including enhancing data diversity and implementing robust governance mechanisms to ensure ethical oversight in resume screening processThis research seeks to advance the conversation on ethical AI by emphasizing fairness, equity, and the social good, ensuring that AI technologies are developed to benefit all communities equally.