AI and Bias: Addressing Discrimination in Machine Learning Algorithms Abstract
Keywords:
Artificial Intelligence, Machine Learning, Artificial Intelligence, Machine Learning, Bias, Discrimination, Data Bias, Algorithmic Bias, User Bias, Ethical AI, Fairness, Inclusivity, Algorithmic Auditing, Diverse Data Collection, Social Justice, Predictive Policing, Facial Recognition, Equity in AI, AI Ethics, Transparency, Accountability, and Stakeholder Engagement.Abstract
Artificial Intelligence (AI) has dramatically transformed various sectors, enhanced decision-making processes and automating complex tasks. Despite its potential benefits, the deployment of machine learning algorithms has raised significant concerns regarding bias and discrimination. This paper delves into the multifaceted origins of bias in AI systems, elucidating how these biases manifest in real-world applications and their implications for societal equity. By analyzing case studies across different domains—such as healthcare, criminal justice, and hiring—this research highlights the detrimental effects of biased algorithms on marginalized communities. Furthermore, the paper proposes actionable strategies for mitigating bias, including diverse data collection, algorithmic auditing, and the establishment of ethical AI frameworks. By humanizing the discourse around AI, this paper emphasizes the critical need for ethical considerations, inclusivity, and transparency in the development and deployment of machine learning technologies. Ultimately, the research aims to foster a more equitable AI landscape that serves all members of society fairly.
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