PUTTING DATA SCIENCE AND AI ETHICS INTO PRACTICE

Abstract: Data ethics govern the ethical use of data in various domains including data acquisition, examination, retention, and decision-making. Within Data Science and AI, ethical considerations are crucial for mitigating biases and ensuring transparency, security, and fairness. Ethical principles involved obtaining informed consent, preventing biased outcomes, and maintaining data integrity. Best practices for practitioners and regulatory bodies include forming diverse teams, adhering to ethical frameworks, prioritizing data privacy and consent, mitigating bias, conducting regular audits, and considering social impacts. A case study conducted in an IT product Development Company exemplifies the integration of ethical AI practices, emphasizing cross-functional collaboration, clear guidelines, proactive ethical reviews, bias detection, stakeholder engagement, education, continuous monitoring, and improvement. The lessons learned underscore the importance of ethical AI practices in fostering trust, sustainability, and competitive advantage. Continuous reflection and refinement are essential for addressing ethical challenges in the dynamic fields of data science and AI. Keywords: Data ethics, AI, Data Science, Data privacy.