ETHICAL IMPLICATIONS AND GOVERNANCE MODELS FOR RESPONSIBLE DATA SCIENCE PRACTICES

Korang Appleby Journal

ETHICAL IMPLICATIONS AND GOVERNANCE MODELS FOR RESPONSIBLE DATA SCIENCE PRACTICES

Authors

Korang Appleby Journal

AI Ethics and Governance Lead, France.

Keywords:

Data Ethics, Governance Models, Responsible AI, Data Privacy, Algorithmic Bias, Fairness, Transparency, Accountability, Risk Mitigation

Synopsis

The rapidly expanding reach of data science into sensitive sectors—healthcare, criminal justice, education, and financial systems—has amplified concerns about ethical conduct and accountability. As machine learning and artificial intelligence systems become central to decision-making, the risks of algorithmic bias, privacy violations, and opaque decision processes have increased. This paper explores ethical implications emerging from modern data science applications and proposes comprehensive governance models that balance innovation with societal responsibility. Grounded in recent and prior academic literature, this study examines both the philosophical underpinnings and practical mechanisms of responsible data science, offering visual models and comparative frameworks to guide institutional policy.

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