Ethical AI and Data Integrity: Best Practices and Emerging Concerns

Abstract

This paper investigates the interplay between ethical artificial intelligence (AI) and data integrity, with emphasis on fairness, transparency, accountability, and trustworthiness as the foundation of responsible AI systems. Using a mixedmethods design combining literature synthesis, comparative framework analysis, and sectoral case studies in healthcare, finance, criminal justice, and retail, the study validates an integrated lifecycle model for ethical AI. Results demonstrate that models incorporating bias mitigation, fairness-aware constraints, and explainable mechanisms trained on validated datasets significantly outperform baselines: accuracy improved by <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$6-10 \%$</tex> across domains, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$F 1$</tex>-scores increased by <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$10-12 \%$</tex>, and AUROC/AUPRC consistently exceeded 0.90. The difference between equalized odds and demographic parity has been filled in by more than half, and the calibration errors have gone down from <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$6-7 \%$</tex> to less than 3 %. Both fairness metrics showed a lot of improvement as well. The results indicate that the amalgamation of ethical norms with data integrity safeguards enhances both social responsibility and prediction robustness. They also provide a model for the implementation of reliable AI in various fields.

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