AI training data can comprise billions of points from diverse sources, challenging traditional management methods.
Low-quality, biased, or inconsistent training data greatly affects AI models performance and fairness. Maintaining high data quality at AI-scale is exceptionally challenging.
The inner workings of complex AI models are often black boxes, making specific decisions opaque and posing governance challenges.
Biased training data can cause AI models to make prejudiced decisions. Continuous bias detection and management are vital.
INCREASED PRIVACY RISKS
AI's deep insights from data patterns raise privacy concerns.
Data anonymization also provides limited protection against
Increased use of consumer data by AI applications raises compliance requirements like GDPR, CCPA etc.
SHORTAGE OF SKILLS
Governing AI requires a blend of data governance and data science skills which are scarce, hampering oversight of AI systems.
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