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Common Costly Mistakes in AI Implementation - and How to Avoid Them!

Overlooking ethical and legal implications

Neglecting data and AI ethics erodes trust, alienates employees and customers, damages brand reputation, and exposes businesses to regulatory scrutiny and legal penalties.

Breaching data & AI regulations is very costly.

  • Understand and follow data & AI regulations to ensure compliance from Day 1

  • Set up an AI Ethics Committee as a strong cross-functional team to establish clear standards, best practices, and principles for AI, ethics, privacy and security

  • Include Ethical Principles such as Privacy & Data Protection, Accountability & Responsibility, Fairness & Non-Discrimination, Transparency & Explainability, Reliability & Accuracy, Safety & Security, among others

  • Take a proactive approach to AI ethics by identifying and addressing likely concerns and questions from employees and customers before they escalate

  • Enforce responsible, transparent, and accountable data and AI practices to uphold ethical standards, build trust, and ensure compliance

  • Define and enforce strict standards to mitigate model bias, protect data privacy and prevent copyright infringements

  • Ensure fairness by regularly auditing and refining AI models to eliminate bias

How to avoid it:

MISTAKE 4: