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Role of Stakeholders in AI Risk Management

AI risk management is in no way a one-team effort. It requires input from diverse stakeholders, including executives, AI developers, data scientists, users, policymakers, and ethicists.

And this is where you need to invest in involving multiple perspectives. Let’s look at the typical roles involved in the management process.

Who’s Involved?	Roles and Responsibilities
Data Owners	They ensure the data used to train AI is accurate, clean, and free from bias. If insufficient data is input, bad decisions are made, so they set rules on data quality, privacy, and security before AI models start learning.
System Developers	These are the people designing and fine-tuning AI models. Their job is to make AI work efficiently while preventing issues like bias or security flaws. They also focus on making AI explainable so it does not spit out answers without reasoning behind them.
AI Users	This group ensures that AI aligns with business goals and does not introduce unnecessary risks. They also assess how AI decisions impact customers, finances, and overall operations.
Compliance Officers	These people ensure AI does not break privacy laws (like GDPR or HIPAA) or violate ethical guidelines. They develop AI policies, enforce security controls, and ensure AI follows regulatory standards.
External Auditors	External reviewers audit AI models for fairness, transparency, and security issues.