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AI Ethics Controls Explained
This episode of the ISACA Advanced in AI Security Management (AAISM) exam prep series moves into a domain that is often unfamiliar to technical professionals but where AI causes the most public harm: ethics. It explains why AI raises ethical questions that ordinary technology does not, which uses society has now declared outright unacceptable, and the strategies and concrete controls that turn ethics from a slogan into a managed program inside the enterprise.
What this episode covers
- Why AI ethics is distinctive, with automatic decision-making and self-learning giving the system harmful autonomy at scale.
- The EU AI Act and the catalog of uses it treats as outright unacceptable.
- The strategies that keep AI ethical, including context-aware purpose review, diverse collaboration, benchmarking, and user feedback.
- The human in the loop as a foundational ethics control over consequential AI decisions.
- The role of an AI ethics and compliance committee and an AI code of conduct in operationalizing ethics.
- The need to document adherence to standards covering health, safety, law, environment, and fundamental rights.
- Transparency, fairness, bias controls, and environmental impact as the routine artifacts of an auditable ethics program.
Watch the full episode above for the worked examples and detailed explanations of each concept.
Frequently Asked Questions
Why does AI raise ethical questions unlike other technology?
Two capabilities set AI apart from ordinary technology: automatic decision-making and self-learning. Both mean the system acts with some independence from humans, and that autonomy is exactly what can create undue harm or risk to people, which is why ethics deserves dedicated attention in AI governance.
What AI uses does the European Union’s AI Act treat as unacceptable?
The EU AI Act identifies prohibited uses including social credit scoring, emotion recognition in workplaces and schools, systems that exploit vulnerabilities such as age or disability, behavioral manipulation that undermines free will, untargeted scraping of facial images, biometric categorization using sensitive characteristics, and certain predictive policing and real-time public biometric identification applications.
What strategies keep AI ethical?
Consider the AI’s intended purpose alongside your context, applicable laws, and societal norms. Ensure genuine collaboration and diversity in development across demographics, departments, and skills. Gather benchmarking data from similar projects. Build feedback mechanisms so users can surface concerns and unexpected harms early.
What are the core AI ethics controls organizations should implement?
Keep a human in the loop overseeing AI systems, establish an AI ethics and compliance committee, create and regularly evaluate an AI code of conduct, document that systems adhere to standards of health, safety, law, environment, and fundamental rights, ensure transparency, fairness, and freedom from bias, and evaluate the environmental impact of using AI.
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Reference: This article is based on concepts discussed in AI Ethics Controls Explained.