AI in Aviation SMS
Balancing Innovation and Ethics
The AI Revolution in Aviation Safety
From revolutionizing data insight and analysis to making predictive maintenance and hazard detection near instantaneous, artificial intelligence (AI) applications have transformed safety management and operational efficiency for the better. But these mysterious tools must be made transparent, privacy controls must remain robust, and human judgment on safety critical decisions cannot be entirely replaced or automated.
The Human Factor in AI-Driven Safety Systems
As industries begin adopting or maturing AI in their workflows, aviation safety professionals must always remain aware that human intelligence is necessary in the system and of the importance of effective technological oversight. Although AI will continue to rapidly change and evolve, safety practitioners must also apply traditional risk control techniques to balance the governance of innovation and ethics within their organizations.
International Standards for AI Management
The International Organization for Standardization (ISO) and the International Electrotechnical Commission (IEC) released comprehensive technical guidance in ISO/IEC 42001:2023: Information Technology - Artificial Intelligence - Management System (AIMS), which outlines pathways for organizations using AI services and products to responsibly adopt and continually improve their AI usage. Much like Safety Management Systems (SMS), the AIMS will be continually monitored, assessed, and improved to ensure its output is meeting regulatory compliance standards, aligns with ethical business practices, and increases safety by mitigating risks (ISO/IEC, 2024).
Ethical Considerations in AI Implementation
When it comes to automation and AI, privacy and human oversight cannot be compromised. Because AI systems will learn and change behavior over time, their output and decision making can sometimes feel inexplicable. Humans must be involved in AI processes to audit and evaluate those automated behaviors as they evolve. Ethically employing new AI technologies requires deliberate consideration to fairness, transparency, and accountability—the emphasis should be on complementing the human component, not replacing it.
The Multidisciplinary Approach to AI Ethics
According to IBM, AI ethics is the multidisciplinary focus to "optimize AI's beneficial impact while reducing risks and adverse outcomes" (IBM, 2024). Just as traditional business practices emphasize data privacy, trust, and sustainability (among other areas), AI systems and products represent their own unique challenges to the entities that use them.
Proactive AI Governance in Aviation
Although Federal regulations and policies for generative AI tools are certainly underway, it is still up to owners/operators to ensure their systems are performing responsibly, ethically, morally, and safely. The approach will vary based on the specific context of each organization, but the process should remain as proactive and adaptive as the core safety principles each already upholds. PAI Consulting can help your company remain mindful of these considerations and seek out best practices for AI adoption and implementation. Our experts are here to ensure your organization upholds ethical integrity while meeting regulatory standards.
Key Takeaways
- AI is transforming aviation safety management, but requires careful implementation.
- Human oversight remains crucial in AI-driven safety systems.
- International standards like ISO/IEC 42001:2023 provide guidance for responsible AI adoption.
- Ethical considerations, including privacy and fairness, are paramount in AI implementation.
- Organizations must proactively govern their AI systems to ensure responsible and safe performance.
- PAI Consulting offers expertise in navigating the complex landscape of AI in aviation safety.
What do you think about AI in safety management? Let us know:
References
IBM. (2024, August 27). What is AI ethics? Retrieved from IBM
ISO/IEC. (2024, August 27). ISO/IEC 42001:2023. Retrieved from International Organization for Standardization
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