With increased roles of Artificial Intelligence and Machine Learning algorithms, “Humans-in-the-Loop” will become an increasingly important role for engineers. As arbiters of the Natural Laws, the consequences of failures can be catastrophic and life threatening.
CoEngineers is pioneering the emerging role of the AI/ML Validation engineer offering industry, financial, and insurance companies with expert nodes to safeguard AI impacted systems. In the future, AI/ML Validation Engineers will become essential for ensuring the ethical, accurate, and safe deployment of AI systems.
In General, AI must be treated the same as actual intelligence. Here are the key areas where human oversight and involvement are crucial.
1. Accountability and Governance
Just like the real world, human oversight is vital for maintaining accountability in AI systems. This includes ensuring that AI systems are designed with clear goals, roles, responsibilities, and lines of command. Human involvement helps in recognizing authority, interrogating AI decisions, and limiting the power of AI systems to prevent misuse and ensure compliance with ethical standards and regulations.
2. Transparency and Trust
Transparency in AI operations is necessary for building trust among users and stakeholders. Human overseers are responsible for making the methods, data, and performance of AI systems transparent and understandable to laypeople. This transparency helps mitigate issues like automation bias and algorithm aversion, where humans either over-rely or under-rely on AI advice.
3. Error Handling and Decision-Making
Humans are needed to intervene when AI systems make mistakes or when decisions have significant ethical or safety implications. For example, in scenarios where an autonomous vehicle misidentifies a pedestrian or an AI-powered chatbot provides incorrect medical advice, human intervention is necessary to correct these errors and make informed decisions.
4. Ethical Framework and Bias Mitigation
AI systems can harbor hidden biases due to the data they are trained on. Human intervention is essential to identify and mitigate these biases, ensuring that AI decisions are fair and do not perpetuate existing inequalities. This involves continuous monitoring and updating of AI systems to align with ethical standards.
5. Regulatory Compliance and Legal Accountability
Human oversight is crucial for ensuring that AI systems comply with legal and regulatory requirements. This includes conducting independent evaluations, pre-release certifications, and ensuring that AI systems meet the criteria for trustworthy AI, such as being valid, reliable, safe, secure, and privacy-enhanced.
6. Hybrid Intelligence
In some cases, human oversight may be augmented by AI to create a hybrid intelligence system. This approach leverages the strengths of both humans and AI, ensuring that human judgment and ethical considerations are integrated into AI decision-making processes.
7. Institutional Design and Democratic Governance
Human oversight roles need to be clearly defined and institutionalized to anticipate and mitigate the fallibility of human overseers. This involves creating a taxonomy of oversight roles and formulating normative principles that ensure effective and trustworthy human oversight in AI governance.
In conclusion, human intervention is critical at various points in the lifecycle of AI systems to ensure they operate ethically, transparently, and safely. This involves a combination of accountability, transparency, error handling, ethical oversight, regulatory compliance, hybrid intelligence, and robust institutional design.