In an interview with Forbes, Augury’s VP of Strategy Artem Kroupenev discusses how human-AI collaborations are being optimized for the industrial sector. In short, it’s about keeping the humans in the driver’s seat while using a system they can trust.
Whether it’s Machine Health providing step-by-step maintenance recommendations or Process Health helping operators navigate production in real-time, the article ‘Human-AI Collaboration: Keep The Machines On A Short Leash’ by Joe McKendrick highlights real examples of AI and humans working together.
In the context of a recent study in Nature, ‘When Combinations Of Humans And AI Are Useful: A Systematic Review And Meta-Analysis’, which suggests humans and artificial intelligence don’t necessarily work as well together as many assume, the article in Forbes outlines the scenario where such a collaboration does work.
Here are some key insights on blending human and AI tasks:
1. Trust Is Key
“Collectively, these studies show that ‘human-AI systems do not necessarily achieve better results than the best of humans or AI alone,’ […] Challenges such as communication barriers, trust issues, ethical concerns and the need for effective coordination between humans and AI systems can hinder the collaborative process.”
2. AI Already Has Proven Track Record In Manufacturing
“Some hands-off AI-driven processes are already operational and trusted in manufacturing. [Examples include:] providing prescriptive diagnostics for a wide range of critical industrial equipment, identifying faults and recommending precise, step-by-step maintenance actions months in advance.”
3. Human In The Driver’s Seat
However: “AI should have similar safeguards as statistical or threshold-based automation. Humans should be able to review and intervene in the overall plan, specific tasks, decisions, and actions for any critical part of the AI-driven process. There should also be a simple way to review and edit process goals, guide rails, and constraints to guide AI-driven processes. With robust intervenability and guardrails, a single human supervisor can oversee multiple AI-driven processes, increasing autonomy and productivity.”
4. Learning … Together
“The ability to overturn AI insights or decisions should be considered a product feature, not a bug. Attaching a confidence score to raw AI insights can help users trust the recommendation, but there are cases where users have made decisions contrary to AI recommendations, especially in edge cases with low confidence. In my experience, users who initially overturned AI recommendations often came to the conclusion that they made incorrect decisions, which has ultimately increased their trust in the system for future encounters.”
Read the full article: ‘Human-AI Collaboration: Keep The Machines On A Short Leash’.
