If an AI system can perform all tasks with human-level precision but lacks explainability, should its lack of explainability be a concern if it still meets the accuracy and performance requirements expected by humans?
Dear Saif ul Islam ,
Experts emphasize that artificial intelligence technology itself is neither good nor bad in a moral sense, but its uses can lead to both positive and negative outcomes.
With artificial intelligence (AI) tools increasing in sophistication and usefulness, people and industries are eager to deploy them to increase efficiency, save money, and inform human decision making. But are these tools ready for the real world? As any comic book fan knows: with great power comes great responsibility. The proliferation of AI raises questions about trust, bias, privacy, and safety, and there are few settled, simple answers.
https://scienceexchange.caltech.edu/topics/artificial-intelligence-research/trustworthy-ai
Regards,
Shafagat
This is an excellent question. There are many applications where AI performs tasks better than humans or using human labour is economically pointless (e. g. excellent recommendation systems on the social media). But even there they optimise for one's engagement and usually not one's best interest - if a person watches naive videos on cats for 10 hours, the recommendation is: maybe a next cat video?
Chatbots - they answer any question, but their answer is softened through various filters and so called RLHF. They usually use excessive qualifiers even if the evidence is clear. Therefore they are not perfectly reliable for summarising academic papers (but of course are commonly used for it).
Summary: Even simple examples show the serious concerns.
Especially in fields like accounting or healthcare, trusting artificial intelligence that makes extremely precise decisions but cannot clarify how it functions, often referred to as a "black box" and can be dangerous. One clever concept is to pair it with another artificial intelligence able to simply explain such choices. We could also use separate AI systems to watch the main one and let us know if something goes wrong. Companies can also keep records of how decisions are made, so they can review them later and make sure everything is working as it should.
In high-stakes industries such as banking, law, or healthcare, knowing why a decision was made shapes trust, accountability, and compliance. Including decision impact tracking and confidence rating helps us to let people evaluate how much to trust an AI's output depending on context and risk level.
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