We're making AI models more powerful, but they're also becoming more complex. We're trying to get them to explain their decisions, but sometimes these "explanations" feel shallow or incomplete. Can we ever truly make AI fully transparent, or will it always be a bit of a mystery?
On the one hand, we can design systems to provide explanations for their decisions, like feature importance or causal pathways, and frameworks like Explainable AI (XAI) are already making progress here. But the complexity of these models, especially deep learning ones, makes it hard to fully unpack every decision in a way that feels intuitive or meaningful.
It’s like asking a genius painter to explain every stroke—they might show you the techniques, but the depth of intuition or abstraction might still feel elusive. AI is similar: some parts can be explained, but its “creative” leaps might always remain a bit of a black box.
I think the goal shouldn’t be perfect transparency (which might not be realistic) but building enough trust and reliability that the mystery doesn’t hinder our ability to use it responsibly. So yeah, AI might stay a bit mysterious, but it’s about managing that uncertainty wisely.
"Explainable AI" (XAI) is a realistic goal, though it presents significant challenges. Here are some key points to consider:
Realistic Aspects of XAI:
Touhidul Alam Seyam
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