Maria Alice Maia
Your AI's Promises vs. its Deliverables' Reality
When your AI recommends a product, do you trust its logic? When you ask it to forget a customer, do you trust its memory?
This week, we're tackling two critical gaps between the promises of AI and the reality of its implementation. These aren't edge cases; they are fundamental flaws in how most companies operate, creating a dangerous combination of broken trust and legal liability.
One is a problem of insight, where simplistic recommendation engines insult your customers' intelligence. The other is a problem of compliance, where the "right to be forgotten" becomes a promise your AI technically cannot keep. Both stem from "Doing Data Wrong" and demand a more pragmatic, intelligent approach.
This Week's Essential Reading: From Practical Frameworks to Systemic Risks
Here are some of the most insightful pieces I've read this week, with a take on how they connect to the challenges of building smarter, more responsible AI.
In the Researcher-Practitioner's Toolkit
AI groups spend to replace low-cost 'data labellers' with high-paid experts
This signals a crucial shift in the industry. High-quality data isn't just about accurate labels; it's about capturing domain expertise. To build a recommendation engine that understands "secluded luxury" instead of just "beaches," you need experts to define those latent preferences. This investment in expert-led data annotation is the foundation for moving from simple pattern-matching to building systems that actually understand context.
In AI Regulation & Policy in Practice
Why politicians need to get over their tech insecurity
This is essential for effective governance, especially around issues like the "right to be forgotten." A confident policymaker won't just ask, "Did you delete the data?" They will ask the harder, better question: "Can you provide an audit log proving your AI is no longer
acting on this person's data?". This is the core of Output Suppression—focusing on provable outcomes, not opaque technical promises.
In AI Regulation & Policy in Practice
A more intelligent approach to AI regulation
The FT's call for a surgical, risk-based approach aligns perfectly with the principle of Output Suppression. Instead of trying to regulate the impossible concept of "Machine Unlearning," we should regulate the specific, high-stakes harm: the misuse of a forgotten person's data. It’s about focusing on the function and the risk, not the technology itself.
In the Human-AI Frontier
Disinformation warriors are 'grooming' chatbots
This signals a crucial shift in the industry. High-quality data isn't just about accurate labels; it's about capturing domain expertise. To build a recommendation engine that understands "secluded luxury" instead of just "beaches," you need experts to define those latent preferences. This investment in expert-led data annotation is the foundation for moving from simple pattern-matching to building systems that actually understand context.
In the Human-AI Frontier
The evolution of stupid
This is essential for effective governance, especially around issues like the "right to be forgotten." A confident policymaker won't just ask, "Did you delete the data?" They will ask the harder, better question: "Can you provide an audit log proving your AI is no longer
acting on this person's data?". This is the core of Output Suppression—focusing on provable outcomes, not opaque technical promises.
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