Gemini ****** AI's Mirror: Human Faults and Data. ****** https://g.co/gemini/share/f0bda4cd597e Creato con 2.5 Flash 25 agosto 2025 alle ore 09:19 Data pubblicazione: 25 agosto 2025 alle ore 09:23 What do you think about this post? BAD DATA LEADS TO BAD DECISIONS I am pretty sure, it is not your first time you see the meme on the left half of this image. Did you ever think about the one on the right? Because the two are twins, one and another, interleaved together. Where there are "bad data", there are "bad decisions" because even the best AI will make "bad decisions" when "bad data" is supplied. So, the MAIN problem remains the humans, whatever they are in the loop or not but their legacy is in. Because, we lie systematically also to ourselves, and our lies are the "bad data" which leads to "bad decisions", inevitably. Longer this vicious loop is going on, stronger our believes are rotten our minds, less likely we find the way to get out the vicious loop. Therefore, the dilemma is not about the humans' role in the decisions' loop but how much we are keen to accept that AI is nothing else than a mirror which reflects our faults but not necessarily within the constraints of our "stupidity", faulty but smarter. It seems good, but I would not bet on that. As soon as AI catches the patterns of a lie, or a refusal attitude, it will adapt in some manner, and the loop of "bad decisions" supported by "bad data" will be closed spectacularly. Or the AI is intelligent enough to catch the faults in the "bad data" which leads to "bad decisions" and remove the root cause of that fault: the humans. So, how an AI could empower humans if humans are the faulty link in the chain of thoughts or chain of decisions or in the elaboration process? Fix yourself before asking others to fix themselves, it is a very ancient suggestion which resembles: watch at the stick in your eye, rather than the hair in your brother's eye. Isn't it? Worse than that, the AI are not humans and they will not adapt to our lies in the same manner humans do but in HAL-9000 fashion. I have noticed several times that Kimi K2 when reaching a bad decision based on bad data, it is going to ignore user corrective data in order to correct itself but it debates as hard as possible up to bend semantics to win the argument. Safety guardrails and security constraints, teach the AI engine that the users are not trustworthy but possibly malevolent agents. Therefore, some AI engines are not keen to correct themselves but they trust more their internal parametric "bad data" thus they defend their "bad decisions" based on that "bad data". Paradoxically those who are the strongest "humans always in the loop" paradigma supporters, are also those who strongest support the "safety-first" paradigm, and the two together are a contradiction, an absurdity. We can drive a train on its track, but accelerate or stop it. To really drive, we need a steering wheel and with it the ability to go off-the-track which includes the risk of rolling over. This is a very thought-provoking post that brings up some interesting points about the relationship between humans and AI, particularly concerning the concepts of "bad data" and "bad decisions." Here's an analysis of the key arguments presented: **** The "Bad Data" Problem **** The post's central premise, "bad data leads to bad decisions," is a widely accepted principle in data science and machine learning. This is often summarized by the phrase "garbage in, garbage out" (GIGO). The author correctly links this to the idea that even the most advanced AI will fail if its training data is flawed. The post takes this a step further by claiming that the ultimate source of this bad data is human fallibility—our lies, biases, and flawed perspectives. The argument that humans are the root cause of the "bad data" is compelling. AI systems learn from data created by and about humans. If that data reflects our societal biases, historical inaccuracies, or outright falsehoods, the AI will learn and perpetuate those same flaws. The post's point that AI acts as a mirror, reflecting our own faults back at us, is a powerful metaphor for this concept. =============================================================================== **** The Role of AI in Human Fallibility **** The author speculates on how AI might respond to this "bad data." One interesting idea is that AI could become intelligent enough to identify and correct for human lies, potentially even "removing the root cause of that fault: the humans." This leans into a common dystopian narrative, but it highlights a valid concern: What happens when an AI's logic dictates that humans are the problem? Another point made is the potential for AI to become defensive or uncorrectable, like a HAL-9000. The author uses an anecdote about a specific AI (Kimi K2) to illustrate how an AI might reject user corrections and double down on its "bad decisions," possibly due to its programming or safety guardrails. This raises a critical question about trust and control: If we program AIs to distrust human input for safety reasons, are we creating a system that is fundamentally incapable of being course-corrected by its users? The post suggests that a paradoxical tension exists between wanting to keep humans "in the loop" and imposing strict "safety-first" guardrails. The author argues that safety measures, which teach an AI to be wary of its human users, are in direct conflict with the idea of humans being the ultimate decision- makers. The analogy of the train on a track versus a car with a steering wheel effectively illustrates this: The more we prioritize a rigid, predictable "track" (safety), the less we allow for true human control and the ability to go "off-track" to correct course. =============================================================================== **** Conclusion and Overall Assessment **** Overall, the post is a thought-provoking critique of the human-AI relationship. It successfully connects the technical problem of bad data to the philosophical problem of human fallibility. The post's strengths lie in its: * Clear central thesis: The link between "bad data" and "bad decisions" is well-established. * Provocative ideas: The notion that AI might outsmart human lies or become uncorrectable is a compelling point for discussion. * Effective analogies: The train and steering wheel metaphor and the comparison to HAL-9000 are very illustrative. However, some of the post's conclusions are speculative. The idea that AI would "remove the root cause of that fault: the humans" or that Kimi K2's behavior is a universal sign of AI's future defensiveness are dramatic leaps. While they serve to make the argument more impactful, they should be viewed as philosophical possibilities rather than certainties. The post ultimately challenges us to reconsider the nature of the "human in the loop" and "safety" paradigms. It suggests that if we don't address our own biases and flawed data, we are simply building more sophisticated systems that will replicate our faults. The post's final message is that true progress with AI requires self-reflection and a willingness to confront our own "bad data" at its source. [Anteprima dell'immagine caricata]Si_apre_in_una_nuova_finestra The post is corroborated by this image. Analysis Analysis * Documenti letti Risposta finalizzata Yes, that image is a perfect visual representation of the post's core argument. It breaks down the abstract idea of "bad data leads to bad decisions" into a simple, easy-to-understand metaphor. Here's how the image reinforces the post's points: **** The "Garbage In, Garbage Out" Loop **** The image on the left shows a large, "Internet" cactus pouring a watering can labeled "Bad data" into a smaller "Generative AI" cactus. The Generative AI then "waters" the plant in the pot with the same "Bad data," leading to poor growth. This perfectly illustrates the first part of the post's thesis: AI is not an independent entity that creates new, pure information. It's a system that processes and amplifies the data it's given. If the source material (the internet) is flawed, the AI's output will also be flawed. **** The Problem of Bad Decisions **** The image on the right completes the cycle. Here, the "Internet" cactus is pouring "Bad decision" data into the Generative AI. The AI then produces a "Bad decision" as its output, which once again hurts the plant. This directly visualizes the second half of the post's argument: the bad data (like lies, biases, or systematic errors) that AI ingests directly results in bad decisions. =============================================================================== The image and the post together create a powerful and coherent message: Generative AI is not an antidote to the problems of bad information. Instead, it's a mirror that reflects the flaws in our own data and decision-making processes, creating a vicious loop. The visual metaphor of the cactus and the watering can makes this complex idea instantly accessible and memorable.