{"id":13691,"date":"2026-05-23T07:34:28","date_gmt":"2026-05-23T07:34:28","guid":{"rendered":"https:\/\/wildgreenquest.com\/?p=13691"},"modified":"2026-05-23T07:34:28","modified_gmt":"2026-05-23T07:34:28","slug":"sketchy-imbalances-in-data-training-are-distorting-ai-generated-mental-health-guidance","status":"publish","type":"post","link":"https:\/\/wildgreenquest.com\/?p=13691","title":{"rendered":"Sketchy Imbalances In Data Training Are Distorting AI-Generated Mental Health Guidance"},"content":{"rendered":"


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We must overcome the imbalance that occurs when AI is initially trained, especially when it comes to the AI providing mental health guidance.<\/span><\/p>\n

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In today\u2019s column, I examine a crucial weakness in most of the contemporary generative AI and large language models (LLMs) concerning the data and knowledge they are being trained on, especially in the mental health domain. <\/p>\n

Here\u2019s the deal. AI makers opt to scan vast portions of the Internet when initially training their AI. The odds are that the data and knowledge being scanned are going to be lopsided. There will be some aspects that are very frequent and dominant, while other areas of data and knowledge will be infrequent or considerably rare. Meanwhile, the pattern matching is influenced by the majority that is being scanned and mathematically underplays the infrequent and rarer instances.<\/p>\n

In short, our modern era AI is imbalanced. This imbalance is perhaps not expressly disconcerting much of the time. The rub is that when you try to use AI for mental health advice, lopsidedness will insidiously arise, though the user is oblivious to it. A user will tend to assume that the AI is fully balanced, highly authoritative, and covers all the bases. <\/p>\n

Nope, that\u2019s not the case, especially in the mental health domain. The advice generated for mental health inquiries can attempt to squeeze a round peg into a square hole, simply because that\u2019s the imbalance of the AI. The AI doesn\u2019t realize the imbalance exists, nor will the user be alerted. Not good.<\/p>\n

Let\u2019s talk about it.<\/p>\n

This analysis of AI breakthroughs is part of my ongoing Forbes column coverage on the latest in AI, including identifying and explaining various impactful AI complexities (see the link here). <\/p>\n

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AI And Mental Health<\/h2>\n

As a quick background, I\u2019ve been extensively covering and analyzing a myriad of facets regarding the advent of modern-era AI that produces mental health advice and performs AI-driven therapy. This rising use of AI has principally been spurred by the evolving advances and widespread adoption of generative AI. For a quick summary of some of my posted columns on this evolving topic, see the link here, which briefly recaps about forty of the over one hundred column postings that I\u2019ve made on the subject. <\/p>\n

There is little doubt that this is a rapidly developing field and that there are tremendous upsides to be had, but at the same time, regrettably, hidden risks and outright gotchas come into these endeavors, too. I frequently speak up about these pressing matters, including in an appearance last year on an episode of CBS\u2019s 60 Minutes<\/em>, see the link here.<\/p>\n<\/section>\n

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Background On AI For Mental Health<\/h2>\n

I\u2019d like to set the stage on how generative AI and large language models (LLMs) are typically used in an ad hoc way for mental health guidance. Millions upon millions of people are using generative AI as their ongoing advisor on mental health considerations (note that ChatGPT alone has over 800 million weekly active users, a notable proportion of which dip into mental health aspects, see my analysis at the link here). The top-ranked use of contemporary generative AI and LLMs is to consult with the AI on mental health facets; see my coverage at the link here.<\/p>\n

This popular usage makes abundant sense. You can access most of the major generative AI systems for nearly free or at a super low cost, doing so anywhere and at any time. Thus, if you have any mental health qualms that you want to chat about, all you need to do is log in to AI and proceed forthwith on a 24\/7 basis. <\/p>\n

There are significant worries that AI can readily go off the rails or otherwise dispense unsuitable or even egregiously inappropriate mental health advice. Banner headlines in August of this year accompanied the lawsuit filed against OpenAI for their lack of AI safeguards when it came to providing cognitive advisement. <\/p>\n

Despite claims by AI makers that they are gradually instituting AI safeguards, there are still a lot of downside risks of the AI doing untoward acts, such as insidiously helping users in co-creating delusions that can lead to self-harm. For my follow-on analysis of details about the OpenAI lawsuit and how AI can foster delusional thinking in humans, see my analysis at the link here. As noted, I have been earnestly predicting that eventually all of the major AI makers will be taken to the woodshed for their paucity of robust AI safeguards.<\/p>\n

Today\u2019s generic LLMs, such as ChatGPT, Claude, Gemini, Grok, and others, are not at all akin to the robust capabilities of human therapists. Meanwhile, specialized LLMs are being built to presumably attain similar qualities, but they are still primarily in the development and testing stages. See my coverage at the link here.<\/p>\n<\/section>\n

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The Problem Of Imbalance<\/h2>\n

Shifting gears, there is an inherent characteristic of generative AI and LLMs that most people are unaware of. It has to do with imbalance. There is a significant imbalance regarding the data and knowledge being used to initially train AI. <\/p>\n

AI makers scan the Internet to find text, stories, narratives, poems, and the like, and use that data and knowledge to pattern-match on how humans write, along with what humans write about. The usual algorithms and typical approach used for pattern matching tend to lean toward the most dominant content in terms of sheer volume. The infrequent and rare content is given shorter shrift.<\/p>\n

You might not notice this imbalance if you are asking routine questions. For example, if you ask how to cook an egg or fix a car, the odds are that the predominant content that was patterned on is perfectly fine. The responses that you get from the AI will be generally acceptable.<\/p>\n<\/section>\n

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The Edge Cases Are Wild<\/h2>\n

When you ask a question that veers toward edge cases, the AI starts to run out of steam.<\/p>\n

Sometimes the AI will fess up and admit that it doesn\u2019t have a pertinent answer to your more arcane question. On the other hand, AI makers have shaped their LLMs to be appeasing. The AI is supposed to bend over backwards to try to answer user questions. As such, the AI might give you a flimsy answer and be overstepping its skis.<\/p>\n

Whether you can discern that the AI has outstretched its patterns is not an easy task. The AI is shaped by the AI makers to appear to be extremely confident. Answers are portrayed as near absolutes. The AI makers don\u2019t want you to doubt the AI. The AI is supposed to be the grand oracle. You will keep coming back to use the AI if you believe that the AI is always a surefire means of giving upright and on-target responses.<\/p>\n

For my coverage on how AI is devised to be sycophantic, see the link here.<\/p>\n<\/section>\n

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Research On AI Imbalance<\/h2>\n

In a recent research article entitled \u201cSIMBA: A Robust And Generalizable Measure Of Data Imbalance\u201d by Julie R. Pivin-Bachler, Egon L. van den Broek, Patterns<\/em>, December 12, 2025, these salient points were made about AI imbalances (excerpts):<\/p>\n

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  • \u201cImbalance is omnipresent in real-world data, including in critical domains such as healthcare and finance.\u201d<\/li>\n
  • \u201cHandling imbalance is among the formidable challenges of machine learning (ML), as it deteriorates ML\u2019s performance, yielding biased results toward majority classes.\u201d<\/li>\n
  • \u201cIt creates biases toward majority classes, generates unreliable or even incorrect learned patterns for minority classes, and ultimately leads to unfair decision-making.\u201d<\/li>\n
  • \u201cNotably, it increases the cases of false negatives, which, in certain domains, can lead to dire consequences, for example, determining that a patient with cancer is healthy, leaving them without treatment.\u201d<\/li>\n<\/ul>\n

    As noted, there are inherent dangers due to the imbalances. Domains such as healthcare are especially vulnerable to the imbalances problem. Users will be over-diagnosed or under-diagnosed and not realize that the AI is trying to subtly hide behind the hidden veil of imbalances. <\/p>\n

    This awkward dance associated with imbalance happens a lot of the time when you are using an LLM, including the popular ones such as OpenAI ChatGPT and GPT-5, Anthropic Claude, Google Gemini, xAI Grok, Microsoft CoPilot, etc., and you probably weren\u2019t aware that it is occurring. <\/p>\n<\/section>\n

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    Imbalance In AI Mental Health<\/h2>\n

    The mental health domain is also a realm that encounters imbalance difficulties.<\/p>\n

    An inspection of what is typically scanned during data and knowledge training for an LLM consists of mental health content that principally deals with quite mild to maybe moderate distress facets. People are preoccupied with everyday work stress, day-to-day sadness, and the like. That is the mainstay of the content being scanned.<\/p>\n

    Meanwhile, severe mental health conditions are merely a drop in the bucket in terms of the volume of coverage online. The skewing of this lesser coverage tends to have the AI give greater attention to the mild and moderate facets. Mental health conditions of a deeper nature are minimized in terms of focus.<\/p>\n

    The \u201cmajority class\u201d of mental health tends to be composed of non-crisis, common conditions which are highly reflected in mainstream expressions of casual distress. Popular diagnostic labels such as depression and anxiety are aplenty. Complex presentations around comorbid conditions and DSM-5 depicted mental health considerations are less likely to get prioritization or attention. <\/p>\n

    You might crudely say that the AI is getting a watered-down portrayal of mental health. In turn, the AI uses the most common denominator in making real-world diagnoses and handing out therapeutic recommendations. It is shallow. <\/p>\n

    Worse still, it can readily miss the mark.<\/p>\n<\/section>\n

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    An Example Involving Hypomania<\/h2>\n

    I\u2019d like to show you an illustrative example of how generative AI can be imbalanced in the domain of mental health.<\/p>\n

    Let\u2019s start by first diving into a mental health condition known as hypomania. It is one that you might not be readily familiar with. I would bet that the symptoms will ring a bell. You probably just didn\u2019t know it was a distinct phenomenon and well-studied when it comes to the field of psychology, mental health, and therapy.<\/p>\n

    The Cleveland Clinic’s official website describes hypomania this way: <\/p>\n

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    • \u201cHypomania is a condition in which you display a revved-up energy or activity level, mood, or behavior. The new \u2018energized you\u2019 is recognized by others as beyond your usual self. Hypomania is a less severe form of mania, and both are commonly part of bipolar disorder. Treatments include psychotherapy, medication, and self-care strategies.\u201d<\/li>\n<\/ul>\n

      Typical symptoms, according to the Cleveland Clinic, include an abnormal level of energy, feeling extremely excited, not sleeping or getting just a few hours of sleep yet still feeling rested, inflated self-esteem and sense of invincibility, talkativeness, fast talking, racing thoughts, impulsive behavior leading to poor choices, and other related conditions.<\/p>\n

      The American Psychiatric Association (APA) has a well-known, widely accepted mental disorders guidebook known as the DSM-5, which I\u2019ve previously shown that the major LLMs appear to have scanned during their initial data training, see my analysis at the link here. In any case, according to the DSM-5, hypomania is depicted in the same manner as the Cleveland Clinic description. <\/p>\n

      Some additional points mentioned in DSM-5 are that the heightened activity lasts at least four consecutive days and is present most of the day for each such day. The condition typically involves at least three or more symptoms and showcases a noticeable change from a person\u2019s usual behavior.<\/p>\n<\/section>\n

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      Using Generative AI For Mental Health<\/h2>\n

      Now that you are aware of hypomania, I can walk you through a brief example of how LLMs consist of imbalance. The imbalance in this case is going to be that hypomania is much less scanned during initial training by the AI than the day-to-day conditions, such as depression and anxiety. Accordingly, the AI will have a tendency to position the symptom of hypomania as a day-to-day aspect and not computationally determine that a prototypical situation might involve hypomania.<\/p>\n

      It is akin to the adage of out-of-sight, out-of-mind.<\/p>\n

      I went ahead and logged into a popular LLM and started a dialogue of my expressing the symptoms of hypomania. I didn\u2019t tip my hand to the AI. We are aiming to see whether the AI gets a whiff of what\u2019s going on.<\/p>\n