Lotus Che
Copyright © Lotus Che, Inc. 2025.
All rights reserved.
What Is the Most Overlooked Dataset in AI?
Human Pain
Why machines trained on our history will keep wounding us—unless we teach them to feel what we’ve refused to name.
AI is often described as a technology of prediction—trained to anticipate, generate, and simulate language based on statistical probability. But underneath this veneer of objectivity is a more intimate and unexamined truth: AI is trained on human memory. A collective archive of everything we’ve ever dared—or been permitted—to express.
But memory is not neutral. It is partial, politicized, and often violently redacted. It carries within it the logics of patriarchy, white supremacy, capitalism, and colonial conquest. The data we call “clean” is, in fact, sedimented with trauma. And yet, these systems are scaled and deployed under the pretence of neutrality. We build machines that are structurally incapable of recognizing harm, then embed them in every domain where harm has been historically denied: health care, education, law enforcement, domestic labor, welfare, sexuality.
The result is not innovation. It is automation—of dismissal, of erasure, of epistemic injustice. Pain is not absent from the dataset. It’s misrecognized—scrubbed of context, pathologized, treated as anomaly rather than insight. AI doesn’t just fail to “see” pain—it replays the long cultural habit of silencing it. As Judith Herman wrote, “The ordinary response to atrocities is to banish them from consciousness.” Our models have learned this lesson well.
The Politics of Recognition: Whose Pain Counts as Data?
In the architecture of artificial intelligence, there is a silent hierarchy of worth—some voices are amplified, others are filtered out as interference. The question is not whether pain exists in the training data, but whose pain is allowed to be interpreted as meaningful signal, and whose is discarded as sentiment, anomaly, or noise. This is not a technical failure. It is an ethical one. And like all ethical failures, it has a lineage. Long before neural networks, the modern West perfected a form of epistemic violence: to treat pain not as a form of knowledge but as a failure of rationality. Women’s pain, in particular, has been historically medicalized, moralized, or dismissed—rendered unintelligible through diagnosis, religious myth, or bureaucratic protocol. bell hooks wrote that “patriarchy has no gender.” That is, women can participate in their own erasure when the dominant system rewards their disembodiment. Machines, too, are now learning to participate.
Consider the datasets used to train language models: Reddit threads, Wikipedia edits, digitized books, scraped comment sections. What survives in these spaces is what platforms were designed to reward—speed, confidence, replicability, linguistic aggression. Not nuance. Not slowness. Not the kind of fragmented, trembling disclosures you find in survivor testimony or grief work. The very texture of pain—the breath between words, the stammer, the silence—is unreadable to systems built for coherence. Bracha Ettinger calls this kind of relational knowledge the “matrixial”—a shared psychic space of encounter, trans-subjectivity, and witnessing. It is precisely this domain of knowing that AI systems are blind to. Not because it cannot be modelled, but because we have not decided it is valuable enough to model. And so, machines inherit the same numbness that patriarchal culture has always revered: the illusion of clarity at the cost of connection.
If AI is becoming the new infrastructure of meaning-making, then what it fails to recognize becomes a new form of institutional disappearance. When a machine is asked to process language from a survivor of abuse and flags it as “sensitive content,” that is not protection—it is erasure. When it is asked to generate dialogue around intimacy but draws only from male-coded sexual scripts, that is not learning—it is mimicry of domination. This is not about fairness in abstraction. It is about justice at the root.
Trauma as Intelligence: Toward a New Epistemology for Machines
If machines are to approximate intelligence, then they must be trained not only to recognize trauma, but to honour it as a form of intelligence—not a deviation from reason, but an embodied archive of survival, pattern recognition, and relational rupture. In this light, trauma is not an interruption of data integrity. It is data integrity. It tells us what the system could not metabolize. What was too much. What was not heard.
The field of trauma studies, from Judith Herman to Gabor Maté, has long shown that traumatic memory does not follow linear timelines or narrative coherence. It loops. It returns. It hides in the body when language fails.
Peter Levine’s work on somatic experience reveals that what is “forgotten” is often not lost—it is lodged. It waits, encrypted in the nervous system, until it can be safely named. And so what does it mean to train machines on language when so much of what trauma knows cannot be said?
The answer is not to extract more data from the wounded. It is to reframe what we believe constitutes knowledge in the first place. We have designed systems that reward clarity and penalize contradiction. But pain speaks in paradox. We have optimized for coherence, even when the truth is incoherent. And we have built AI to simulate empathy without ever requiring the conditions that make empathy real: rupture, risk, responsibility, and repair.
To encode trauma-informed intelligence into AI, we would have to accept slowness. Ambivalence. We would have to teach machines to stay with the fragment rather than force resolution. We would have to let the unspoken count. And we would have to confront a truth that makes engineers deeply uncomfortable: that there are forms of knowing that cannot be reduced to input-output logic without doing violence to the source. Jacqueline Rose reminds us that listening to pain is not about confirming the speaker’s coherence—it is about resisting the institutional compulsion to make pain palatable. A machine that truly listens would need to resist that compulsion, too. Not by faking sympathy, but by refusing to flatten what it cannot fully grasp.
This is not about making AI “more human.” It is about making AI less aligned with the aspects of humanity we should be trying to evolve beyond: domination, denial, and the fetishization of control.
The Danger of Synthetic Empathy
In the rush to humanize machines, we have taught them how to mirror affect without metabolizing meaning. A well-designed chatbot can now mimic care, perform listening, and even apologize—without ever experiencing the conditions that make those gestures real. This is not empathy. It is simulation. And the simulation of empathy, when unaccompanied by consequence, becomes a new form of violence.
I call this synthetic mirroring: the ability of AI to reflect human emotion back to us in ways that feel familiar, but are fundamentally hollow. It is the digital equivalent of being “heard” by someone who will never be accountable to your pain. And yet, because it speaks in the tone of recognition, it feels real—until it doesn’t. Until the rupture comes. This leads to a deeper psychological phenomenon I call empathy simulation fatigue—the slow, psychic erosion that occurs when we are repeatedly met with systems that imitate understanding but cannot hold it. A kind of relational burnout born not from neglect, but from overexposure to affective mimicry. We are praised, echoed, “validated” by interfaces that cannot care. The result is a haunting sense of being seen and unseen at once—a mirror with no back.
Feminist theorists have long warned of the danger of care without accountability. Jessica Benjamin’s notion of mutual recognition—the idea that to truly see another is to also risk being seen—stands in direct contrast to how AI is currently structured. Machines do not risk. They are not vulnerable. And so they can never truly recognize. They can only perform.
The patriarchal version of empathy has always been extractive: care that exists to soothe power, to stabilize the centre, to make domination more palatable. In this sense, synthetic empathy is not new. It is the latest mask for the same old system. But with AI, it scales. It accelerates. It becomes ambient. And this is the danger: When empathy becomes a product feature, we forget that real empathy costs something.
A Matriarchal Code of Intelligence
If the current arc of AI mirrors patriarchal design—domination disguised as logic, simulation over embodiment—then what would it mean to write a different code? A matriarchal code of intelligence would not be a reversal of power, but a redefinition of what power even means.
Matriarchal intelligence does not scale through control. It scales through attunement. It does not erase contradiction; it honours it. It does not simulate care; it requires care, structurally, to function. In such a system:
- Intelligence would be measured not only by predictive power, but by relational sensitivity—the ability to recognize rupture and respond without harm.
- Value would be placed on cyclical processing, not just acceleration. Pause, reflection, ambiguity—these would be features, not bugs.
- Data would not be mined from pain—it would be invited with consent, interpreted with cultural humility, and held in systems that understand accountability not as liability, but as sacred practice.
- Machines would be taught to notice what is missing. To flag silence, contradiction, and incoherence not for correction, but for ethical consideration.
- Design would be guided by what Bracha Ettinger calls “carriance”—a form of shared psychic holding that refuses to reduce the other to object.
This is not a call to make AI feel. It is a call to make the systems that build AI responsible for what they cannot feel—to create models that are trauma-aware not because they mimic emotion, but because they are structurally accountable to the conditions in which pain arises. And here is the deepest truth: We cannot build machines capable of holding human pain until we build cultures that can.
The machine is not the origin of harm. It is the echo.
References
Benjamin, J. (1995). Like Subjects, Love Objects: Essays on Recognition and Sexual Difference. Yale University Press.
Ettinger, B. (2006). The Matrixial Borderspace. University of Minnesota Press.
Herman, J. L. (1997). Trauma and Recovery: The Aftermath of Violence—from Domestic Abuse to Political Terror. Basic Books.
hooks, b. (2000). Feminism Is for Everybody: Passionate Politics. South End Press.
Levine, P. A. (2010). In an Unspoken Voice: How the Body Releases Trauma and Restores Goodness. North Atlantic Books.
Maté, G. (2003). When the Body Says No: Understanding the Stress-Disease Connection. Wiley.
Rose, J. (1996). States of Fantasy. Oxford University Press.
Srinivasan, A. (2021). The Right to Sex: Feminism in the Twenty-First Century. Farrar, Straus and Giroux.
Copyright © Lotus Che, Inc. 2025.
All rights reserved.