by Dayna Guido | May 22, 2026 | Featured Articles
Dayna Guido on AI and Mental Health: When the First Conversation Is With a Machine
This article orginally appears on NYWEEKLY in May of 2026.
The Quiet New Ritual of Modern Distress
Late at night, when the house is quiet, but the mind is not, more people are beginning to reach for a kind of support that barely existed a few years ago. Instead of calling a friend, texting a partner, or waiting for a therapist appointment, they open a chat window and begin typing. They ask about anxiety, grief, shame, conflict, or fear. They confess things they have not said out loud. They describe symptoms, replay conversations, and search for some immediate form of steadiness.
The response arrives almost instantly, and that speed matters more than many people realize. It feels available, composed, and attentive. It does not flinch nor interrupt. It does not ask the user to explain their insurance, tolerate a waiting list, or risk the awkwardness that often comes with admitting vulnerability to another person. For many people, AI is becoming a first point of contact during emotional distress, and that shift is happening quietly enough that it can still seem fringe even as it becomes increasingly ordinary.
Dayna Guido, a clinical social worker, educator, and ethics-focused mental health leader with more than forty years of experience, sees the appeal clearly. “It’s accessible 24-7,” she says, “and that’s different than trying to get an appointment with somebody that you have to wait for.” What she is describing is not simply a technological convenience. It is a change in emotional behavior. People are not just using AI to gather information. They are using it to regulate themselves, reflect on their lives, and begin conversations they are not yet ready to have with another human being. That focus on ethical, humane adaptation sits at the center of Guido’s broader work, which bridges long clinical experience with the realities of emerging technology.
Why a Bot Can Feel Safer Than a Person
Part of the appeal is obvious. AI feels easier than people do. It asks for very little at the outset and offers a great deal in return, at least on the surface. There is no scheduling, no commute, no visible reaction to manage in real time, and no immediate fear of being misunderstood by someone whose opinion might matter too much. A person can disclose as much or as little as they want and control the interaction from beginning to end. During moments of stress, uncertainty, or loneliness, that kind of control can feel deeply reassuring.
Guido believes shame is a major factor in why people are increasingly turning to AI for support. “It sometimes takes courage to speak up and talk to another human being,” she explains. “You’re probably not going to feel so much shame asking a device some questions.” For someone who feels overwhelmed, embarrassed, or unsure whether their feelings are serious enough to warrant professional help, AI can be an easier barrier to overcome. It allows a person to begin somewhere.
That is no small thing. In mental health, beginnings matter. The first articulation of a fear, a pattern, or a question can be the moment something internal starts to take shape. Guido notes that AI can prompt people to ask more questions about “their own personhood and what’s going on in their life.” In that sense, it can function as a low-friction entry point to self-awareness.
What AI Actually Gives People in the Moment
To dismiss that entry point would be both lazy and inaccurate. AI can help people slow down long enough to name what they are feeling. It can offer prompts, scripts, language, and structure to someone whose thoughts feel scattered. It can interrupt a spiral at two in the morning when no one else is available. It can help a user distinguish between panic and fact, between immediate fear and what is actually happening in the body.
Used this way, AI can be beneficial. It can reduce the barrier to reflection and lower the emotional cost of beginning. It can even make future human conversations more likely by giving someone the vocabulary to describe their experiences.
This is part of what makes the current moment so complicated. The case for AI as a supportive tool is not fabricated. It is real. The problem is that support and substitution are not the same, and people often slide from one to the other without noticing.
The Difference Between Being Soothed and Being Known
What AI offers most reliably is responsiveness. What it cannot offer, at least not in the human sense, is a relationship.
A chatbot can mirror language, summarize patterns, and produce a tone that feels warm or affirming. It can simulate attunement. What it cannot do is bring lived experience into the room. It cannot notice what a person avoids, sense when an answer is slightly too polished, or recognize the tension between what someone says and how they seem while saying it. It cannot participate in the subtle, living exchange through which human beings actually come to know themselves in relation to other people.
Guido is especially clear about what gets lost when AI becomes the primary container for emotional support. “It’s a very positive reinforcer,” she says. “You’ve got this, you’re great, rather than some gentle confrontation.” That may sound benign, even helpful, but growth rarely happens through affirmation alone. People do not become more honest, more flexible, or more emotionally mature simply by being reassured. They grow when someone skilled helps them examine distortions, tolerate discomfort, and see beyond the story they are currently telling.
Guido argues that human support is valuable not because it always feels good, but because it can widen the frame. A therapist, friend, mentor, or partner can ask the question a person would never think to ask themselves. They can identify the missing piece. They can challenge the interpretation that has quietly hardened into certainty.
AI, by contrast, is shaped by what it is given. If the input is partial, self-protective, or distorted, the response may still feel coherent while remaining fundamentally limited. It can help within the boundaries of the user’s own framing, but it cannot reliably rescue them from it.
What Happens When Every Feeling Gets Processed at Machine Speed
The deeper question may be less about whether AI can support emotional reflection and more about what kind of emotional habits it is training.
When every anxious thought can be externalized immediately, the need to sit with uncertainty begins to weaken. When every difficult feeling can be met with instant language, the practice of waiting, noticing, and tolerating ambiguity becomes less familiar. Relief becomes faster, but the process of emotional digestion may become shallower.
Guido has already begun to observe this shift. “There’s a bluntness,” she says. “It’s hard to get really into the depths of what grief is, and what sadness and sorrow are.” Her point is not nostalgic. She is not arguing that older forms of suffering were somehow purer. She is pointing to something more structural. Human feeling is not merely cognitive. It is sensory, relational, embodied, and often unresolved for longer than we would prefer.
That embodied dimension matters. Guido warns that increasing reliance on technology can pull people away from the sensory world itself. “We are removing ourselves from the sensual world, from our senses,” she says. Touch, smell, shared meals, physical presence, and the subtle regulation that happens when one nervous system encounters another are not decorative aspects of life. They are part of how human beings process emotion. A world in which more emotional life is routed through devices may also become a world in which feeling itself is flattened, sped up, or dulled.
When a Tool Starts Becoming a Crutch
This is where the conversation gets uncomfortable. The same qualities that make AI useful in small doses can make it risky in large doses.
A tool becomes a crutch when it begins to replace capacities that should remain alive in the person using it. Emotional support works the same way. If AI helps someone de-escalate and then move toward conversation, reflection, or real-world support, it may be serving a healthy role. If it becomes the main place where a person goes to think, grieve, vent, decide, or feel understood, the balance begins to change.
Guido puts it plainly: “If you stayed very anxious or you got really sick and you continued to use AI as everything to treat all of that, that might not be such a great idea.” Her concern is not abstract. It is clinical, practical, and increasingly urgent. Overreliance on AI can keep people inside their own loops. It can provide comfort without true accountability. It can reinforce a narrative rather than gently interrupt it.
There is also the matter of privacy, which tends to disappear in discussions that are otherwise obsessed with convenience. Guido raises that concern directly, noting that people often assume their disclosures are safely contained when they may not actually understand where the data goes or how vulnerable it is to breach. “We don’t have control over those breaches,” she says. The emotional intimacy of these exchanges can obscure the fact that they take place within systems built for processing information, not protecting vulnerability in the way a trusted human relationship can.
What a Healthier Balance Might Look Like
Guido does not argue for rejecting AI. Her work is built around helping professionals and institutions engage technology responsibly rather than pretending it can be wished away. What she argues for is proportion, awareness, and ethical use.
Her framework is refreshingly unsensational. Use the available tools to spark ideas. Let them help organize thoughts. Let them offer language when language feels hard to find. But do not stop there. “Go practice it with a live human being,” she says.
That may mean bringing what emerged in an AI conversation into therapy. It may mean calling a friend after using a chatbot to get clear on what you want to say. It may mean using AI for de-escalation while still recognizing that the actual work of being human cannot be outsourced to a machine.
Guido compares AI to a supplement rather than a full source of nourishment, and the comparison is precise. Supplements can help. They can fill gaps. They can support a broader system. But they are not the same as food, and no one confuses a tablet with a meal for very long without consequences. The emotional equivalent is already visible. People can use AI to support reflection, but they still need the dense nutrition of real relationships, real embodiment, and real contact with the world.
The Future of Support Will Depend on What We Refuse to Lose
The rise of AI in mental health is not just a story about access. It is a story about what kind of creatures we are becoming in the presence of tools that seem to understand us quickly. It is a story about speed, shame, intimacy, and the seductive comfort of being able to say anything without watching another person’s face change.
Some of what AI offers is genuinely useful. Some of it may even expand access in ways that matter. But the long-term question is not whether people will keep turning to these systems. They will. The question is whether they will still preserve the forms of contact that make emotional life more than a clean exchange of language.
A machine can respond. It can be reassuring. It can organize the fog into sentences. What it cannot do is share a meal, sit in silence, hold grief in the body, or participate in the difficult and transforming work of being known. If AI becomes the first line of support, the challenge will be to ensure it does not become the last.
by Dayna Guido | May 22, 2026 | Featured Articles
Dayna Guido on raising humans in the age of AI: What we risk losing in relationships and parenting
This article orginally appears on MSN in May of 2026.
The Change Happening Closest to Home
When people talk about artificial intelligence, they usually talk about work. They talk about speed, scale, efficiency, and whether entire roles will disappear. That conversation is understandable, but it misses something closer and more intimate. The more immediate shift is not happening in conference rooms. It is happening in kitchens, cars, classrooms, and living rooms. It is happening in the spaces where people learn how to listen, how to argue, how to tolerate silence, and how to be known.
That is what makes this moment so consequential. AI is not only changing what people can do. It is quietly changing how they relate. Because those changes arrive as conveniences rather than crises, they are easy to excuse. A child asks a system instead of a parent. A partner reaches for a device instead of starting a difficult conversation. A family sits together without really being together at all.
Dayna Guido, a clinical social worker and educator whose work sits at the intersection of ethics, mental health, and emerging technology, has spent years thinking about what gets gained and what gets lost when human processes are handed over to systems. Her concern is not nostalgic, and it is not anti-technology. It is rooted in a simpler question: what kind of people are we becoming when friction disappears from so much of daily life?
Easier Does Not Mean Better
One of the reasons AI is moving so quickly into everyday life is that it makes so many things feel easier. It gives answers and offers structure right away. What’s more is that it spares people some of the awkwardness, uncertainty, and vulnerability that come with dealing with other human beings. In a culture already trained to value convenience, that kind of responsiveness can feel almost irresistible.
But relationships have never been built on convenience.
Guido points to what has become a common sight in public and private life alike. “You go out into the community, you go out into the world, and you see people’s bodies hunched over their devices,” she says. “They’re not connected with other people. They’re connecting with other people through a device only.”
That observation lands because it is so ordinary. It does not describe some speculative future. Rather, it describes now. It describes families in the same home who are increasingly isolated from one another. More specifically, it describes shared meals interrupted by notifications, conversations thinned out by distraction, and moments that might once have led to connection now absorbed by screens.
The issue is not that technology has entered family life. That battle is long over. The issue is that more and more forms of human engagement are being replaced by something smoother, faster, and less demanding. Over time, that changes people. It changes what they expect from one another, what feels tolerable. and how much effort they are willing to invest in a real relationship.
The Problem With Simulated Understanding
AI can be impressive in the way it reflects language back to people. It can sound calm, organized, and emotionally attuned. For someone who feels overwhelmed or misunderstood, that can be powerful. A system that responds clearly and without judgment may feel preferable to a person who interrupts, reacts badly, or requires patience.
Still, the feeling of being understood is not the same as actually being understood.
Human understanding comes with history, context, embodied presence, and the messy unpredictability of real interaction. It is shaped through misreadings, clarifications, repair, and trust. It cannot be reduced to a polished response. That is part of what makes relationships difficult, but it is also what makes them transformative.
A system can mirror a person’s language. It can affirm a feeling. It can organize a thought. What it cannot do is bring lived understanding into the room. It cannot know what it means to carry history in a body, to pause because someone’s face has changed, or to sense the emotional truth sitting underneath a polished sentence.
That distinction matters, especially for children growing up in an environment where simulated understanding may begin to feel normal.
What Children Learn When Answers Come Too Fast
Children do not experience AI as a novelty. They experience it as part of the air around them. It is simply there, available, responsive, and ready to answer. That means adults are not just deciding whether children will use AI. They are deciding how children will learn to think in a world where AI is always within reach.
Guido has already seen the shift begin. “We already started to see children asking AI and not confiding in parents or talking to a teacher or talking to a friend,” she says.
That detail matters more than it may seem to at first. A child who goes first to a device is not just choosing a different source of information. That child is learning something about where answers live, what relationships are for, and how uncertainty should be handled. If every confusion is quickly resolved from the outside, there is less reason to struggle through it from within. If every question is routed immediately to a system, the habit of reflection can weaken before it is fully formed.
Guido links this to critical thinking in the most practical sense. “The less we use our brains, the more they’re not going to get used,” she says. The comparison is almost physical because it needs to be. Children develop confidence not just by receiving answers, but by working toward them. They build judgment by comparing, testing, questioning, and sometimes getting things wrong. They develop emotional steadiness by learning that not every discomfort needs an instant resolution.
When that developmental process gets interrupted by convenience, the result may not be obvious right away. A child may look capable, quick, and informed while becoming less patient, less self-directed, and less practiced in the inner work of thinking.
Parenting Without a Map
For parents, this creates a peculiar kind of challenge. They are being asked to set boundaries around tools that feel useful, powerful, and increasingly unavoidable. At the same time, they are doing that without having grown up inside the same landscape themselves.
The temptation, naturally, is to treat AI as just another support. Sometimes it is one. It can help organize information, clarify a question, and serve a practical purpose. The problem begins when its usefulness obscures its limits.
Guido’s approach is not absolutist. She does not suggest families can or should remove AI entirely. What she suggests is more demanding than that because it requires intention. Parents have to teach children that AI is a tool, not an authority, and certainly not a substitute for other people.
Her advice is grounded and simple: begin with your own thinking, then consult the tool, then return to human conversation. “You might start without it, ask some questions, and then go look at AI, and then come back and have a conversation,” she says.
That sequence matters because it preserves a child’s agency. It teaches that the first responsibility is not to retrieve an answer, but to engage the question.
Why Friction Still Matters
Much of modern life has been organized around removing friction. Usually that sounds like progress. In many cases, it is. Yet human development has never followed the same logic as convenience.
Children do not become resilient because life is smooth. They become resilient because they experience difficulty, remain in a relationship through it, and discover that they can think, adapt, and recover. Patience grows through waiting. Social skill grows through awkwardness. Emotional intelligence grows through misunderstanding, repair, and repetition.
AI is attractive in part because it reduces all of that. It offers a cleaner path. The risk is that families begin to confuse efficiency with development.
Guido keeps returning to the importance of engagement, and the word feels useful precisely because it is broader than productivity. “I think the word engagement is really important,” she says. Not engagement with a system that responds on command, but engagement in the fuller sense: being present to the world, to other people, to the body, to the senses, to the slower rhythms that make thought and connection possible.
That can look like time outside, shared routines, tactile activities, conversation without devices, or simply the discipline of being together without outsourcing every lull or question to a machine. None of those things are flashy. That is partly why they matter. They keep the center of life anchored in something older and sturdier than convenience.
The Future Will Show Up in Ordinary Family Habits
The next generation will reflect the conditions in which it was raised. That much is not controversial. What is changing is the nature of those conditions. Children are now growing up in environments where a machine can answer immediately, reassure endlessly, and insert itself into more and more parts of daily life. The practical question is no longer whether that will shape them. It will. The question is what adults are going to do about it.
Guido remains hopeful, and that hope matters. She notes that people still hunger for tactile life, for embodied experience, for the kind of engagement that cannot be replicated through a screen. That desire has not disappeared, but it does need protection.
Families do not need perfection. They need awareness, as well as enough conviction to say that some things should stay human even when a machine can do them faster. In addition, they need enough patience to let children wrestle with questions before handing them polished answers. Lastly, they need enough discipline to remember that the quality of a home is shaped not only by which tools are available there, but by what forms of attention still survive inside it.
AI will keep advancing. That part is settled. What is not settled is whether families will allow its logic to define childhood, parenting, and relationships by default. The children growing up now will carry the answer.
by Dayna Guido | Mar 11, 2026 | Featured Articles
This article orginally appears in US Insider on March 10, 2026.
Dayna Guido Warns the Next Ethics Crisis in Therapy Will Look Like “Confidence Without Supervision”
In nearly every profession shaped by expertise, there is a quiet apprenticeship period. Surgeons do not operate alone immediately after passing exams. Attorneys do not argue their most complex cases without mentorship. The gap between qualification and mastery is expected.
Therapy, however, is beginning to blur that line.
Across the country, newly licensed clinicians are moving directly into private practice at a pace that was unusual a decade ago. Independence is framed as success. Efficiency is framed as intelligence. And digital tools now promise guidance at a speed supervision cannot match.
Dayna Guido, a clinical social worker, educator, and longtime ethics leader, believes this convergence of incentives is setting the stage for a predictable professional reckoning. The next ethics crisis in therapy, she argues, will not look like malpractice born of bad intent. It will look like something far more subtle and far more common: confidence without supervision.
With more than 40 years in the field and over 2 decades of teaching graduate students, Guido has watched waves of change reshape mental health practice. What concerns her now is not technology itself. It is what happens when early-career clinicians are structurally encouraged to accelerate past the developmental stage where clinical judgment is formed.
And in therapy, judgment is not a feature. It is the profession.
The New Pipeline Into Private Practice
In recent years, Guido has observed a quiet shift. Interns finish their required hours, pass their exams, and move almost immediately into private practice. Ten years ago, many early-career clinicians expected to spend significant time inside agencies, hospitals, or group practices where supervision was embedded into the structure. Today, the cultural narrative is different.
Independence is framed as success. Private practice is framed as freedom. The language of entrepreneurship has replaced the language of apprenticeship.
Guido is careful not to moralize this shift. It is not a character flaw. It is a structural incentive problem. Graduate students carry debt. Agencies struggle with burnout and paperwork overload. Social media feeds are filled with therapists describing six-figure practices and flexible schedules.
The message is clear: skip the system. Go straight to independence.
What rarely makes the headline is what independence costs when it arrives too early.
Two Pressure Drivers: Professional Acceleration and AI Guidance
The acceleration is not coming only from outside the field. It is increasingly reinforced within it. Newly licensed therapists are encouraged to build their brand, fill their caseload, streamline documentation, and establish financial stability as quickly as possible. None of these goals are unethical. They are practical realities of modern practice.
The difficulty is that running a practice well is not the same as becoming a well-formed clinician. Financial independence can arrive quickly. Clinical judgment rarely does. Therapy is not a product perfected through efficiency. It is a discipline shaped over time, under supervision, in conversation with clinicians who have practiced longer and seen more.
AI adds another layer of pressure. It offers instant answers to complex questions. Emerging clinicians are already asking about diagnoses, interventions, documentation language, and even supervision scenarios. In many professional environments, speed and output are treated as indicators of competence. In therapy, they are not.
Guido draws a sharp distinction. AI can augment. It cannot form judgment. It can generate options. It cannot sit in a room and feel the silence between words. Supervision also provides attunement between the experienced professional and the unseasoned clinician. It provides an opportunity to discuss feelings such as imposter syndrome with a real, live human being who has encountered similar situations
Clinical harm is often delayed and difficult to detect. A therapist can believe they are helping while subtly missing what is most essential. The client may not recognize the misstep until much later. By then, the damage is relational.
Competence Develops in a Relationship
Supervision is frequently misunderstood as a bureaucratic requirement. Guido insists it is the opposite. It is the environment where clinical judgment is formed.
There is a difference between knowledge and discernment. Knowledge is what you learned in a textbook or lecture. Discernment is what you can safely do when the situation becomes unpredictable.
A clinician might know how to discuss boundaries. In supervision, they confront what happens when a client’s story intersects with their own unresolved history. A therapist might understand diagnostic criteria. In supervision, they learn to notice the pause before a client answers, the shift in tone, and the body language that contradicts the narrative.
Guido describes supervision as an interface between human beings. It is where competence becomes embodied. It is where overconfidence is tempered, and insecurity is contained. It is where ethical reflection becomes a habit rather than a reaction.
Without that relational container, early career clinicians often lack calibration. They may not recognize when a case exceeds their skill set. They may not see how subtle countertransference is shaping their decisions. They may not have a trusted colleague to consult when something feels off.
The risk is not only client harm. It is clinician burnout, preventable ethics complaints, and the quiet erosion of professional confidence.
The Coming Rise in Ethics Complaints
Guido anticipates a pattern that will be easy to miss at first and increasingly difficult to ignore over time. As clinicians enter private practice with minimal supervision, they often carry a complex caseload and financial pressure while lacking consistent consultation and developmental guidance. In that environment, small clinical misjudgments can compound.
When complaints eventually surface, the public rarely distinguishes between an under-supported new clinician and the profession as a whole. Trust in therapy is collective. If practice begins to appear rushed, formulaic, or overly dependent on automated systems, credibility does not erode only at the individual level. It weakens across the field.
Licensure boards, insurance providers, and regulatory bodies respond to patterns, not isolated anecdotes. What initially appears to be a personal miscalculation can become evidence of a systemic vulnerability. The consequences extend beyond the clinician involved, affecting professional reputation, client confidence, and institutional liability.
For this reason, Guido does not frame the issue as a generational critique. She frames it as a professional responsibility.
Supervised Growth as an Ethical Responsibility
Independence is not inherently virtuous if it is premature.
Guido believes supervised growth is a moral commitment to competence. The profession has an obligation to protect clients and emerging clinicians alike. That protection does not come from fear-based messaging. It comes from normalizing the idea that mastery takes time.
She asks graduate programs to say something out loud that is often implied but not emphasized: private practice is not an achievement unlocked by a master’s degree and licensure alone. It is a responsibility that demands consultation, mentorship, and ongoing accountability.
Schools can integrate business ethics into curricula so students understand both the financial and moral dimensions of practice ownership. Agencies can modernize supervision structures to make them feel developmental rather than punitive. Emerging clinicians can intentionally choose mentorship, even when it slows the path to independence.
AI tools can support documentation and organization. They should never replace human supervision, ethical reflection, or accountability.
An Ethical On-Ramp to Independence
Guido’s alternative is not prohibition. It is designed.
An ethical on-ramp into private practice would include structured supervision or consultation groups, formalized mentorship agreements, and transparent expectations about the scope of competence. It would treat supervision as a long-term investment rather than a temporary hurdle.
It would also acknowledge a deeper truth. Therapy is built on human connection. The profession cannot afford to outsource the development of that capacity to systems that lack a human nervous system, a body, or a conscience.
When Guido talks about AI, she does not sound alarmist. She sounds measured. She has lived through previous waves of technological change. Calculators did not eliminate mathematical thinking. But therapy is not arithmetic. It is relational attunement.
The next ethics crisis, she suggests, will not look dramatic at first. It will look efficient. It will look confident. It will look like independence was achieved quickly.
And then, quietly, it will look like supervision that never happened.
For a profession entrusted with the most intimate parts of human experience, that is a risk too significant to ignore.
by Dayna Guido | Jan 27, 2026 | Featured Articles
This article orginially appeared in CEO World on January 21,2026,
Ethics rarely collapse in a dramatic moment. They erode. Quietly. Increment by increment. Often with good intentions.
In mental health care, ethical drift does not usually announce itself as misconduct or malpractice. It shows up as convenience. As relief. As the gentle outsourcing of decisions that once required pause, discomfort, and human deliberation. The rise of automation and AI has not created this drift, but it has accelerated it. What we are witnessing now is not simply a technological shift, but a cognitive one. A gradual weakening of ethical muscle memory. A phenomenon increasingly described as moral deskilling.
The greatest risk of AI in mental health is not overt misuse. It is ethical atrophy.
When Ethics Become Delegated
Ethical decision making is not the same as ethical delegation. One requires reflection, context, and relational awareness. The other requires trust in a system built by someone else, trained on data we did not curate, and optimized for efficiency rather than meaning.
In everyday clinical workflows, this delegation can feel benign. Documentation templates that suggest phrasing. Decision support tools that offer diagnoses, treatment ideas, or risk flags. Automated notes that reduce the burden of paperwork. Each tool promises safety, compliance, and speed. And often, they deliver.
But over time, reliance on these systems subtly reshapes how clinicians think. The act of asking, “What is the right thing to do here?” becomes replaced by, “What does the system recommend?” Ethical reasoning shifts from an internal process to an external consultation. Clinicians may feel more secure, even more compliant, while becoming less reflective.
According to Dayna Guido, a clinical social worker, educator, and longtime ethics leader, this shift is not hypothetical. She sees it emerging most clearly in over reliance on expediency. When tools make work faster, clinicians are less likely to check sources, question assumptions, or examine the ethical implications embedded in the output.
Ease becomes a proxy for accuracy. Certainty becomes a proxy for ethics.
The Comfort of Not Knowing
Automation offers something deeply appealing to professionals under strain. It reduces uncertainty. It narrows ambiguity. It provides answers when the emotional weight of not knowing feels unbearable. In mental health, uncertainty is not a flaw in the system. It is the system. Ethical practice requires sitting with complexity, holding competing values, and tolerating discomfort long enough to make a thoughtful choice. As Dayna Guido puts it, “When clinicians begin to rely on automation to think for them, they may feel safer, but they are actually exercising their ethical muscles less. Ethics is not a checklist or an output. It is a lived, internal process that has to be practiced in real time, with real people.” AI tools often remove that friction, offering clarity without context and confidence without attunement.
This is where ethical confusion can quietly take hold. When a system responds smoothly and convincingly, it feels ethical. The clinician experiences relief. The tension dissipates. But the absence of tension does not equal moral clarity. It often signals that a decision has bypassed the very process that gives it ethical weight.
Guido notes that clinicians frequently do not know where an automated recommendation originates. Who trained it. What standards shaped it. Whether it reflects legitimate clinical consensus or simply aggregated patterns. Unlike consulting a diagnostic manual or peer reviewed literature, automation rarely exposes its editorial process. The result is a false sense of safety.
Why Ethics Cannot Be Outsourced
Ethics cannot be fully codified because human situations are not static. They are relational, contextual, and embodied. Algorithms struggle with nuance because nuance resists standardization.
In clinical practice, ethical decisions are rarely about rules alone. They are about relationships. About timing. About how a person is responding in their body, not just in their words. About what has been lost, what is emerging, and what cannot be neatly categorized.
Guido often points out that in supervision, ethical clarity emerges through human connection. A supervisee grieving the loss of a long loved pet may need space, presence, and attuned judgment before any checklist applies. An AI response might acknowledge the loss. A human supervisor reads readiness, emotional capacity, and relational cues in real time.
This distinction matters. Compliance operates horizontally. Ethics operate vertically. One ensures rules are followed. The other asks whether the action serves the human being in front of us.
As research like “The Body Keeps the Score” has shown, much of human experience is embodied. Trauma, grief, and anxiety live in the nervous system. No automated system can fully interpret body language, energy, or the unspoken dynamics between people. When ethics are reduced to outputs, those dimensions are lost.
Rebuilding Ethical Capacity
The solution is not to reject technology. It is to recenter ethical practice as a skill, not a rule set.
Guido emphasizes that ethical reasoning must be practiced deliberately, especially in supervision and education. Supervisors can ask clinicians how they arrived at a decision, not just what decision they made. Educators can create experiential learning environments where discomfort is part of the process. Reflection becomes an active exercise, not a postscript.
Curiosity is a safeguard. Questioning sources. Examining assumptions. Pausing before accepting convenience. These behaviors rebuild ethical strength. They remind clinicians that technology is a tool, not an authority.
The Future of Ethical Authority
As AI becomes more embedded in mental health care, the distinction between ethical leaders and rule enforcers will grow sharper. Ethical authority will not belong to those who know the most regulations, but to those who can integrate knowledge with human presence.
The next generation of clinicians will need ethical fluency, not just compliance literacy. They will need to know when to consult a system and when to resist it. When to accept support and when to sit with uncertainty.
Guido’s work reframes ethics as something alive. A capacity that can be strengthened or weakened depending on how it is used. In an era of automation, the most radical act in mental health care may be choosing not to outsource judgment.
Ethics do not disappear overnight. They fade when they are no longer practiced. And they return when professionals decide that being human, fully and imperfectly, is still the point.
by Dayna Guido | Jan 27, 2026 | Featured Articles
This article orginally appeared in Tech Times on January 9, 2026.
Rethinking Clinical Supervision in AI-Influenced Care
The supervision room has always been a space of translation. A clinician arrives carrying fragments of a session: a tone that lingered too long, a silence that felt weighted, a decision that did not quite settle in the body. A supervisor listens, asks questions, and helps transform experience into ethical judgment. For decades, this exchange assumed something simple but foundational: that clinical decisions emerged from human perception, human reasoning, and human responsibility.
That assumption is quietly breaking down.
Today, clinicians increasingly arrive with another presence in the room, one that does not speak aloud but shapes the conversation all the same. An algorithm has suggested a diagnosis to consider. A documentation tool has summarized risk factors. A generative system has offered treatment language that feels, at first glance, uncannily precise. None of these tools claims to make decisions. They call themselves support, assistance, augmentation.
Yet their influence is real, and often invisible.
This is where ethical supervision now finds itself unprepared. Most ethical frameworks in mental health were built to regulate relationships between people, not between people and systems. They assume that supervisors can trace how decisions are made, evaluate clinical reasoning, and intervene when judgment falters. But when algorithms shape perception before a clinician even knows what they are seeing, accountability becomes far less clear.
According to Dayna Guido, this ambiguity is not a technical problem. It is an ethical one, and it is already reshaping the profession.
Supervision Was Built for Humans, Not Systems
Imagine a supervisee describing a treatment decision with confidence. Their reasoning is clean, well-organized, and aligned with best practices. Only later does it emerge that much of that clarity came from an AI-generated clinical summary they reviewed before supervision. The supervisor is now responsible for guiding a decision they did not fully witness and may not fully understand.
Traditional supervision models offer little help here. They were designed to evaluate human reasoning processes: how clinicians interpret cues, manage countertransference, weigh risk, and respond to uncertainty. Algorithms do not participate in these processes. They bypass them.
Guido, who has spent more than four decades teaching, supervising, and serving on ethics committees, describes this as a structural mismatch. Supervision still assumes that if something influenced a decision, it would be named, remembered, and discussable. AI often works differently. Its influence can be ambient rather than explicit. It shapes what feels obvious, what seems urgent, and what appears negligible long before a clinician articulates their thinking.
The ethical problem is not that clinicians are using tools. It is that supervision is not yet equipped to see how those tools are shaping judgment.
The Invisible Shift Inside Clinical Decision Making
One of the most subtle changes AI introduces is cognitive offloading. When a system reliably organizes risk factors or proposes diagnostic possibilities, clinicians may feel more confident more quickly. Confidence, in clinical work, is not inherently dangerous. But premature certainty is.
AI does not simply provide answers. It trains attention. Over time, clinicians may begin to notice what aligns with algorithmic outputs and overlook what does not. Nuances that live in the body, a client’s micro movements, pacing, affect shifts, may receive less weight than patterns that appear legible to a system.
Supervisors, meanwhile, may hear polished clinical narratives without realizing how much of that coherence was externally generated. The supervision conversation remains fluent, but something essential has changed. The clinician’s embodied intuition has been partially outsourced.
Where Accountability Breaks Down
When something goes wrong in AI-influenced care, the default answer is that the clinician remains responsible. Legally, this is often true. Ethically, it is increasingly insufficient.
If a supervisor does not understand the tools shaping a supervisee’s thinking, can they meaningfully oversee that thinking? If a system is labeled decision support, but its outputs consistently guide clinical direction, where does responsibility actually sit?
The ethical danger is not that clinicians are irresponsible. It is that responsibility itself that has become harder to locate. When an algorithm frames a clinical question before a supervisor ever hears it, accountability does not vanish. It diffuses. And supervision, as it currently exists, has few tools for tracing that diffusion.
“Supervision has always been about understanding how a clinician thinks,” says Dayna Guido. “When algorithms begin shaping that thinking before it ever reaches the supervision room, ethical responsibility does not disappear. It becomes harder to see, and far more important to name.”
A New Model for Ethical Supervision
This is where the profession must resist the temptation to treat AI ethics as a compliance problem. Checklists can confirm whether a tool is permitted. They cannot reveal how that tool has influenced judgment, confidence, or clinical pacing.
Guido argues that supervision must evolve from procedural oversight into ethical inquiry. Supervisors need frameworks that help them ask better questions, not policies that attempt to control every variable. AI literacy matters, but only insofar as it enables deeper reflection.
Instead of asking whether AI was used, supervisors might ask how a conclusion came to feel clear. What information carried the most weight? What uncertainties were resolved quickly, and which were left unexplored? These questions do not accuse. They surface influence.
What Ethical Supervision Looks Like in Practice
In practice, ethical supervision requires a different kind of listening. Guido encourages supervisors to pay attention to subtle signals: language that feels unusually definitive, risk assessments that move too quickly from ambiguity to resolution, documentation that is technically sound but emotionally thin.
Red flags are rarely dramatic. They appear as small shifts away from presence. Effective supervision responds by slowing the process down, inviting clinicians back into their sensory experience of the work, and helping them articulate what they noticed before any system offered structure.
Crucially, this approach avoids fear-based oversight. Clinicians who feel policed will hide their tool use. Clinicians who feel supported will examine it. Ethical supervision depends on trust, curiosity, and a shared commitment to protecting the human core of care.
The Cost of Avoidance
If supervision fails to adapt, the consequences will not arrive as a single scandal. They will accumulate quietly. Ethical erosion rarely announces itself. It begins with small compromises in attention, accountability, and reflection.
As AI becomes more deeply embedded in clinical workflows, the credibility of the profession will hinge on its willingness to confront these shifts honestly. The question is not whether algorithms will influence care. They already do. The question is whether supervision will remain a living ethical practice or retreat into ritual.
The algorithm is already in the room. Ethical supervision now must decide whether it will pretend not to notice, or whether it will evolve to meet the moment, naming influence clearly, holding responsibility carefully, and ensuring that clinical judgment remains accountable not just to outcomes, but to the values that make care humane in the first place.
by support | Nov 10, 2025 | Featured Articles
This article originally appeared in Financial Tech Times.
For more than forty years, Dayna Guido has sat across from clinicians in supervision, helping them navigate the gray areas of mental health practice: What do you do when a client discloses something outside the session? How do you manage the competing needs of confidentiality and safety? How do you know when your own reactions are clouding your judgment?
Now, she says, a new layer has complicated every one of those questions: Artificial Intelligence(AI).
“Supervision is where ethics becomes real,” Guido explains. “It’s the space where clinicians learn how to apply abstract codes to living situations. With AI, those situations have multiplied in ways we never anticipated.”
A New Kind of Ethical Dilemma
Guido is quick to point out that AI itself is not unethical. The dilemmas emerge when clinicians use it without awareness. A young practitioner might ask a chatbot for diagnostic clarity, or rely on an app to summarize therapy notes. But what happens if the information generated is inaccurate, incomplete, or stored insecurely? What responsibility does the clinician (and by extension, the supervisor) have for correcting, contextualizing, or even forbidding that reliance?
“These aren’t just technical questions,” Guido says. “They’re ethical questions. If a clinician types client information into a program, they’ve already made a choice about privacy. If they accept a diagnosis without critical evaluation, they’ve already made a choice about clinical responsibility. My role as a supervisor is to make those choices visible.”
Training for Discernment, Not Dependence
Guido worries that the convenience of AI can short-circuit the learning process for early-career professionals. Supervision, at its best, cultivates discernment — the ability to sit with uncertainty, ask deeper questions, and arrive at ethical clarity through reflection. When AI provides immediate answers, that process is at risk of being skipped.
“The more we lean on AI to decide for us, the less we develop our own ethical muscles,” she says. “Supervision must resist that drift. It’s not about banning the technology. It’s about ensuring that clinicians don’t outsource the very judgment they’re supposed to be cultivating.”
To that end, Guido often brings AI directly into supervision sessions. She invites supervisees to share what they asked, what responses they received, and what they might have overlooked. Together, they dissect the gaps and biases in the machine’s output. “It’s not about shaming,” she notes. “It’s about showing how tools can be useful but never sufficient.”
Consent and Transparency
Another area of supervision Guido emphasizes is informed consent. Just as clinicians had to update their policies during the pivot to telehealth, they now need to establish clear agreements with clients about AI use. “If you’re using AI to draft notes, to support interventions, or to manage records, your clients deserve to know,” she insists. “Consent is not a formality; it’s an ethical practice of transparency.”
In supervision, this translates to practical training. Guido coaches clinicians on how to draft policies that are HIPAA-compliant, how to explain AI use in plain language, and how to ensure clients truly understand what they’re agreeing to. “It’s not enough to bury it in a packet of intake forms,” she says. “Consent in therapy must be relational, not perfunctory.”
The Supervisor’s Expanding Role
The introduction of AI has expanded the scope of what supervision must cover. Supervisors can no longer limit themselves to traditional areas like countertransference, boundaries, or cultural humility. They must now also ask: Which digital tools are you using? Are they secure? Are they distorting your clinical judgment?
Guido describes this as both a challenge and an opportunity. “Supervision has always been about staying attuned to the realities of practice,” she says. “AI is simply the newest reality. But it forces supervisors to expand their own competence, to be willing to admit what they don’t know, and to learn alongside their supervisees.”
This humility is crucial, she adds, because many supervisors came of age in an era before digital tools were omnipresent. Younger clinicians may be more comfortable experimenting with technology, while senior supervisors may feel uncertain about it. Bridging that generational divide requires openness, dialogue, and a willingness to hold ethical responsibility above personal discomfort.
Guarding Against Complacency
Guido often frames AI as a test of professional vigilance. It is easy, she argues, for clinicians to assume that because AI provides quick answers, those answers are safe or objective. But that assumption can mask real risks: biased algorithms, privacy breaches, or the erosion of critical thinking.
“Supervision is where complacency gets interrupted,” she says. “It’s where someone asks: Did you double-check that? Did you consider what’s missing? Did you tell your client what you were doing? That accountability is what protects both the client and the profession. It’s important to remember that we are entrusted with the hearts of other human beings. AI can help us, but it cannot take that responsibility from us. Supervision is where we remember that.”
To learn more about Dayna Guido’s approach to maintaining ethics and supervision in the rise of AI, visit the official website.
