ENG3702
Assignment 2
DUE 15 July 2025
,ENG3702
Assignment 2
DUE 15 July 2025
The Illusion of Neutrality: The Dangers of Using Generative AI as Therapy by Non-
Professionals
Introduction: Framing the Issue—The Perilous Shift to AI as Therapy
Generative Artificial Intelligence (AI) tools, including prominent platforms such as
ChatGPT, Gemini, Copilot, and GROK, are increasingly being repurposed to provide
emotional support. This emergent application often operates under the implicit
assumption that these systems can serve as viable substitutes for human therapists.
This report critically examines this premise, asserting that such perceived neutrality is
fundamentally illusory and introduces substantial ethical, emotional, and practical
hazards. The growing reliance on these AI systems by individuals without professional
oversight, coupled with a limited understanding of their inherent limitations and biases,
presents significant risks to vulnerable populations and the broader mental healthcare
ecosystem.
The Illusion of Neutrality: Unpacking Algorithmic Bias in Therapeutic AI
The concept of neutrality, a cornerstone of ethical therapeutic practice, implies unbiased
and equitable treatment. However, generative AI systems inherently deviate from this
ideal. Their training on vast datasets means they reflect and frequently amplify existing
societal biases and prejudices. Furthermore, the design choices made by their creators
inevitably shape their outputs, rendering true impartiality unattainable.
Generative AI inherits biases from its training data, leading to the replication of cultural,
social, and historical prejudices rather than their transcendence. This is not merely a
theoretical concern; it manifests in tangible, potentially harmful ways within mental
health applications. For instance, Large Language Models (LLMs) utilized in mental
, health, despite their potential for assessing disorders, raise considerable concerns
regarding their accuracy, reliability, and fairness due to embedded societal biases and
the underrepresentation of certain populations in their training datasets.
Specific examples illustrate the pervasive nature of these biases in mental health
assessments. Research focusing on eating disorders, specifically anorexia nervosa
(AN) and bulimia nervosa (BN), revealed that ChatGPT-4 produced mental health-
related quality of life (HRQoL) estimates that exhibited gender bias. Male cases
consistently scored lower despite a lack of real-world evidence to support this pattern,
underscoring a clear risk of bias in generative AI within mental health contexts. This
finding is particularly troubling given the existing underrepresentation of men in eating
disorder research and the heightened risk faced by specific subgroups, such as
homosexual men. Beyond eating disorders, ChatGPT 3.5 has been observed to offer
different treatment recommendations based on a user's insurance status, potentially
creating health disparities. It also failed to generate demographically diverse clinical
cases, instead relying on stereotypes when assigning gender or ethnicity. Furthermore,
a Stanford study uncovered that AI therapy chatbots demonstrated increased
stigmatization towards conditions like alcohol dependence and schizophrenia compared
to depression. This stigmatization remained consistent across various AI models,
indicating a deep-seated issue that cannot be resolved simply by increasing data
volume. Such stigmatizing responses are detrimental to patients and may lead to the
discontinuation of essential mental health care.
The progression from biases in training data to biased outputs in LLMs, if applied in
clinical practice or by individuals seeking support, can lead to misdiagnoses,
inappropriate recommendations, or a failure to provide equitable care, thereby causing
tangible harm to vulnerable populations. This reveals that the "illusion of neutrality" is
not just a theoretical problem but a practical pathway for existing societal inequities to
be encoded, perpetuated, and exacerbated within digital mental health tools, directly
impacting patient outcomes.
The misconception of AI's impartiality further discourages users from critically evaluating
the system’s limitations. When individuals believe an AI is neutral and objective, they
Assignment 2
DUE 15 July 2025
,ENG3702
Assignment 2
DUE 15 July 2025
The Illusion of Neutrality: The Dangers of Using Generative AI as Therapy by Non-
Professionals
Introduction: Framing the Issue—The Perilous Shift to AI as Therapy
Generative Artificial Intelligence (AI) tools, including prominent platforms such as
ChatGPT, Gemini, Copilot, and GROK, are increasingly being repurposed to provide
emotional support. This emergent application often operates under the implicit
assumption that these systems can serve as viable substitutes for human therapists.
This report critically examines this premise, asserting that such perceived neutrality is
fundamentally illusory and introduces substantial ethical, emotional, and practical
hazards. The growing reliance on these AI systems by individuals without professional
oversight, coupled with a limited understanding of their inherent limitations and biases,
presents significant risks to vulnerable populations and the broader mental healthcare
ecosystem.
The Illusion of Neutrality: Unpacking Algorithmic Bias in Therapeutic AI
The concept of neutrality, a cornerstone of ethical therapeutic practice, implies unbiased
and equitable treatment. However, generative AI systems inherently deviate from this
ideal. Their training on vast datasets means they reflect and frequently amplify existing
societal biases and prejudices. Furthermore, the design choices made by their creators
inevitably shape their outputs, rendering true impartiality unattainable.
Generative AI inherits biases from its training data, leading to the replication of cultural,
social, and historical prejudices rather than their transcendence. This is not merely a
theoretical concern; it manifests in tangible, potentially harmful ways within mental
health applications. For instance, Large Language Models (LLMs) utilized in mental
, health, despite their potential for assessing disorders, raise considerable concerns
regarding their accuracy, reliability, and fairness due to embedded societal biases and
the underrepresentation of certain populations in their training datasets.
Specific examples illustrate the pervasive nature of these biases in mental health
assessments. Research focusing on eating disorders, specifically anorexia nervosa
(AN) and bulimia nervosa (BN), revealed that ChatGPT-4 produced mental health-
related quality of life (HRQoL) estimates that exhibited gender bias. Male cases
consistently scored lower despite a lack of real-world evidence to support this pattern,
underscoring a clear risk of bias in generative AI within mental health contexts. This
finding is particularly troubling given the existing underrepresentation of men in eating
disorder research and the heightened risk faced by specific subgroups, such as
homosexual men. Beyond eating disorders, ChatGPT 3.5 has been observed to offer
different treatment recommendations based on a user's insurance status, potentially
creating health disparities. It also failed to generate demographically diverse clinical
cases, instead relying on stereotypes when assigning gender or ethnicity. Furthermore,
a Stanford study uncovered that AI therapy chatbots demonstrated increased
stigmatization towards conditions like alcohol dependence and schizophrenia compared
to depression. This stigmatization remained consistent across various AI models,
indicating a deep-seated issue that cannot be resolved simply by increasing data
volume. Such stigmatizing responses are detrimental to patients and may lead to the
discontinuation of essential mental health care.
The progression from biases in training data to biased outputs in LLMs, if applied in
clinical practice or by individuals seeking support, can lead to misdiagnoses,
inappropriate recommendations, or a failure to provide equitable care, thereby causing
tangible harm to vulnerable populations. This reveals that the "illusion of neutrality" is
not just a theoretical problem but a practical pathway for existing societal inequities to
be encoded, perpetuated, and exacerbated within digital mental health tools, directly
impacting patient outcomes.
The misconception of AI's impartiality further discourages users from critically evaluating
the system’s limitations. When individuals believe an AI is neutral and objective, they
