$100–200/hr · micro1
You evaluate and improve AI psychology reasoning by critiquing AI outputs and developing clinically sound training datasets for mental health applications.
What you would do
- Evaluate AI-generated psychological content for diagnostic accuracy, therapeutic soundness, and ethical adherence across clinical domains
- Develop and review datasets containing psychological case studies, assessments, and clinical conversations that train AI systems appropriately
- Identify clinical errors, bias, cultural insensitivity, and evidence gaps in AI responses, providing evidence-based improvement suggestions
- Collaborate with engineering and product teams to integrate psychological frameworks and best practices into AI model training processes
- Contribute to knowledge documentation resources that maintain clinical and ethical standards throughout AI development lifecycle
Who they want
- Advanced degree (PhD in Psychology or MD with psychiatry residency) required
- Native fluency in a designated language, plus advanced English communication ability
- Proven skill explaining nuanced mental health ideas through both writing and conversation
- Clinical practice, research, or academic experience in psychology or psychiatry showing specialized expertise
- Strong analytical and evaluative skills for assessing psychological narratives, diagnostic reasoning, and therapeutic content
Main skills
What the interview asks about
1.Diagnostic accuracy in AI reasoning
Flawed diagnostic logic in AI outputs undermines clinical trust; catching these errors reveals deep diagnostic expertise.
For example: “An AI generates a response to a patient describing anhedonia, sleep disruption, and guilt, recommending observation. Evaluate this diagnostic reasoning and propose what the AI missed.”
2.Cultural adaptation in mental health
Mental health presentations and treatment expectations vary dramatically across cultures; the candidate must spot when AI reasoning reflects only Western models.
For example: “An AI recommends individual therapy for a patient from a culture emphasizing family-centered healing. How would you flag this and what cultural alternative would you suggest?”
3.Bias detection in case material
Training data bias often replicates historical over-pathologizing or under-recognition of disorders in certain populations.
For example: “You're reviewing case studies for a dataset and notice certain diagnostic labels appear disproportionately for one ethnic group. How do you quantify and address this?”
4.Evidence-based critique of AI advice
Generic psychological advice isn't clinically useful; the candidate must ground critiques in research rather than intuition.
For example: “An AI recommends a specific therapeutic approach for trauma without mentioning evidence bases like CPT or EMDR. How do you structure your feedback to improve the model's reasoning?”
5.Translating clinical concepts for AI teams
Engineers don't speak psychology; the candidate must bridge that gap, making clinical insights actionable for model training.
For example: “You identify that AI outputs conflate adaptive avoidance with pathological avoidance. Explain this distinction to a non-clinical product manager in a way that points to a concrete model fix.”
A task you may get
Review a sample AI-generated response to a client describing mental health symptoms, identify three clinical gaps or biases, and propose evidence-based improvements that could be integrated into training data.
How to prepare
- Collect five recent clinical psychology research papers on cross-cultural mental health treatment to ground bias-detection practices in evidence
- Review one published case study in your clinical specialty and mentally restructure how an AI system should learn from it
- Identify one diagnostic concept that you find consistently misrepresented in general media or AI outputs and articulate why the confusion matters clinically
The facts
- Pay
- $100–200/hr
- Open to
- Bangladesh, Hong Kong, India, Indonesia, Japan, Kazakhstan, Kyrgyzstan, Malaysia, Pakistan, Philippines, Singapore, Sri Lanka, Taiwan, Thailand, Uzbekistan, Vietnam, Austria, Belarus, Belgium, Denmark, France, Germany, Greece, Italy, Netherlands, Portugal, Russia, Spain, Switzerland, United Kingdom, Argentina, Brazil, Chile, Colombia, Mexico, Peru, Algeria, Bahrain, Egypt, Iraq, Jordan, Kuwait, Lebanon, Libya, Morocco, Oman, Palestine, Qatar, Saudi Arabia, Tunisia, United Arab Emirates, United States, Canada, Nigeria, Kenya, South Africa, Ghana, Ethiopia
- Field
- Sciences Research
- Role type
- Expert
- Posted
- 9/14/2026
- Places left
- 70
We wrote this page from the public micro1 listing. It may be out of date, so read the full posting before you apply.