Oct 27, 2024

Hero Journey: Personalized Health Interventions for the Incarcerated

Anthony Li, Samuel Ntow, Antonio Bandeira, Niroj Bhandari

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Summary

Hero Journey is a groundbreaking AI-powered application designed to empower individuals struggling with opioid addiction while incarcerated, setting them on a path towards long-term recovery and personal transformation. By leveraging machine learning algorithms and interactive storytelling, Hero Journey guides users through a personalized journey of self-discovery, education, and support. Through engaging narratives and interactive modules, participants confront their struggles with substance abuse, build coping skills, and develop a supportive network of peers and mentors. As users progress through the program, they gain access to evidence-based treatment plans, real-time monitoring, and post-release resources, equipping them with the tools necessary to overcome addiction and reintegrate into society as productive, empowered individuals.

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@misc {

title={

Hero Journey: Personalized Health Interventions for the Incarcerated

author={

Anthony Li, Samuel Ntow, Antonio Bandeira, Niroj Bhandari

date={

10/27/24

organization={Apart Research},

note={Research submission to the research sprint hosted by Apart.},

howpublished={https://apartresearch.com}

}

Review

Reviewer Name

Constructive critique

Criteria 1:

Advancing interpretability (Possible questions to answer in this criteria: Does the project contribute to the field of mechanistic interpretability? Does it provide new insights into understanding or steering AI model behavior? How well does it move us towards reprogramming AI models? How original and innovative is the approach?)

2.5

Criteria 2:

Relevance for AI safety (Possible questions to answer in this criteria: How important is the contribution to advancing the field of AI safety? Do we expect the results to generalize beyond the specific case(s) presented in the submission? Does the approach introduce new safety mechanisms or enhance existing ones in innovative ways?))

2.5

Criteria 3:

Methodology & presentation (Possible questions to answer in this criteria: How well is the project executed from a technical standpoint?, Is the code well-structured, documented, and reproducible?, How effectively does it utilize Goodfire's SDK/API and other provided resources?, How clearly and effectively is the research presented in the paper?, Quality of visualizations and demos (if applicable), Clarity of methodology explanation and results interpretation))

2.5

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