Mar 10, 2025

SafeAI Academy - Enhancing AI Safety Awareness through Interactive Learning

Arzu Guney Caner

Details

Summary

SafeAI Academy is an interactive learning platform designed to teach AI safety principles through engaging scenarios and quizzes. By simulating real-world AI challenges, users learn about bias, misinformation, and ethical AI decision-making in an interactive and stress-free environment.

The platform uses gamification, mentorship-driven pedagogy, and real-time feedback to ensure accessibility and engagement. Through scenario-based learning, users experience AI safety risks firsthand, helping them develop a deeper understanding of responsible AI development.

Built with React and GitHub Pages, SafeAI Academy provides an accessible, structured, and engaging AI education experience, helping bridge the knowledge gap in AI safety.

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Cite this work:

@misc {

title={

SafeAI Academy - Enhancing AI Safety Awareness through Interactive Learning

author={

Arzu Guney Caner

date={

3/10/25

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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