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Sprint projectJun 21, 2026Mumbai, India

Structural Amplifiers of AI-Induced Harm: A Five-Dimension Sector Vulnerability Framework for South and Southeast Asia

Sakshi Chaubey

Submitted to Global South AI Safety Hackathon. Sprint projects are early-stage work by participants, not Apart Research publications.

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Report: Structural Amplifiers of AI-Induced Harm: A Five-Dimension Sector Vulnerability Framework for South and Southeast Asia

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This paper proposes a five-dimension sector vulnerability framework that assesses the structural conditions under which AI deployment poses the greatest risk to users and workers across sectors in South and Southeast Asia. The framework to five sectors, namely, rural healthcare, public welfare, gig platforms, financial services, and surveillance, across six countries, i.e India, Vietnam, Indonesia, Philippines, Thailand, and Bangladesh. The paper finds that workforce vulnerability scores maximum across all five sectors, and harm visibility represents the most consistently unaddressed structural gap.

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How much would this matter for AI safety if it worked? How innovative is it? For scores of 4-5: is this actually new to the field, or replicating recent work?

Scoring guide
  1. 1Negligible. No clear problem addressed, or no meaningful novelty.
  2. 2Limited. Addresses a real problem but with a generic or well-trodden approach. Incremental at best.
  3. 3Moderate. Clear problem with a reasonable approach; some novelty in framing or method beyond routine application of existing tools.
  4. 4Significant. Important problem with an original approach, or identifies a neglected problem area. A valuable contribution others could build on.
  5. 5Exceptional. Tackles a critical AI safety problem with a genuinely novel approach, or opens a new research direction. Clear theory of change. You'd be excited to share this with researchers in the area.

How sound are methodology, implementation, and findings?

Scoring guide
  1. 1Seriously flawed. Methodology broken, results uninterpretable, or implementation doesn't work.
  2. 2Weak. Approach has significant gaps: missing validation, flawed experimental design, or incomplete implementation.
  3. 3Competent. Technically solid given the short duration. Methodology makes sense, results are interpretable, limitations acknowledged, work builds toward clear conclusions.
  4. 4Strong. Thorough methodology with convincing validation. Results clearly support conclusions. Immediately useful for future work.
  5. 5Exceptional. Ambitious scope executed rigorously. Surprising findings, novel methods, or unusually robust validation.

How clearly are work, findings, and impact potential communicated?

Scoring guide
  1. 1Incomprehensible. Cannot determine what the project is actually claiming or doing.
  2. 2Hard to follow. Key information buried, missing, or diluted by excessive length. Significant effort to extract main points.
  3. 3Clear enough. Can understand the problem, approach, and results without undue effort. Core content clearly present: problem, method, findings, limitations.
  4. 4Well presented. Easy to follow, well-structured, appropriate level of detail. Target audience would get it quickly.
  5. 5Exceptionally clear. A pleasure to read. Complex ideas made accessible. Could serve as a model for how to present this type of work.

  1. Framing's good, making the sector the unit of analysis, and scoring structural amplifiers of harm instead of specific risks, fills a real gap between country-level indices and model-level taxonomies, and the sector profiles are concrete and well-sourced (e-Sanjeevani's 282M consultations, Samagra Vedika, Aadhaar). The catch is the scale saturates. On 1-to-3, four of five sectors hit 14 or 15, several dimensions max out, and harm visibility defaults to 3 wherever the record is thin. So the tool cannot yet separate the sectors, which is its whole job. The country-disaggregated 5x6 matrix you propose is the fix, prioritize it. Add a second rater (or a written rationale per cell) to test reliability, and anchor each score level with an example to break the ceiling. Strong framing that an NGO could pick up and run.

  2. Focusing on specific industries rather than whole countries fills a genuine gap in how we measure AI risk, and grounding this in existing post-colonial research gives it real depth. The main weakness here is a "ceiling effect" in your data: when every sector scores a maximum for workforce vulnerability and nearly the same for deployment, your scale loses the power to help anyone prioritize.

Cite this project

@misc{chaubey2026structural,
  title = {{Structural Amplifiers of AI-Induced Harm: A Five-Dimension Sector Vulnerability Framework for South and Southeast Asia}},
  author = {Sakshi Chaubey},
  year = {2026},
  month = jun,
  note = {Submitted to Global South AI Safety Hackathon, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/structural-amplifiers-of-aiinduced-harm-a-fivedimension-sector-vulnerability-framework-for-south-and-southeast-asia-1l3v}},
  url = {https://apartresearch.com/sprints/projects/structural-amplifiers-of-aiinduced-harm-a-fivedimension-sector-vulnerability-framework-for-south-and-southeast-asia-1l3v}
}

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