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Sprint projectJun 22, 2026Bangladesh

Taap·AI How good is this data center for your area?

Srijit Paul, Ucchas Muhury · Team TAAP AI

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

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Report: Taap·AI How good is this data center for your area?

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Every wave of AI is built on a physical foundation that almost nobody sees: enormous buildings full of computers that drink water, draw power, and throw off heat. Taap·AI answers one question for anyone, in seconds, from open data: how good is this data center for your area? It gives developers, regulators, and ordinary residents the same honest, inspectable facts so the people who live beside this infrastructure are no longer the last to know. Every wave of AI is built on a physical foundation that almost nobody sees: enormous buildings full of computers that drink water, draw power, and throw off heat. Taap·AI answers one question for anyone, in seconds, from open data: how good is this data center for your area? It gives developers, regulators, and ordinary residents the same honest, inspectable facts so the people who live beside this infrastructure are no longer the last to know.

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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. Taap·AI offers a highly novel framing of AI safety by expanding the definition to include physical, environmental, and civic infrastructure impacts. By bridging the gap between open-source GIS data and accessible public analysis, the tool addresses a critical real-world friction point in data center development.The implementation effectively aggregates reliable open signals (like OpenStreetMap and WRI Aqueduct) into an intuitive, transparent scoring mechanism rather than a complex black-box algorithm. The inclusion of automated EIA report exports adds direct utility for local governments and community organizers.

    Suggestions for improvement:

    Expand indicator granularities, as regional or state-level data averages can obscure local realities.Introduce basic predictive templates for future climate scenarios rather than relying purely on historical reanalysis baseline data

  2. Informative project, clear methodologies.

    For data, most places show "led by grid proximity (no data)"

    Authors should have more than 1 source of data for each dimension to check data quality.

    The dimensions are rather still simple: Siting suitability - grid proximity (30%), free-cooling potential (25%), grid carbon (20%), renewable potential (15%), water availability (10%).

    Community cost - basin water stress (45%), population nearby (35%), local grid emissions (20%).

    More frameworks should be incorporated regarding health, economic, social costs (and benefits).

  3. The physical footprint of AI infrastructure is an important and under-discussed part of AI safety, especially for countries in the global south where building compute/ data center capacity is part of 'sovereignty'. The project is also practical and usable.

Cite this project

@misc{paul2026taapai,
  title = {{Taap·AI How good is this data center for your area?}},
  author = {Srijit Paul and Ucchas Muhury},
  year = {2026},
  month = jun,
  note = {Submitted to Global South AI Safety Hackathon, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/taapai-how-good-is-this-data-center-for-your-area-2srh}},
  url = {https://apartresearch.com/sprints/projects/taapai-how-good-is-this-data-center-for-your-area-2srh}
}

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