Pith Number

pith:EIKT7DAA

pith:2026:EIKT7DAADASNOTHYHYDRLIFMHK

not attested not anchored not stored refs resolved

"It became a self-fulfilling prophecy": How Lived Experiences are Entangled with AI Predictions in Menstrual Cycle Tracking Apps

Alexandra Weilenmann, Jichen Zhu, Pelin Karaturhan, Wendy Zhou

Users of menstrual cycle tracking apps interpret their own bodies and feelings through AI predictions, even when those predictions rest on incomplete or inaccurate logs.

arxiv:2605.13261 v1 · 2026-05-13 · cs.HC · cs.AI

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\pithnumber{EIKT7DAADASNOTHYHYDRLIFMHK}

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Record completeness

1 Bitcoin timestamp

2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

✓ Portable graph bundle live · download bundle · merged state

The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

users understand their lived experiences in light of AI predictions, although these predictions can be faulty due to imperfect logging practices

C2weakest assumption

The 14 interviews plus group autoethnography sufficiently represent the range of user experiences without major selection or interpretation bias

C3one line summary

Users entangle their lived experiences with AI predictions in menstrual tracking apps, leading to self-fulfilling prophecies, limited critical awareness from UI, and isolation for non-normative users.

References

77 extracted · 77 resolved · 1 Pith anchors

[1] Lim, and Mohan Kankanhalli 2018 · doi:10.1145/3173574.3174156

[2] Saleema Amershi, Dan Weld, Mihaela Vorvoreanu, Adam Fourney, Besmira Nushi, Penny Collisson, Jina Suh, Shamsi Iqbal, Paul N Bennett, Kori Inkpen, et al. 2019. Guidelines for human-AI interaction. InPr 2019

[3] Using thematic analysis in psychology 2006 · doi:10.1191/1478088706qp063oa

[4] To Trust or to Think: Cognitive Forcing Functions Can Reduce Overreliance on AI in AI-assisted Decision-making 2021 · doi:10.1145/3449287

[5] Kelly Caine. 2016. Local Standards for Sample Size at CHI. InProceedings of the 2016 CHI Conference on Human Factors in Computing Systems(San Jose, California, USA)(CHI ’16). Association for Computing 2016 · doi:10.1145/2858036.2858498

Receipt and verification

First computed	2026-05-18T02:44:49.352203Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

22153f8c001824d74cf83e0715a0ac3a9c0de8809382289dfc876a6a5a2d7f6f

Aliases

arxiv: 2605.13261 · arxiv_version: 2605.13261v1 · doi: 10.48550/arxiv.2605.13261 · pith_short_12: EIKT7DAADASN · pith_short_16: EIKT7DAADASNOTHY · pith_short_8: EIKT7DAA

Agent API

Resolver JSON Graph JSON Events JSON Schema Signing key

Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/EIKT7DAADASNOTHYHYDRLIFMHK \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 22153f8c001824d74cf83e0715a0ac3a9c0de8809382289dfc876a6a5a2d7f6f

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "7b2c84d2f458cfac6d2d8757897bf1a67c108666ac4ba4e36df8c0f3e6358b32",
    "cross_cats_sorted": [
      "cs.AI"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.HC",
    "submitted_at": "2026-05-13T09:42:37Z",
    "title_canon_sha256": "de7509b8426f9f52117eb114bb651411d60bbd4a8e4409122e5982a5913abe4b"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.13261",
    "kind": "arxiv",
    "version": 1
  }
}