Pith Number

pith:6V4II2NK

pith:2024:6V4II2NK5TO6UCBX2E3YVOOAWH

not attested not anchored not stored refs resolved

Perception Gaps in Risk, Benefit, and Value Between Experts and Public Challenge Socially Accepted AI

Felix Glawe, Gian Luca Liehner, Luisa Vervier, Martina Ziefle, Philipp Brauner

Experts anticipate higher AI probabilities, lower risks, greater benefits, and more positive value than the public across 71 scenarios.

arxiv:2412.01459 v2 · 2024-12-02 · cs.CY · cs.AI · cs.HC

Open paper page JSON Open Graph Bundle Merged state Verified badge What is a Pith Number?

Add to your LaTeX paper

\usepackage{pith}
\pithnumber{6V4II2NK5TO6UCBX2E3YVOOAWH}

Prints a linked badge after your title and injects PDF metadata. Compiles on arXiv. Learn more · Embed verified badge

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

experts consistently anticipate higher probabilities, perceive lower risks, report greater benefits, and express more positive sentiment toward AI compared to the non-experts. Moreover, both groups apply different weighting schemes: experts discount risk more heavily relative to benefit than non-experts.

C2weakest assumption

The samples of 119 academic AI experts and 1,110 public respondents are representative of their respective populations and that self-reported ratings on the four dimensions accurately capture true perceptions without bias from question framing or social desirability.

C3one line summary

Experts rate AI scenarios as more likely, less risky, more beneficial, and more valuable than the public, applying different weightings to risk versus benefit.

References

84 extracted · 84 resolved · 3 Pith anchors

[1] Experts evaluate topics left of the line as less likely, topics right of the line as more likely 2025

[2] 5.1 Diverging Expectations for the Future of AI First and foremost, experts and the public diverged significantly in their expectations regarding the likelihood of AI developments 2021

[3] we shape our tools, and thereafter our tools shape us 1967

[4] the tools of their tools 2025

[5] In this section, we analyze the data but now treating each topic (rather than each individual) as the unit of analysis 1960

Receipt and verification

First computed	2026-06-11T01:09:10.374636Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

f5788469aaecddea0837d1378ab9c0b1d08c28f1e0c697d28928f479a073f1b9

Aliases

arxiv: 2412.01459 · arxiv_version: 2412.01459v2 · doi: 10.48550/arxiv.2412.01459 · pith_short_12: 6V4II2NK5TO6 · pith_short_16: 6V4II2NK5TO6UCBX · pith_short_8: 6V4II2NK

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/6V4II2NK5TO6UCBX2E3YVOOAWH \
  | 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: f5788469aaecddea0837d1378ab9c0b1d08c28f1e0c697d28928f479a073f1b9

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "06b3d3eb26e4ba7f6f3af82db6813285060b4af180244317468fc78a7ae619de",
    "cross_cats_sorted": [
      "cs.AI",
      "cs.HC"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.CY",
    "submitted_at": "2024-12-02T12:51:45Z",
    "title_canon_sha256": "507c64efbd2e4fda537b64f97609f729869152764d6ed3c5176c6d385f1aa122"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2412.01459",
    "kind": "arxiv",
    "version": 2
  }
}