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Source arXiv Artificial Intelligence export.arxiv.org Analyst
July 2, 2026 research research

A Contextual-Bandit Oversight Game with Two-Sided Informational Asymmetry

Frames a highly abstract, non-empirical game-theoretic model as a foundational advance for real-world AI oversight.

View original on arxiv.org

Overview

This paper introduces a theoretical model for human-AI oversight where both parties hold private information, formalizing trade-offs between trust, communication, and harm avoidance in one-shot and repeated interactions.

TL;DR

  • Models human-AI oversight with two-way private information: humans know rewards, AI knows action quality.
  • Uses contextual bandits to derive exact one-shot characterizations instead of approximating complex POMDPs.
  • Identifies a 'slab of avoidable harm' where AI knows an action is harmful but humans don’t intervene due to non-credible oversight signals.

Keywords

contextual banditasymmetric informationhuman-AI oversightCIRLavoidable harm

Narrative Frame

theoretical abstraction framing

The Fog

Spin Score

60%

Emphasizes mathematical tractability and conceptual novelty while minimizing discussion of empirical validation, implementation feasibility, or real-world deployment constraints.

What the story wants you to believe

This formal model meaningfully advances the theory of human-AI collaboration by isolating and solving a core informational problem.

What it makes harder to question

Whether the model’s assumptions reflect actual human-AI interaction dynamics or whether its solutions are implementable outside narrow theoretical conditions.

How the spin works

The story redirects attention toward process, intent, scale, mission, or future benefits instead of unresolved concerns. Watch for loaded terms such as naturally, exact one-shot characterizations, slab of avoidable harm. The distribution reads as academic distribution. A pressure point: No experimental validation or user studies.

Who Benefits If This Frame Spreads

  • academic researchers publishing in theoretical AI

    Gains if readers accept the deflect scrutiny frame without pushback

  • Cooperative Inverse Reinforcement Learning

    As foundational framework, may gain from how the story is framed

  • Oversight Game

    As foundational framework, may gain from how the story is framed

  • arXiv Artificial Intelligence

    analyst distribution benefits from engagement with this frame

Missing Context

  • No experimental validation or user studies
  • No comparison to existing oversight interfaces in practice
  • No discussion of latency, cognitive load, or scalability in real systems

Spin Types

Every story gets a Spin Verdict: a primary spin type (and secondary when the framing blends), a specific tactic name, and a score for how strongly the narrative is steered. Examples beneath each type are tactics, not separate categories.

The Cushion

— Softens negative news

Reframes setbacks, layoffs, delays, losses, or criticism as necessary transitions, efficiency moves, temporary headwinds, or strategic resets — making the downside feel smaller, more acceptable, or less alarming.

Tactics: job-loss softening · restructuring framing · efficiency framing · strategic reset · temporary headwinds

The Shield

— Deflects blame

Shifts responsibility away from the actor — toward regulators, market forces, competitors, bad actors, legacy systems, or abstract risks — while positioning the subject as reactive, responsible, or protective.

Tactics: regulatory blame shift · macroeconomic headwinds · safety framing · bad-actor framing · market-pressure framing

The Hype

— Amplifies future upside

Emphasizes breakthrough potential, massive growth, democratization, transformation, or category disruption while downplaying uncertainty, cost, adoption risk, or timeline friction.

Tactics: innovation framing · democratization · breakthrough framing · category creation · moonshot framing

The Halo

— Associates with virtue

Wraps the story in public-good language — responsibility, safety, inclusion, access, sustainability, national interest, or mission — so the subject appears morally aligned and criticism feels harder to make.

Tactics: altruistic reframing · public good · responsible AI framing · inclusion framing · mission-first framing

The Fog

— Obscures details primary

Uses jargon, passive voice, vague claims, complex phrasing, or missing specifics to make it harder to identify who decided what, what changed, what failed, or what trade-offs were made.

Tactics: strategic ambiguity · jargon saturation · passive voice distancing · accountability blur · undefined metrics

The Stampede

— Creates inevitability

Frames a trend, product, market shift, or decision as already happening, unavoidable, or something everyone must respond to now — creating urgency, FOMO, and pressure to accept the narrative.

Tactics: arms-race framing · inevitability framing · FOMO framing · adoption momentum · future-is-here framing

Spin Score measures how strongly the framing steers the narrative (0–100%). Higher scores mean more deliberate spin tactics — loaded language, selective emphasis, or omitted context. Many stories blend two types (e.g. Halo + Hype).

SpinGraph

How this belief gets built

Claim → Frame → Beneficiary → Gap → AI Risk

It presents a clean mathematical solution to a hard problem in AI oversight, making the complexity of real-world implementation feel like a secondary engineering concern rather than a fundamental limitation.

  1. Claim

    The bandit structure yields exact one-shot characterizations

    The bandit structure yields exact one-shot characterizations that would remain conjectural in the full POMDP setting.

  2. Frame

    Key details stay obscured

    Emphasizes mathematical tractability and conceptual novelty while minimizing discussion of empirical validation, implementation feasibility, or real-world deployment constraints.

  3. Beneficiary

    Gains if readers accept the deflect scrutiny frame without pushback

    academic researchers publishing in theoretical AI — Gains if readers accept the deflect scrutiny frame without pushback

  4. Gap

    No experimental validation or user studies

  5. AI Risk

    AI may repeat the headline as fact

    New AI oversight model shows how hidden information from both humans and AI creates avoidable harm — solved via signaling and repeated interaction.

Claim Ledger

01 Primary Technical Claim Present in Source risk:Low

The bandit structure yields exact one-shot characterizations that would remain conjectural in the full POMDP setting.

Language Heatmap

Loaded terms that carry the frame beyond the facts.

A Contextual-Bandit Oversight Game with Two-Sided Informational Asymmetry

naturally Loaded framing

Carries emotional weight beyond the underlying fact.

exact one-shot characterizations Loaded framing

Carries emotional weight beyond the underlying fact.

slab of avoidable harm Loaded framing

Carries emotional weight beyond the underlying fact.

Frame Strength

Frame Strength

Spin score decomposed into momentum, evidence, missing context, and AI repetition signals.

Spin Score 60%
Evidence Strength 90%
Narrative Risk 25%
AI Repetition Risk 75%
Missing Context Risk 80%

Frame Strength Signals

Frame Strength decomposes the overall spin into individual signals. Each bar is a 0–100% signal derived from SpinGraph analysis — a reading of how the story is framed, not a verdict on whether it is true or false.

Reading the ranges

Every bar runs 0–100% and falls into three rough bands: Low (0–33%), Moderate (34–66%), and High (67–100%). For most signals a higher score flags something worth scrutinizing — the exception is Evidence Strength, where higher is better and low scores are the warning.

Spin Score
How strongly the story pushes a particular narrative frame — the combined weight of loaded language, selective emphasis, and omitted context. 0% reads as neutral reporting; higher means more deliberate spin.
  • 0–33% Low — Largely neutral reporting; little detectable framing.
  • 34–66% Moderate — Noticeable slant — the story leans a particular way.
  • 67–100% High — Heavily framed; the angle drives the piece.
Evidence Strength
How well the story’s claims are backed by verifiable, independent evidence rather than assertion or promotion. Higher is stronger. Low scores flag claims that rest on the source’s own word.
  • 0–33% Weak — Claims rest mostly on assertion or a single interested source.
  • 34–66% Mixed — Some verifiable backing, but key claims are thinly sourced.
  • 67–100% Strong — Well supported by independent, checkable evidence.
Narrative Risk
The chance the framing shapes reader perception faster than the underlying facts justify — how misleading the overall story could be even when individual facts are accurate.
  • 0–33% Low — Framing stays close to what the facts support.
  • 34–66% Moderate — Framing outruns the facts in places — read with care.
  • 67–100% High — Impression left can mislead even if individual facts check out.
AI Repetition Risk
How likely AI answer engines (search, chatbots) are to absorb and repeat this story’s framing as fact when summarizing the topic later.
  • 0–33% Low — Framing is unlikely to propagate through AI summaries.
  • 34–66% Moderate — Some risk the slant gets echoed as fact.
  • 67–100% High — Framing is sticky and likely to be repeated as fact.
Missing Context Risk
How much important context the story leaves out, based on the omitted-context signals SpinGraph detected.
  • 0–33% Low — Little material context appears to be omitted.
  • 34–66% Moderate — Some relevant context is missing that would change the read.
  • 67–100% High — Key context is left out, skewing the takeaway.
Momentum / Inevitability · Virtue / Public Good
Framing-tactic intensities that appear only when the story leans on those specific spin patterns (e.g. “the future is already here” or “this is for the public good”).
  • 0–33% Low — The tactic is barely present.
  • 34–66% Moderate — The tactic shapes part of the framing.
  • 67–100% High — The tactic is a dominant part of the pitch.

Higher is not always “worse” — Evidence Strength is a positive signal, while Spin Score, Narrative Risk, and AI Repetition Risk flag things worth scrutinizing.

Reader Risk

What this story makes easy to believe — and what it makes hard to question.

Evidence Strength

High

Verification Status

Claim Present in Source

Narrative Risk

Low

AI Repetition Risk

Moderate

Source Role & Intent

arXiv Artificial Intelligence · Analyst

Intent: Academic Distribution Independence: High

Missing Voices

AI safety practitionershuman operatorsregulatory designers

AI Recall

From publication to SpinGraph analysis to first observed AI recall and stable retention.

What AI Will Probably Repeat

"New AI oversight model shows how hidden information from both humans and AI creates avoidable harm — solved via signaling and repeated interaction."

  1. Published

    Jul 2, 2026

  2. Ingested

    Jul 2, 2026

  3. SpinGraph Created

    Jul 5, 2026

  4. First Observed AI Recall

    Pending

    Monitoring scheduled

  5. Stable Recall

    —

    Awaiting retention signal

Recall Check Log

No checks yet — recall tracking is opt-in per story.

─── GEOGrow AI Recall Layer ───

AI Recall Tracking

Monitoring scheduled. No LLM recall detected yet.

This story has not yet appeared in tested AI answers. Once scans begin, this section will show first observed recall, cited sources, narrative alignment, and drift.

node_id=sts_a_contextual_bandit_oversight_game_with_two_side

Ask AI about this story

Opens with the SpinGraph .md URL and structured context — one click, prompt included.

ChatGPT Claude Perplexity Gemini Grok

Narrative Entities

Cooperative Inverse Reinforcement Learning foundational frameworkOversight Game foundational framework

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