Learning to Decide with AI Assistance under Human-Alignment

Dimensions overall score 3.0

• This paper analyzes the impact of AI confidence alignment with human confidence on the complexity of learning optimal decisions in AI-assisted scenarios. However, empirical evidence suggests that decision-makers often struggle to determine when to trust a prediction based solely on this communicated confidence.problem 35%
  Evidencepartial

  This paper analyzes the impact of AI confidence alignment with human confidence on the complexity of learning optimal decisions in AI-assisted scenarios. However, empirical evidence suggests that decision-makers often struggle to determine when to trust a prediction based solely on this communicated confidence.

  Implicationpartial

  Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.

  Verificationpartial

  partial

• It is widely agreed that when AI models assist decision-makers in high-stakes domains by predicting an outcome of interest, they should communicate the confidence of their predictions. However, empirical evidence suggests that decision-makers often struggle to determine when to trust a prediction based solely on this communicated confidence.method 35%
  Evidencepartial

  It is widely agreed that when AI models assist decision-makers in high-stakes domains by predicting an outcome of interest, they should communicate the confidence of their predictions. However, empirical evidence suggests that decision-makers often struggle to determine when to trust a prediction based solely on this communicated confidence.

  Implicationpartial

  Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.

  Verificationpartial

  partial

• ScienceToStartup currently rates this 3.0/10 on the public viability pass. We first show that this problem is equivalent to a two-armed online contextual learning problem with full feedback, and establish a lower bound of $Ω(\sqrt{|H| \cdot |B| \cdot T} )$ on the expected regret any learner can attain, where $H$ and $B$ denote the sets of human and AI confidence values. Code availability is flagged in the production record; the public repository link still needs proof alignment.result 35%
  Evidencepartial

  ScienceToStartup currently rates this 3.0/10 on the public viability pass. We first show that this problem is equivalent to a two-armed online contextual learning problem with full feedback, and establish a lower bound of $Ω(\sqrt{|H| \cdot |B| \cdot T} )$ on the expected regret any learner can attain, where $H$ and $B$ denote the sets of human and AI confidence values. Code availability is flagged in the production record; the public repository link still needs proof alignment.

  Implicationpartial

  Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.

  Verificationpartial

  partial

• AI-Assisted Decision Making moved forward this cycle; last verified May 2026. Public score 3.0/10. Production flags indicate code availability.why now 35%
  Evidencepartial

  AI-Assisted Decision Making moved forward this cycle; last verified May 2026. Public score 3.0/10. Production flags indicate code availability.

  Implicationpartial

  Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.

  Verificationpartial

  partial

Competitive landscape

This paper analyzes the impact of AI confidence alignment with human confidence on the complexity of learning optimal decisions in AI-assisted scenarios.