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arxiv: 1907.00850 · v1 · pith:W3CSV74Unew · submitted 2019-07-01 · 💻 cs.CY

Following wrong suggestions: self-blame in human and computer scenarios

Andrea Beretta , Massimo Zancanaro , Bruno Lepri This is my paper

Pith reviewed 2026-05-25 11:33 UTC · model grok-4.3

classification 💻 cs.CY
keywords self-blameresponsibilityintelligent machineswrong suggestionsdecision makinghuman-computer interactionemotionscooperation
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The pith

People report less responsibility for a wrong choice when the suggestion comes from an intelligent machine instead of a human expert.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The study tested how people feel after following a suggestion that leads to a bad outcome, comparing cases where the suggestion came from a human expert versus an intelligent machine. Participants felt significantly less responsible for the error in the machine case. This difference matters because reduced self-blame could shape whether users develop distrust toward AI systems after repeated mistakes and how they cooperate with them over time. The work points to a need for better ways to present machine suggestions so that negative emotions do not undermine long-term use.

Core claim

In a typical decision-making task, participants followed a suggestion leading to a wrong outcome in two parallel scenarios, one with an expert human and one with an intelligent machine. Perceived responsibility for the wrong choice dropped significantly when the suggestion originated from the machine. The authors note that few studies have examined the negative emotions arising from such machine-assisted failures or how these emotions might affect sustained cooperation and trust.

What carries the argument

Direct comparison of self-reported responsibility after following a wrong suggestion, with the sole manipulated variable being whether the source is a human expert or an intelligent machine.

If this is right

  • Users may experience lower self-blame and different emotional responses when machine suggestions lead to errors.
  • Long-term cooperation with intelligent systems could be affected by this reduced sense of personal responsibility.
  • Design choices in how suggestions are offered may need to address self-blame to maintain user trust.
  • Studies of decision-making with machines must account for source-dependent differences in responsibility attribution.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Lower self-blame with machines might encourage continued reliance even when the system is unreliable.
  • The pattern could influence real-world domains such as medical or financial advice where accountability questions arise.
  • Testing the effect with suggestions framed as probabilistic or learning-based might reveal whether the responsibility drop persists.

Load-bearing premise

The human-expert and intelligent-machine scenarios are equivalent in every respect except the identity of the suggester, so any difference in reported responsibility can be credited to that distinction alone.

What would settle it

A replication in which participants assign equal or greater responsibility to themselves after following a machine suggestion than after a human suggestion would falsify the reported decrease.

read the original abstract

This paper investigates the specific experience of following a suggestion by an intelligent machine that has a wrong outcome and the emotions people feel. By adopting a typical task employed in studies on decision-making, we presented participants with two scenarios in which they follow a suggestion and have a wrong outcome by either an expert human being or an intelligent machine. We found a significant decrease in the perceived responsibility on the wrong choice when the machine offers the suggestion. At present, few studies have investigated the negative emotions that could arise from a bad outcome after following the suggestion given by an intelligent system, and how to cope with the potential distrust that could affect the long-term use of the system and the cooperation. This preliminary research has implications in the study of cooperation and decision making with intelligent machines. Further research may address how to offer the suggestion in order to better cope with user's self-blame.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The paper reports results from an empirical study using a decision-making task in which participants followed a suggestion leading to a wrong outcome, comparing conditions where the suggestion came from either an expert human or an intelligent machine. The central finding is a statistically significant decrease in perceived responsibility for the wrong choice in the machine-suggestion condition. The work positions itself as preliminary and discusses implications for user self-blame, distrust, and long-term cooperation with intelligent systems.

Significance. If the reported difference is robust after methodological clarification and controls for confounds, the result would be relevant to the growing literature on human-AI decision-making and blame attribution. It could inform the design of suggestion interfaces to reduce negative emotional consequences. The study is explicitly preliminary, however, and its contribution is limited by the absence of reported sample sizes, test statistics, effect sizes, and equivalence checks between conditions.

major comments (3)
  1. [Abstract] Abstract: the claim of a 'statistically significant difference' in perceived responsibility is presented without any information on sample size, statistical test(s), effect size, power, or controls for confounds. This information is load-bearing for evaluating the central empirical claim.
  2. [Abstract] Abstract / Methods (implied design): the two scenarios are described as 'expert human being' versus 'intelligent machine.' No evidence is provided that these labels were matched for perceived expertise, authority, or competence. Without such checks or matched framing, any responsibility difference cannot be unambiguously attributed to the human/machine distinction rather than a difference in implied authority (directly relevant to the weakest assumption identified in the stress-test).
  3. [Abstract] Abstract: the paper states that 'few studies have investigated the negative emotions' but provides no citations or literature review to support this positioning or to situate the contribution relative to existing work on blame and automation.
minor comments (2)
  1. [Abstract] The abstract refers to 'self-blame' in the title but reports 'perceived responsibility'; a brief clarification of the relationship between these constructs would improve precision.
  2. [Abstract] The final sentence on 'how to offer the suggestion' is forward-looking but lacks any concrete proposal or link back to the reported data.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their constructive comments on this preliminary study. Below we respond point-by-point to the major comments and indicate planned revisions.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the claim of a 'statistically significant difference' in perceived responsibility is presented without any information on sample size, statistical test(s), effect size, power, or controls for confounds. This information is load-bearing for evaluating the central empirical claim.

    Authors: We agree the abstract should convey these details. The revised abstract will report the sample size, the specific statistical test, effect size, and any controls applied. As the work is explicitly preliminary, no a priori power analysis was performed. revision: yes

  2. Referee: [Abstract] Abstract / Methods (implied design): the two scenarios are described as 'expert human being' versus 'intelligent machine.' No evidence is provided that these labels were matched for perceived expertise, authority, or competence. Without such checks or matched framing, any responsibility difference cannot be unambiguously attributed to the human/machine distinction rather than a difference in implied authority (directly relevant to the weakest assumption identified in the stress-test).

    Authors: We accept that the design does not include explicit matching or manipulation checks for perceived expertise or authority. The revised manuscript will add an explicit discussion of this limitation and its implications for interpreting the human-machine contrast. revision: yes

  3. Referee: [Abstract] Abstract: the paper states that 'few studies have investigated the negative emotions' but provides no citations or literature review to support this positioning or to situate the contribution relative to existing work on blame and automation.

    Authors: We will revise the introduction to include relevant citations on blame attribution, automation, and human-AI decision-making, together with a concise literature review to better situate the contribution. revision: yes

Circularity Check

0 steps flagged

No significant circularity; empirical reporting of observed differences

full rationale

The paper is a straightforward empirical study that presents two scenarios to participants and reports measured differences in perceived responsibility. No equations, model derivations, fitted parameters renamed as predictions, or load-bearing self-citations appear in the provided text. The central claim rests on participant data rather than any definitional identity or reduction to prior inputs by construction. This is the most common honest finding for non-theoretical empirical work and warrants score 0.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The paper is an empirical psychology study; it contains no mathematical derivations, fitted parameters, or postulated entities.

axioms (1)
  • domain assumption Participants can accurately self-report perceived responsibility for a decision outcome.
    The central measurement relies on self-report scales whose validity is assumed.

pith-pipeline@v0.9.0 · 5676 in / 1104 out tokens · 18039 ms · 2026-05-25T11:33:06.930639+00:00 · methodology

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Reference graph

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