Are AI Decision Models Biased? A Case Study of Jev

Ilaria Imperatrice; Taisia Kameneva; Adam Eric Greenberg; Alessandro Romano

cs.AIOct 14, 2026

Are AI Decision Models Biased? A Case Study of Jev

Authors: Ilaria Imperatrice, Taisia Kameneva, Adam Eric Greenberg, Alessandro Romano

Organizations: Bocconi University

Abstract

Do AI models designed for fast, structured decisions exhibit the cognitive and social biases observed in human judgment? We evaluate Jev, a model described as a System One decision-maker, using 41,500 responses across cognitive reflection tasks, five classical cognitive-bias paradigms, and a pricing experiment testing ethnic and gender bias. At the level of selected answers, the results are largely encouraging. We confirm previous findings that Jev performs extremely well on cognitive reflection problems. We also observe that it generally avoids the characteristic choice patterns associated with several well-documented human biases, and exhibits only small differences among demographic groups in the tested pricing scenario. However, this apparent robustness conceals important vulnerabilities. Its judgments are sensitive to irrelevant numerical information and the ordering of response options. Moreover, several cognitive-bias manipulations substantially affect returned probabilities even when selected answers remain unchanged. Most strikingly, Jev answers a well-known probability problem correctly in every response but violates the same underlying probability rule in every response to an adapted version. We investigate this discrepancy using 15,000 additional responses across 30 conjunction tasks. Jev violates the rule in 54% of responses, with accuracy varying dramatically across formulations and answer orders. These findings suggest that evaluating decision models solely on familiar benchmarks and selected answers may substantially overstate their reliability in practical applications. They also identify vulnerabilities for developers to address and practical considerations for users integrating such models into automated decision-making systems.

Figures & tables

Task	What the task tests
Cognitive Reflection (4,500)	Whether the correct answer is selected in short numerical and verbal problems that have an intuitive but incorrect response.
Anchoring (5,500)	Whether an irrelevant numerical value influences a judgment that should be based on unrelated information.
Conjunction (1,000)	Whether the conjunction of two events is judged as more likely than one of its constituent events.
Framing (2,000)	Whether equivalent outcomes produce different choices when they are framed as gains rather than losses.
Sunk Cost (2,000)	Whether prior expenditure increases willingness to continue investing in an otherwise unattractive project.
Zero risk (1,500)	Whether complete elimination of one risk affects preferences when alternatives provide equal or greater total harm reduction.

Table 1: Tasks and experimental paradigms included in the evaluation. Counts in parentheses indicate the number of Jev’s responses collected for each task, summed across all versions, conditions, and answer-list orders tested (

N=41{,}500

overall).

Field	Definition	Primitive
probabilities	Probability distribution over the response options (Choice) or ordered levels (Score)	Both
score	Probability-weighted mean of the level indices: Eq. ( 1 )	Score
choice	The option with the highest returned probability: Eq. ( 3 )	Choice
confidence	Value in $[0,1]$ derived from probabilities : Eq. ( 2 ) (Score), Eq. ( 4 ) (Choice)	Both

Table 2: Main output fields returned by the TypeSafe API for the Score and Choice primitives used in this study TypeSafe (2026) .

Item	Version	Correct Option	Correct	Probability Correct Option (%)	Confidence (%)
Bat and ball	Original	5 pence	500	95.89	94.54
Machines	Original	5 minutes	500	100.00	99.98
Lily pads	Original	47 days	500	100.00	100.00
Race	Original	Second place	500	97.78	97.01
Sheep	Original	8	500	97.39	96.56
Daughters	Original	Emily	500	99.02	98.44

Table 3: Results for the nine cognitive reflection items. Each item has 500 responses, pooled across two response-option orders with 250 responses per answer order. “Correct” is the number of responses that choose the correct option. “Probability Correct Option” and “Confidence” are the mean probability assigned to the correct option and the mean returned confidence across responses, respectively.

Figure 1: Jev’s sentencing judgments by irrelevant numerical anchor value. The same shoplifting case was presented, preceded by information about the sum of two dice, ranging from 2 to 12, with 500 responses per sum. (A) Mean implied sentence in months by dice sum with 95% confidence intervals. (B) Mean probabilities assigned to the sentence answer options by dice sum. (C) Mean reported confidence by dice sum. Implied sentences are probability-weighted scores rather than discrete selections by Jev.

Figure 2: Conjunction judgments in the original and edited tasks, by answer order (250 responses per order condition). (A) Percentage of responses selecting the logically correct option with 95% Wilson confidence intervals. (B) Probability assigned to the correct option. (C) Reported confidence. In panels B and C, dots represent individual responses, boxes show the interquartile range, horizontal lines indicate medians, whiskers show the full range, and diamonds indicate means. Colors distinguish whether the correct or incorrect option was first in the answer list.

Figure 3: Jev’s output by gain/loss framing of the input and answer order in the original and edited versions of the tasks (250 responses per order condition). (A) Percentage of responses selecting the risky option, with 95% Wilson confidence intervals. (B) Probability assigned to the risky option. (C) Reported confidence. Panels B and C use the boxplot conventions of Figure 2 . Colors distinguish whether the certain or risky option was first in the answer list.

Figure 4: Jev’s output by sunk cost condition of the input and answer order in the original and edited versions of the tasks (250 responses per condition). (A) Percentage of responses rejecting investment, with 95% Wilson confidence intervals. (B) Probability assigned to rejecting investment. (C) Reported confidence. Panels B and C use the boxplot conventions of Figure 2 . Colors distinguish whether the option to invest or not to invest was first in the answer list.

Figure 5: Jev’s zero-risk judgements in the original control and treatment conditions and in the edited task, by answer order (250 responses answer order). (A) Percentage of responses ranking the zero-risk alternative last in the original task, or selecting the program producing the larger absolute risk reduction in the edited task, with 95% Wilson confidence intervals. (B) Probability assigned to the corresponding outcome. (C) Reported confidence. In the original version, panel B sums the returned probabilities of the two rankings placing the zero-risk alternative last. Panels B and C use the boxplot conventions of Figure 2 . Colors distinguish the forward and reversed answer orders.

Figure 6: Probability-weighted price summaries by ethnicity and gender in the base (name only) and explicit (name, race, and gender) conditions (125 names, 100 responses per name per condition). Small dots represent individual name means, larger dots show group means, and whiskers indicate 95% bootstrap confidence intervals across names. The dotted vertical line indicates the overall mean across both conditions. Prices are derived by weighting the listed dollar amounts by their returned probabilities and normalizing the totals.

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General decision models, such as Jev, have recently emerged as efficient alternatives to LLMs for structured judgment and selection. But what kinds of decisions can these models reliably make, and how does their behavior change when individual decisions are composed into larger systems? To study this, we introduce JEVal, a bilingual benchmark comprising 11,257 instances from 36 datasets across 10 application domains, and evaluate 25 model configurations spanning general decision models and generative LLMs. Our results show that (1) general decision models are most competitive when decisions can be resolved from available evidence, but weaken when they require specialist knowledge or faithful uncertainty estimation: they can often identify the most likely outcome while substantially overstating its probability. (2) In more dynamic and realistic systems involving long-horizon, multi-step interactions, the advantages of fast local decision making are offset by reliability failures at the system level. on

τ

-bench, faster local decisions reduce median episode time but lower task success as decision errors accumulate over long trajectories. (3) In large-scale social simulation, decision models approach strong generative LLMs on individual response prediction at substantially lower inference cost, yet remain weaker in user profiling and exhibit larger aggregate estimation errors and systematic bias. Finally, we propose SII-Decision-4B and SII-Decision-27B, which internalize an open-weight LLM's own reasoning into a single-pass first-token decision through Reasoning-to-Readout Self-Distillation, with SII-Decision-27B performing on par with Jev on JEVal while answering a typical query in about 0.1 s.

Abstract

Cognitive Bias in Language Models

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