most citedBiased AI improves human decision-making but reduces trust

2 citations · 3 across the 5 of their papers we have counts for

collaborators

6 papers

econ.EM2026

Content vs. Form: What Drives the Writing Score Gap Across Socioeconomic Backgrounds? A Generated Panel Approach

Nadav Kunievsky, Pedro Pertusi

Students from different socioeconomic backgrounds exhibit persistent gaps in test scores, gaps that can translate into unequal educational and labor-market outcomes later in life.…

econ.EM2025

Linear Regression in a Nonlinear World

Nadav Kunievsky

The interpretation of coefficients from multivariate linear regression relies on the assumption that the conditional expectation function is linear in the variables. However, in ma…

econ.GN2025

Polarization by Design: How Elites Could Shape Mass Preferences as AI Reduces Persuasion Costs

Nadav Kunievsky

In democracies, major policy decisions typically require some form of majority or consensus, so elites must secure mass support to govern. Historically, elites could shape support…

econ.GN20251 cited

The (Short-Term) Effects of Large Language Models on Unemployment and Earnings

Danqing Chen, Carina Kane, Austin Kozlowski +2

Large Language Models have spread rapidly since the release of ChatGPT in late 2022, accompanied by claims of major productivity gains but also concerns about job displacement. Thi…

cs.HC20252 cited

Biased AI improves human decision-making but reduces trust

Shiyang Lai, Junsol Kim, Nadav Kunievsky +2

Current AI systems minimize risk by enforcing ideological neutrality, yet this may introduce automation bias by suppressing cognitive engagement in human decision-making. We conduc…

cs.CL2025

Missing vs. Unused Knowledge Hypothesis for Language Model Bottlenecks in Patent Understanding

Siyang Wu, Honglin Bao, Nadav Kunievsky +1

While large language models (LLMs) excel at factual recall, the real challenge lies in knowledge application. A gap persists between their ability to answer complex questions and t…