Video Experimental Relacionado

Updated: May 5, 2026

Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods

Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods

Published on: September 19, 2012

Explicación del escepticismo de las mujeres hacia la inteligencia artificial: El papel de la orientación al riesgo y

Sophie Borwein¹, Beatrice Magistro², R Michael Alvarez³

¹Department of Political Science, University of British Columbia, Vancouver, BC, Canada V6T 1Z1.

|January 22, 2026

Resumen

Las mujeres perciben la adopción de inteligencia artificial (IA) como más arriesgada debido a una mayor aversión al riesgo y exposición. Abordar estos riesgos específicos de género es crucial para una política de IA equitativa y para prevenir reacciones negativas.

Área de la Ciencia:

Ciencias Sociales
Economía
Estudios de Tecnología

Sus antecedentes:

La adopción de inteligencia artificial (IA) en el lugar de trabajo presenta beneficios económicos potenciales pero también riesgos.
La investigación existente indica una posible brecha de género en la adopción de tecnología y en las actitudes.
Comprender estas diferencias es crucial para una integración equitativa de la IA.

Objetivo del estudio:

Examinar la brecha de género en las actitudes hacia la adopción de IA en el lugar de trabajo.
Identificar los impulsores de esta brecha de género, centrándose en la orientación y exposición al riesgo.
Informar la formulación de políticas de IA incorporando las perspectivas de las mujeres sobre los riesgos de la IA.

Principales métodos:

Se utilizaron datos de encuestas originales de aproximadamente 3000 encuestados en Canadá y Estados Unidos.
Se empleó un diseño experimental para establecer un vínculo causal entre la percepción del riesgo y las actitudes hacia la IA.
Se aplicó modelado de temas estructurales para analizar respuestas abiertas sobre los beneficios de la IA.

Principales resultados:

Las mujeres perciben consistentemente la IA como más arriesgada que los hombres.
Se identificaron los principales impulsores: la mayor aversión general al riesgo de las mujeres y una mayor exposición a los riesgos relacionados con la IA.
Las mujeres expresan mayor incertidumbre sobre los beneficios de la IA y anticipan menos efectos positivos en el empleo.

Conclusiones:

Las diferencias de género en la percepción del riesgo influyen significativamente en las actitudes hacia la adopción de la IA.
La falta de abordaje de los riesgos específicos de género de la IA puede exacerbar las desigualdades y conducir a la oposición política.
La incorporación de las perspectivas de las mujeres es vital para desarrollar políticas de IA inclusivas y efectivas.

Palabras clave:

inteligencia artificial brecha de género opinión pública riesgo

Más Videos Relacionados

Experimental Research Examining How People Can Cope with Uncertainty Through Soft Haptic Sensations

Experimental Research Examining How People Can Cope with Uncertainty Through Soft Haptic Sensations

Published on: September 16, 2015

High-definition Transcranial Direct Current Stimulation over Right Dorsolateral Prefrontal Cortex to Enhance Metacognitive Sensitivity

High-definition Transcranial Direct Current Stimulation over Right Dorsolateral Prefrontal Cortex to Enhance Metacognitive Sensitivity

Published on: September 26, 2025

Videos de Conceptos Relacionados

The Availability Heuristic

The Availability Heuristic

A heuristic is a general problem-solving framework (Tversky & Kahneman, 1974). You can think of these as mental shortcuts that are used to solve problems. Different types of heuristics are used in different types of situations, and the impulse to use a heuristic occurs when one of five conditions is met (Pratkanis, 1989):

The Representativeness Heuristic

The Representativeness Heuristic

The representative heuristic describes a biased way of thinking, in which you unintentionally stereotype someone or something. For example, you may assume that your professors spend their free time reading books and engaging in intellectual conversation, because the idea of them spending their time playing volleyball or visiting an amusement park does not fit in with your stereotypes of professors.

Stereotype Content Model

Stereotype Content Model

The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...

Stereotype Threat and Self-fulfilling Prophecies

Stereotype Threat and Self-fulfilling Prophecies

When we hold a stereotype about a person, we have expectations that he or she will fulfill that stereotype. A self-fulfilling prophecy is an expectation held by a person that alters his or her behavior in a way that tends to make it true. When we hold stereotypes about a person, we tend to treat the person according to our expectations. This treatment can influence the person to act according to our stereotypic expectations, thus confirming our stereotypic beliefs. Research by Rosenthal and...

Criticisms of the Evolutionary Perspective

Criticisms of the Evolutionary Perspective

In a study where individuals posing as strangers offered compliments and proposed casual sex to students, the responses differed significantly based on gender. Not a single woman accepted the proposal, while 70% of the men agreed. This outcome provides a useful scenario to explore through the lens of evolutionary psychology and social learning theory, highlighting the diverse perspectives on human sexual behaviors.
Evolutionary psychology provides one explanation for these findings, suggesting...

Unrealistic Optimism Bias

Unrealistic Optimism Bias

Unrealistic optimism bias is the tendency to overestimate the likelihood of positive outcomes. This cognitive bias makes individuals believe they are less likely to experience failures, setbacks, or risks and more likely to succeed than others. For example, people may assume they are less prone to health issues, accidents, or financial struggles than their peers, even when they share similar risk factors.One key component of this bias is the above-average effect, where individuals perceive...