{"id":2410,"date":"2023-07-10T18:40:02","date_gmt":"2023-07-10T18:40:02","guid":{"rendered":"https:\/\/dailyai.com\/?p=2410"},"modified":"2023-07-10T18:58:36","modified_gmt":"2023-07-10T18:58:36","slug":"ai-detection-programs-discriminate-against-non-native-english-speakers-study-reveals","status":"publish","type":"post","link":"https:\/\/dailyai.com\/es\/2023\/07\/ai-detection-programs-discriminate-against-non-native-english-speakers-study-reveals\/","title":{"rendered":"Los detectores de inteligencia artificial discriminan a los hablantes de ingl\u00e9s no nativos, seg\u00fan un estudio"},"content":{"rendered":"

Una nueva investigaci\u00f3n ha revelado un posible sesgo en contra de los hablantes de ingl\u00e9s no nativos en el software de detecci\u00f3n de IA.<\/b><\/p>\n

En <\/span>estudiar<\/span><\/a> sugiere que m\u00e1s de la mitad de las redacciones humanas en ingl\u00e9s no nativo se marcan err\u00f3neamente como generadas por IA. Las consecuencias de estos falsos positivos son importantes, sobre todo para estudiantes y solicitantes de empleo.<\/span><\/p>\n

El estudio prob\u00f3 siete detectores de texto de IA ampliamente utilizados en art\u00edculos escritos por hablantes no nativos de ingl\u00e9s. Los resultados indicaron una elevada tasa de falsos positivos, ya que estos art\u00edculos se clasificaron err\u00f3neamente como generados por IA.<\/span><\/p>\n

James Zou, profesor adjunto de ciencia de datos biom\u00e9dicos de la Universidad de Stanford, dirigi\u00f3 el equipo que pas\u00f3 91 ensayos escritos por hablantes no nativos de ingl\u00e9s por siete conocidos detectores de GPT.\u00a0<\/span><\/p>\n

M\u00e1s de la mitad de estas redacciones, escritas para el mundialmente reconocido TOEFL (Test of English as a Foreign Language), se marcaron err\u00f3neamente como generadas por IA. En un caso, un programa lleg\u00f3 a marcar 98% de los ensayos como creados por IA.\u00a0<\/span><\/p>\n

Por el contrario, cuando el software analiz\u00f3 ensayos escritos por alumnos nativos de habla inglesa de octavo curso de EE.UU., m\u00e1s de 90% se identificaron correctamente como generados por humanos.<\/span><\/p>\n

Los detectores de IA han sido muy criticados por penalizar a las personas con falsos positivos, aunque algunos son considerablemente peores que otros.\u00a0<\/span><\/p>\n

Una de las principales conclusiones del estudio es que los detectores de IA suelen tener problemas para interpretar con precisi\u00f3n las expresiones utilizadas por hablantes de ingl\u00e9s no nativos.\u00a0<\/span><\/p>\n

Estos sistemas se han entrenado principalmente con datos de hablantes nativos de ingl\u00e9s, lo que les lleva a etiquetar ciertas frases o estructuras como incorrectas simplemente porque se desv\u00edan de lo que se considera el uso nativo de la lengua inglesa.\u00a0<\/span><\/p>\n

Las personas cuya lengua materna no es el ingl\u00e9s pueden ser discriminadas<\/span><\/h2>\n

Las implicaciones de este hecho son de gran alcance. Los hablantes no nativos de ingl\u00e9s podr\u00edan enfrentarse a dificultades a la hora de enviar trabajos a trav\u00e9s de plataformas de calificaci\u00f3n automatizadas empleadas por los centros educativos.<\/span><\/p>\n

Adem\u00e1s, los algoritmos discriminatorios podr\u00edan penalizar injustamente a los alumnos cuya lengua materna no es el ingl\u00e9s, perpetuando la desigualdad social en el aula.\u00a0<\/span><\/p>\n

Los autores concluyen: \"Desaconsejamos encarecidamente el uso de detectores de GPT en contextos evaluativos o educativos, especialmente cuando se eval\u00faa el trabajo de hablantes no nativos de ingl\u00e9s.\"<\/span><\/p>\n

Pero, \u00bfpor qu\u00e9 los algoritmos de detecci\u00f3n de IA se ven frustrados por textos en ingl\u00e9s no nativo? <\/span><\/p>\n

Los detectores de IA se basan en<\/span> \"perplejidad textual\", una medida de la capacidad de predicci\u00f3n de un modelo ling\u00fc\u00edstico generativo para anticipar la siguiente palabra de una frase. Una perplejidad baja indica una predicci\u00f3n f\u00e1cil, mientras que una perplejidad alta refleja una frase menos predecible. Las palabras complejas, las frases, los s\u00edmiles, las met\u00e1foras y las expresiones idiom\u00e1ticas aumentan la perplejidad, algo de lo que pueden carecer los escritores no nativos.<\/p>\n

Las IA tienden a producir una secuencia de palabras m\u00e1s predecible. As\u00ed, los humanos que utilizan palabras comunes en patrones familiares corren el riesgo de que su trabajo se confunda con un texto producido por la IA. <\/span><\/p>\n

Por supuesto, esto no s\u00f3lo incluye el texto escrito en un idioma no nativo, sino tambi\u00e9n el texto escrito en un estilo o nivel de lectura espec\u00edfico. Por ejemplo, un texto dise\u00f1ado para ser muy legible corre el riesgo de ser marcado.\u00a0<\/span><\/p>\n

Ir\u00f3nicamente, tras identificar este sesgo inherente, los investigadores utilizaron ChatGPT para reescribir los ensayos TOEFL marcados utilizando un lenguaje m\u00e1s complejo.\u00a0<\/span><\/p>\n

Una vez procesados de nuevo por los detectores de IA, todos los ensayos editados fueron calificados como escritos por humanos. Este resultado pone de manifiesto una situaci\u00f3n un tanto parad\u00f3jica: estos detectores podr\u00edan animar inadvertidamente a los escritores no nativos a utilizar m\u00e1s la IA para evitar ser detectados.<\/span><\/p>\n

Los educadores recurren a algo m\u00e1s que detectores de IA para reprimir el plagio. Llevan a cabo tareas de redacci\u00f3n adicionales en el aula, profundizan en el uso de referencias y su exactitud y analizan la autenticidad de las citas.<\/span><\/p>\n

Mientras tanto, los centros educativos est\u00e1n estableciendo normas para el uso y la gesti\u00f3n de la IA, entre ellos el Grupo Russell de universidades del Reino Unido, que recientemente public\u00f3 un declaraci\u00f3n conjunta sobre la IA<\/a>.<\/p>","protected":false},"excerpt":{"rendered":"

Una nueva investigaci\u00f3n ha revelado un posible sesgo contra los hablantes no nativos de ingl\u00e9s en el software de detecci\u00f3n de IA. El estudio sugiere que m\u00e1s de la mitad de los ensayos escritos por personas cuya lengua materna no es el ingl\u00e9s se marcan err\u00f3neamente como generados por IA. Las consecuencias de estos falsos positivos son importantes, sobre todo para estudiantes y solicitantes de empleo. El estudio puso a prueba siete detectores de texto de IA muy utilizados en art\u00edculos escritos por hablantes no nativos de ingl\u00e9s. Los resultados indicaron una elevada tasa de falsos positivos, ya que estos art\u00edculos se clasificaron err\u00f3neamente como generados por IA. James Zou, profesor adjunto de ciencia de datos biom\u00e9dicos de la Universidad de Stanford, dirigi\u00f3 el equipo que analiz\u00f3 91 ensayos escritos por hablantes no nativos de ingl\u00e9s con siete de los detectores de texto m\u00e1s populares.<\/p>","protected":false},"author":2,"featured_media":2411,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[88],"tags":[163,190],"class_list":["post-2410","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ethics","tag-ai-risks","tag-education"],"yoast_head":"\nAI detectors discriminate against non-native English speakers, argues study | DailyAI<\/title>\n<meta name=\"description\" content=\"New research has revealed potential bias against non-native English speakers in AI detection software.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" 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