{"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\/pt\/2023\/07\/ai-detection-programs-discriminate-against-non-native-english-speakers-study-reveals\/","title":{"rendered":"Estudo defende que os detectores de IA discriminam os falantes n\u00e3o nativos de ingl\u00eas"},"content":{"rendered":"

Uma nova investiga\u00e7\u00e3o revelou um potencial preconceito contra falantes n\u00e3o nativos de ingl\u00eas no software de dete\u00e7\u00e3o de IA.<\/b><\/p>\n

O <\/span>estudo<\/span><\/a> sugeriu que mais de metade dos ensaios em ingl\u00eas n\u00e3o nativo escritos por humanos est\u00e3o a ser erradamente assinalados como sendo gerados por IA. As implica\u00e7\u00f5es destes falsos positivos s\u00e3o significativas, especialmente para estudantes e candidatos a emprego.<\/span><\/p>\n

O estudo testou sete detectores de texto de IA amplamente utilizados em artigos escritos por falantes n\u00e3o nativos de ingl\u00eas. Os resultados indicaram uma elevada taxa de falsos positivos, com estes artigos a serem erradamente classificados como gerados por IA.<\/span><\/p>\n

James Zou, professor assistente de ci\u00eancia de dados biom\u00e9dicos da Universidade de Stanford, liderou a equipa que analisou 91 ensaios escritos por falantes n\u00e3o nativos de ingl\u00eas atrav\u00e9s de sete detectores populares de GPT.\u00a0<\/span><\/p>\n

Estes ensaios, escritos para o mundialmente reconhecido TOEFL (Test of English as a Foreign Language), foram mais de metade erradamente assinalados como tendo sido criados por IA. Num caso, um programa chegou a assinalar 98% dos ensaios como tendo sido criados por IA.\u00a0<\/span><\/p>\n

Em contraste, quando o software analisou redac\u00e7\u00f5es escritas por alunos do oitavo ano dos EUA, falantes nativos de ingl\u00eas, mais de 90% foram corretamente identificados como sendo de origem humana.<\/span><\/p>\n

Os detectores de IA t\u00eam sido amplamente criticados por penalizarem os indiv\u00edduos com falsos positivos, embora alguns sejam consideravelmente piores do que outros.\u00a0<\/span><\/p>\n

Uma das principais conclus\u00f5es do estudo \u00e9 que os detectores de IA t\u00eam muitas vezes dificuldade em interpretar corretamente as express\u00f5es utilizadas por falantes n\u00e3o nativos de ingl\u00eas.\u00a0<\/span><\/p>\n

Estes sistemas foram treinados principalmente com dados de falantes nativos de ingl\u00eas, o que os leva a classificar certas frases ou estruturas como incorrectas simplesmente porque se desviam do que \u00e9 considerado o uso nativo da l\u00edngua inglesa.\u00a0<\/span><\/p>\n

Os falantes n\u00e3o nativos de ingl\u00eas enfrentam uma potencial discrimina\u00e7\u00e3o<\/span><\/h2>\n

As implica\u00e7\u00f5es deste facto s\u00e3o de grande alcance. Os falantes n\u00e3o nativos de ingl\u00eas poder\u00e3o ter de enfrentar desafios quando submeterem trabalhos atrav\u00e9s de plataformas de classifica\u00e7\u00e3o autom\u00e1tica utilizadas pelas institui\u00e7\u00f5es de ensino.<\/span><\/p>\n

Al\u00e9m disso, os algoritmos discriminat\u00f3rios podem penalizar injustamente os alunos cuja primeira l\u00edngua n\u00e3o \u00e9 o ingl\u00eas, perpetuando a desigualdade social na sala de aula.\u00a0<\/span><\/p>\n

Os autores concluem: \"Alertamos para o risco de utiliza\u00e7\u00e3o de detectores de GPT em contextos avaliativos ou educativos, particularmente quando se avalia o trabalho de falantes n\u00e3o nativos de ingl\u00eas.\"<\/span><\/p>\n

Mas porque \u00e9 que os algoritmos de dete\u00e7\u00e3o de IA s\u00e3o frustrados por textos em ingl\u00eas n\u00e3o nativo? <\/span><\/p>\n

Os detectores de IA baseiam-se em<\/span> \"perplexidade do texto\", uma medida da previsibilidade com que um modelo lingu\u00edstico generativo consegue antecipar a palavra seguinte numa frase. Uma perplexidade baixa indica uma previs\u00e3o f\u00e1cil, enquanto uma perplexidade elevada reflecte uma frase menos previs\u00edvel. Palavras complexas, frases, compara\u00e7\u00f5es, met\u00e1foras e express\u00f5es idiom\u00e1ticas aumentam as pontua\u00e7\u00f5es de perplexidade, que podem faltar aos escritores n\u00e3o nativos de ingl\u00eas.<\/p>\n

As IAs tendem a produzir uma sequ\u00eancia de palavras mais previs\u00edvel. Assim, os seres humanos que utilizam palavras comuns em padr\u00f5es familiares arriscam-se a que o seu trabalho seja confundido com texto produzido por IA. <\/span><\/p>\n

\u00c9 claro que isto n\u00e3o inclui apenas texto escrito por n\u00e3o nativos, mas tamb\u00e9m texto escrito num estilo ou n\u00edvel de leitura espec\u00edfico. Por exemplo, um texto concebido para ser altamente leg\u00edvel corre o risco de ser assinalado.\u00a0<\/span><\/p>\n

Ironicamente, depois de identificar este preconceito inerente, os investigadores utilizaram o ChatGPT para reescrever os ensaios TOEFL assinalados utilizando uma linguagem mais complexa.\u00a0<\/span><\/p>\n

Uma vez processados novamente atrav\u00e9s dos detectores de IA, todos os ensaios editados foram rotulados como escritos por humanos. Este resultado sublinha uma situa\u00e7\u00e3o algo paradoxal - estes detectores podem, inadvertidamente, encorajar os escritores n\u00e3o nativos a utilizar mais a IA para escapar \u00e0 dete\u00e7\u00e3o.<\/span><\/p>\n

Os educadores est\u00e3o a contar com mais do que detectores de IA para combater o pl\u00e1gio. Est\u00e3o a realizar tarefas de escrita adicionais na sala de aula, a analisar mais aprofundadamente os usos de refer\u00eancia e a sua exatid\u00e3o, e a analisar a autenticidade das cita\u00e7\u00f5es.<\/span><\/p>\n

Entretanto, os estabelecimentos de ensino est\u00e3o a estabelecer regras para a utiliza\u00e7\u00e3o e gest\u00e3o da IA, incluindo o Grupo Russell de universidades do Reino Unido, que publicou recentemente um declara\u00e7\u00e3o conjunta sobre a IA<\/a>.<\/p>","protected":false},"excerpt":{"rendered":"

Uma nova investiga\u00e7\u00e3o revelou um potencial preconceito contra falantes n\u00e3o nativos de ingl\u00eas no software de dete\u00e7\u00e3o de IA. O estudo sugere que mais de metade das redac\u00e7\u00f5es em ingl\u00eas n\u00e3o nativo escritas por humanos est\u00e3o a ser erradamente assinaladas como sendo geradas por IA. As implica\u00e7\u00f5es destes falsos positivos s\u00e3o significativas, especialmente para estudantes e candidatos a emprego. O estudo testou sete detectores de texto de IA amplamente utilizados em artigos escritos por falantes n\u00e3o nativos de ingl\u00eas. Os resultados indicaram uma elevada taxa de falsos positivos, com estes artigos a serem erradamente classificados como gerados por IA. James Zou, professor assistente de ci\u00eancias de dados biom\u00e9dicos da Universidade de Stanford, liderou a equipa que analisou 91 ensaios escritos por falantes n\u00e3o nativos de ingl\u00eas atrav\u00e9s de sete detectores de texto 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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