{"id":11908,"date":"2024-05-02T18:41:38","date_gmt":"2024-05-02T18:41:38","guid":{"rendered":"https:\/\/dailyai.com\/?p=11908"},"modified":"2024-05-04T17:24:35","modified_gmt":"2024-05-04T17:24:35","slug":"princeton-led-team-warns-that-machine-learning-brings-peril-to-science","status":"publish","type":"post","link":"https:\/\/dailyai.com\/it\/2024\/05\/princeton-led-team-warns-that-machine-learning-brings-peril-to-science\/","title":{"rendered":"Un team di Princeton lancia l'allarme: L'intelligenza artificiale comporta rischi per l'integrit\u00e0 scientifica"},"content":{"rendered":"

L'intelligenza artificiale sta trasformando la ricerca scientifica, ma senza una guida adeguata potrebbe fare pi\u00f9 male che bene.<\/strong><\/p>\n

Questa \u00e8 la conclusione puntuale di un <\/span>nuova carta<\/span><\/a> pubblicato su Science Advances da un team interdisciplinare di 19 ricercatori guidati dagli informatici dell'Universit\u00e0 di Princeton Arvind Narayanan e Sayash Kapoor.\u00a0<\/span><\/p>\n

Il team sostiene che l'uso improprio dell'apprendimento automatico in tutte le discipline scientifiche sta alimentando una crisi di riproducibilit\u00e0 che minaccia di minare le basi stesse della scienza.<\/span><\/p>\n

\"Quando si passa dai metodi statistici tradizionali a quelli di apprendimento automatico, i modi per darsi la zappa sui piedi sono molto pi\u00f9 numerosi\". <\/span>ha detto Narayanan<\/span><\/a>che dirige il Centro per la politica della tecnologia dell'informazione di Princeton.\u00a0<\/span><\/p>\n

\"Se non abbiamo un intervento per migliorare i nostri standard scientifici e di reporting quando si tratta di scienza basata sul machine learning, rischiamo che non solo una disciplina, ma molte discipline scientifiche diverse riscoprano queste crisi una dopo l'altra\".<\/span><\/p>\n

Secondo gli autori, il problema \u00e8 che l'apprendimento automatico \u00e8 stato rapidamente adottato da quasi tutti i settori scientifici, spesso senza standard chiari per garantire l'integrit\u00e0 e la riproducibilit\u00e0 dei risultati. <\/span><\/p>\n

Essi evidenziano che il t<\/span>Sono gi\u00e0 stati pubblicati migliaia di articoli che utilizzano metodi di apprendimento automatico errati.<\/span><\/p>\n

Ma il team guidato da Princeton sostiene che c'\u00e8 ancora tempo per evitare questa crisi imminente. Ha presentato una semplice lista di controllo di buone pratiche che, se ampiamente adottate, potrebbero salvaguardare l'affidabilit\u00e0 dell'apprendimento automatico nella scienza.\u00a0<\/span><\/p>\n

La lista di controllo, chiamata REFORMS (Recommendations for Machine-learning-based Science), \u00e8 composta da 32 domande in otto aree chiave:<\/span><\/p>\n

    \n
  1. Obiettivi dello studio<\/b>: Indicare chiaramente l'affermazione scientifica che si sta facendo e il modo in cui l'apprendimento automatico verr\u00e0 utilizzato per sostenerla. Giustificare la scelta dell'apprendimento automatico rispetto ai metodi statistici tradizionali.<\/span><\/li>\n
  2. Riproducibilit\u00e0 computazionale<\/b>: Fornire il codice, i dati, le specifiche dell'ambiente di calcolo, la documentazione e uno script di riproduzione necessari per consentire ad altri di riprodurre i risultati dello studio in modo indipendente.<\/span><\/li>\n
  3. Qualit\u00e0 dei dati<\/b>: Documentare le fonti dei dati, la struttura di campionamento, le variabili di esito, la dimensione del campione e la quantit\u00e0 di dati mancanti. Giustificare che il set di dati \u00e8 appropriato e rappresentativo del quesito scientifico.<\/span><\/li>\n
  4. Preelaborazione dei dati<\/b>: Riportare il modo in cui i dati sono stati puliti, trasformati e suddivisi in set di addestramento e test. Fornire una motivazione per tutti i dati che sono stati esclusi.<\/span><\/li>\n
  5. Modellazione<\/b>: Descrivere e giustificare tutti i modelli provati, il metodo utilizzato per selezionare i modelli finali e il processo di regolazione degli iperparametri. Confrontare le prestazioni con le linee di base appropriate.<\/span><\/li>\n
  6. Perdita di dati<\/b>: Verificare che il processo di modellazione non abbia utilizzato inavvertitamente informazioni provenienti dai dati di prova e che le caratteristiche di input non abbiano causato perdite nel risultato.<\/span><\/li>\n<\/ol>\n

    \"Si tratta di un problema sistematico con soluzioni sistematiche\", spiega Kapoor. <\/span><\/p>\n

    Tuttavia, i costi dell'errore<\/span> potrebbe essere immenso. Una scienza sbagliata potrebbe affossare ricerche promettenti, scoraggiare i ricercatori ed erodere la fiducia del pubblico nella scienza.\u00a0<\/span><\/p>\n

    Ricerche precedenti, come <\/span>L'indagine su larga scala di Nature<\/span><\/a> di accademici sull'IA generativa nella scienza, ha indicato che l'integrazione pi\u00f9 profonda e progressiva dell'IA nei flussi di lavoro scientifici \u00e8 inevitabile. <\/span><\/p>\n

    I partecipanti hanno evidenziato numerosi vantaggi: 66% hanno notato che l'IA consente un'elaborazione pi\u00f9 rapida dei dati, 58% ritengono che migliori i calcoli e 55% hanno affermato che fa risparmiare tempo e denaro.<\/span>\u00a0<\/span><\/p>\n

    Tuttavia, 53% ritenevano che i risultati potessero essere irreplicabili, 58% erano preoccupati per i pregiudizi e 55% ritenevano che l'IA potesse consentire ricerche fraudolente.\u00a0<\/span><\/p>\n

    Ne abbiamo osservato la prova quando i ricercatori <\/span>ha pubblicato un articolo<\/span><\/a> con diagrammi senza senso generati dall'intelligenza artificiale nella rivista Frontiers - un ratto con testicoli giganti, nientemeno. Comico, ma ha mostrato come la revisione paritaria possa non cogliere nemmeno gli usi pi\u00f9 evidenti dell'IA.\u00a0<\/span><\/p>\n

    In definitiva, come ogni strumento, l'IA \u00e8 sicura ed efficace quanto l'uomo che la utilizza. Un uso incauto, anche se non intenzionale, pu\u00f2 portare la scienza fuori strada.<\/span><\/p>\n

    Le nuove linee guida mirano a mantenere \"oneste le persone oneste\", come ha detto Narayanan. <\/span><\/p>\n

    L'adozione diffusa da parte di ricercatori, revisori e riviste potrebbe stabilire un nuovo standard per l'integrit\u00e0 scientifica nell'era dell'IA.<\/span><\/p>\n

    Tuttavia, costruire il consenso sar\u00e0 una sfida, soprattutto perch\u00e9 la crisi della riproducibilit\u00e0 \u00e8 gi\u00e0 sotto gli occhi di tutti.<\/span><\/p>","protected":false},"excerpt":{"rendered":"

    L'intelligenza artificiale sta trasformando la ricerca scientifica, ma senza una guida adeguata potrebbe fare pi\u00f9 male che bene. Questa \u00e8 la conclusione di un nuovo articolo pubblicato su Science Advances da un gruppo interdisciplinare di 19 ricercatori guidati dagli informatici dell'Universit\u00e0 di Princeton Arvind Narayanan e Sayash Kapoor. Il team sostiene che l'uso improprio dell'apprendimento automatico in tutte le discipline scientifiche sta alimentando una crisi di riproducibilit\u00e0 che minaccia di minare le basi stesse della scienza. \"Quando si passa dai metodi statistici tradizionali a quelli di apprendimento automatico, c'\u00e8 un numero enormemente maggiore di modi per darsi la zappa sui piedi\", ha detto Narayanan, che<\/p>","protected":false},"author":2,"featured_media":11910,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[84],"tags":[105,135],"class_list":["post-11908","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-industry","tag-machine-learning","tag-science"],"yoast_head":"\nPrinceton-led team sounds the alarm: AI poses risks to scientific integrity | DailyAI<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/dailyai.com\/it\/2024\/05\/princeton-led-team-warns-that-machine-learning-brings-peril-to-science\/\" \/>\n<meta property=\"og:locale\" content=\"it_IT\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Princeton-led team sounds the alarm: AI poses risks to scientific integrity | DailyAI\" \/>\n<meta property=\"og:description\" content=\"AI is transforming scientific research, but without proper guidance, it may do more harm than good. That’s the pointed conclusion of a new paper published in Science Advances by an interdisciplinary team of 19 researchers led by Princeton University computer scientists Arvind Narayanan and Sayash Kapoor.\u00a0 The team argues that the misuse of machine learning across scientific disciplines is fueling a reproducibility crisis that threatens to undermine the very foundations of science. “When we graduate from traditional statistical methods to machine learning methods, there are a vastly greater number of ways to shoot oneself in the foot,” said Narayanan, who\" \/>\n<meta property=\"og:url\" content=\"https:\/\/dailyai.com\/it\/2024\/05\/princeton-led-team-warns-that-machine-learning-brings-peril-to-science\/\" \/>\n<meta property=\"og:site_name\" content=\"DailyAI\" \/>\n<meta property=\"article:published_time\" content=\"2024-05-02T18:41:38+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2024-05-04T17:24:35+00:00\" \/>\n<meta property=\"og:image\" 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