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#naturallanguageprocessing

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Harald Klinke

Uncovering AI bias in digital collections

Museums are using data science and NLP to detect and contextualize derogatory language in legacy catalog records. A case study from the Harvard University Herbaria shows how digital stewardship can promote ethical access and institutional transparency.

aam-us.org/2025/06/29/improvin
#DigitalStewardship #MuseumTech #EthicalAI #GLAM #BiasDetection #AIethics #CollectionsManagement #DataScience #NaturalLanguageProcessing #DigitalHumanities #Museums

Continued thread
Christof Schöch

After the conference & day trip, it was time for meetings. We did plenty! I took part in delegation meetings with #AcademiaSinica and #NTU (National Taiwan Univ.), but there were many more. We learned about exchange opportunities, memoranda were signed, and we presented each others' research priorities and focus areas, and found lots of overlap! #Biodiversity #WorldLiterature #Sinology #DigitalHumanities #ArtifcialIntelligence #NaturalLanguageProcessing, to name just a few. #tcdh #UTrier

Hacker News

UCSD: Large Language Models Pass the Turing Test

arxiv.org/abs/2503.23674

arXiv.orgLarge Language Models Pass the Turing TestWe evaluated 4 systems (ELIZA, GPT-4o, LLaMa-3.1-405B, and GPT-4.5) in two randomised, controlled, and pre-registered Turing tests on independent populations. Participants had 5 minute conversations simultaneously with another human participant and one of these systems before judging which conversational partner they thought was human. When prompted to adopt a humanlike persona, GPT-4.5 was judged to be the human 73% of the time: significantly more often than interrogators selected the real human participant. LLaMa-3.1, with the same prompt, was judged to be the human 56% of the time -- not significantly more or less often than the humans they were being compared to -- while baseline models (ELIZA and GPT-4o) achieved win rates significantly below chance (23% and 21% respectively). The results constitute the first empirical evidence that any artificial system passes a standard three-party Turing test. The results have implications for debates about what kind of intelligence is exhibited by Large Language Models (LLMs), and the social and economic impacts these systems are likely to have.
#HackerNews#UCSD#LLM
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Marcel SIneM(S)US

Dieser Artikel ist zwar schon etwas älter, aber er lässt einen doch etwas nachdenken ...

#ChatGPT besteht den #TuringTest, gilt KI jetzt als intelligent? - Das Netz ist politisch dnip.ch/2024/07/15/chatgpt-bes @nohillside #Chatbot #ElizaEffekt #ArtificialIntelligence #NaturalLanguageProcessing #NLP #MaschineLearning #DeepLearning #Digitalisierung #digitalization #MenschMaschineInteraktion #PromptEngineering #DNIP

Das Netz ist politisch · ChatGPT besteht den Turing-Test, gilt KI jetzt als intelligent? - Das Netz ist politischIn verschiedenen Medien (LiveScience, Slashdot, Heise, Netzwoche) wurde vor einigen Wochen berichtet, dass ChatGPT (konkret ChatGPT 4.0) den Turing-Test
Paul M. Heider

Starting today's #AMIA2024 sessions with the @NLP (#NaturalLanguageProcessing, #ClinicalNLP) Working Group Pre-Symposium Workshop. Felix Morales opened with a talk on "ML Pipeline Flags Instances of ARDS from EHR" (medrxiv.org/content/10.1101/20).

I'll have a talk later in this session called "Approximate Randomization Technique
for Comparing Performance Between Demographic Subgroups" (Sample code: codeberg.org/HeiderLab/article)

medRxiv · Open-source machine learning pipeline automatically flags instances of acute respiratory distress syndrome from electronic health recordsPhysicians could greatly benefit from automated diagnosis and prognosis tools to help address information overload and decision fatigue. Intensive care physicians stand to benefit greatly from such tools as they are at particularly high risk for those factors. Acute Respiratory Distress Syndrome (ARDS) is a life-threatening condition affecting >10% of critical care patients and has a mortality rate over 40%. However, recognition rates for ARDS have been shown to be low (30-70%) in clinical settings. In this work, we present a reproducible computational pipeline that automatically adjudicates ARDS on retrospective datasets of mechanically ventilated adult patients. This pipeline automates the steps outlined by the Berlin Definition through implementation of natural language processing tools and classification algorithms. First, we used labeled chest imaging reports from two different hospitals over three different time periods to train an XGBoost model to detect bilateral infiltrates, and a subset of attending physician notes from one hospital labeled for the most common ARDS risk factor (pneumonia) to train another XGBoost model to detect a pneumonia diagnosis. Both models achieve high performance when tested on out-of-bag samples—an area under the receiver operating characteristic curve (AUROC) of 0.88 for adjudicating chest imaging reports, and an AUROC of 0.86 for detecting pneumonia on attending physician notes. Next, we integrate the models and validate the entire pipeline on a fourth cohort from a third hospital (MIMIC-III) and find a sensitivity of 93.5% — an extraordinary improvement over the 22.6% ARDS recognition rate reported for these encounters — along with a false positive rate of 18.8%. We conclude that our reproducible, automated diagnostic pipeline exhibits promising ARDS retrospective adjudication performance, thus providing a valuable resource for physicians aiming to enhance ARDS diagnosis and treatment strategies. We surmise that real-time integration of the pipeline with EHR systems has the potential to aid clinical practice by facilitating the recognition of ARDS cases at scale. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement Feihong Xu was supported in part by the National Institutes of Health Training Grant (T32GM008449) through Northwestern University's Biotechnology Training Program. Curtis H. Weiss was supported by the National Heart Lung and Blood Institute (R01HL140362 and K23HL118139). Luís A. Nunes Amaral was supported by the National Heart Lung and Blood Institute (R01HL140362). Luís A. Nunes Amaral and Feihong Xu are supported by the National Institute of Allergy and Infectious Diseases (U19AI135964). ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Institutional Review Board of Northwestern University gave ethical approval for this work (STU00208049). Institutional Review Board of Endeavor Health gave ethical approval for this work (EH17-325). I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes The datasets analyzed in this study will be made available upon peer-reviewed journal publication at ARCH repository hosted by Northwestern University. The Python code to reproduce the reported results will be made available upon peer-reviewed journal publication at the Amaral Lab GitHub repository. <https://arch.library.northwestern.edu> <https://github.com/amarallab>
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