{"id":890,"date":"2025-10-20T15:25:44","date_gmt":"2025-10-20T15:25:44","guid":{"rendered":"https:\/\/www.data-empowerment.nl\/?p=890"},"modified":"2025-11-07T14:03:42","modified_gmt":"2025-11-07T14:03:42","slug":"chitaly-2025","status":"publish","type":"post","link":"https:\/\/www.data-empowerment.nl\/index.php\/2025\/10\/20\/chitaly-2025\/","title":{"rendered":"CHItaly 2025"},"content":{"rendered":"
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\n \t<\/i> Read Time:<\/span>6 Minute, 2 Second <\/div>\n\n <\/div>\n

This year’s CHItaly conference took place in beautiful Salerno. It featured extensive workshops and keynote presentations that provided inspiration for continuing work on conversational agents and trust. The theme of the conference was \u201cTechnologies and Methodologies of Human-Computer Interaction in the Third Millennium.\u201d Below are snippets from the event, although they do not provide a complete overview, as the conference covered a wide range of topics.<\/p>\n\n\n\n

Language: \u2018Written word (institutional) represents the real world.\u2019<\/strong>
How LLMs \u2018understand\u2019 the written word is tricky, but to get a grip on this, listening to linguistic experts gave a good impression. A concrete example was a project on Gender Sensitivity. The project wants to create a tool that aids in writing more gender sensitive texts with AI. But the research done by Giulia Pedrini on the complexity of annotating and understanding the context behind what is written, shows that for humans there could be many ways of interpreting \u2018they\/them\u2019 (plural or singular) in regulation or statutes texts, then how can an LLM or GenAI generate the appropriate word. Next to that, the workshop gave an impression on how law has a big impact on how these texts are written in conflict with the constitution where it is forbidden to discriminate yet one has the right to express themselves. Discrimination by only using the masculine \u2018he\u2019, in text, against others who do not identify as such, but also, does the law apply to them only? Next to that, LLMs are trained in texts that have these forms in them, so what will come out is the same. Thus, using a model to help with gender sensitive writing will become problematic, and here lies the challenge.<\/p>\n\n\n\n

Hallucinations: they could be mitigated by training the model controllingly and structuring the vocabulary between what terms and concepts and labelling them as such. This way, the model will understand the principles of how humans\u2019 reason, and so one can model cultural knowledge.<\/em><\/pre>\n\n\n\n
Other research projects focused on analysing what comes of a GenAI model and comparing them, but it was not clear based on what or more loosely and holistically. This makes the analysis a bit random.<\/p>\n\n\n\n
Health and Digital Inclusion
<\/strong>The health sector deals with challenges, including having time to diagnose a patient or going through a huge amount of data to predict a disease. The Hessian.ai <\/a>initiative looks into these issues. An example is using visual analytics to give more explanations on how a predictive model came to its predictions, and so it would be more possible to understand the model and manipulate it. An example is predicting schizophrenia by using biomarkers and additional data, such as EEG and linguistics, to produce a more reliable prediction. Visual analytics could also aid doctors in visualising the patient\u2019s history in one overview, as the average time a doctor has for a patient is 6-8 minutes. Yet, risks arise when documenting patients\u2019 symptoms in the database, as each patient has their own way of communicating the symptoms. Other examples of the project include using social robots to aid 65+ seniors with extra cognitive load, to keep their cognitive load high so that they keep proacting, and this helps them with dementia. Still, such concepts need vigorous testing and involvement of this group, which I am not sure is happening already.<\/p>\n\n\n\n