diff --git a/docs/assets/images/blogs/chennai-healthcare.svg b/docs/assets/images/blogs/chennai-healthcare.svg deleted file mode 100644 index 7c153d4..0000000 --- a/docs/assets/images/blogs/chennai-healthcare.svg +++ /dev/null @@ -1,10 +0,0 @@ - diff --git a/docs/assets/images/blogs/digital-twins.svg b/docs/assets/images/blogs/digital-twins.svg deleted file mode 100644 index 819a8ee..0000000 --- a/docs/assets/images/blogs/digital-twins.svg +++ /dev/null @@ -1,10 +0,0 @@ - diff --git a/docs/assets/javascripts/xwhy-nav.js b/docs/assets/javascripts/xwhy-nav.js index 99b1724..e304d93 100644 --- a/docs/assets/javascripts/xwhy-nav.js +++ b/docs/assets/javascripts/xwhy-nav.js @@ -45,6 +45,7 @@ const researchLinks = [ ["Research overview", "research/"], ["Publications", "research/publications/"], + ["Blogs", "research/reddit/blogs/"], ["Citation guidance", "research/citation/"] ]; diff --git a/docs/assets/stylesheets/extra.css b/docs/assets/stylesheets/extra.css index f0b52c7..f8a9a6a 100644 --- a/docs/assets/stylesheets/extra.css +++ b/docs/assets/stylesheets/extra.css @@ -374,6 +374,10 @@ grid-template-columns: repeat(4, minmax(0, 1fr)); } +.md-typeset .xwhy-blog-grid--stories { + grid-template-columns: repeat(2, minmax(0, 1fr)); +} + .md-typeset .xwhy-blog-card { background: var(--md-default-bg-color); border: 1px solid var(--md-default-fg-color--lightest); diff --git a/docs/research/reddit/blogs.md b/docs/research/reddit/blogs.md index c549d01..d90f337 100644 --- a/docs/research/reddit/blogs.md +++ b/docs/research/reddit/blogs.md @@ -17,7 +17,7 @@ hide:

Research group stories

Browse the original Hull research blog -
+
- -
diff --git a/docs/research/reddit/blogs/digital-twins-chennai.md b/docs/research/reddit/blogs/digital-twins-chennai.md deleted file mode 100644 index 2b104da..0000000 --- a/docs/research/reddit/blogs/digital-twins-chennai.md +++ /dev/null @@ -1,28 +0,0 @@ ---- -title: Digital Twin Research in Chennai -description: Research connections between digital twins, dependable intelligent systems and wind energy. ---- - -[← All research and insights](../blogs.md){ .xwhy-blog-back } - -# Digital twin research in Chennai - -![An original illustration of a city and its digital twin](../../../assets/images/blogs/digital-twins.svg) - -*Digital twins · Research note based on [the Hull research group visit report](https://www.responsibleaihull.com/post/responsible-digital-twin-research-theme-in-chennai-india), which credits Xinhui Ma as blog author.* - -A digital twin connects observations of a physical system with a model that can help people understand and test its behaviour. For a wind turbine, such a system might bring together sensor readings, an engineering model and predictions about operating conditions. The usefulness of the twin depends on whether its model remains dependable when the physical system or its environment changes. - -## A research exchange across disciplines - -During a University of Hull visit to IIT Madras in October 2024, the research team discussed digital twins, wind energy, intelligent systems and simulation. Topics included physics-based and machine-learning predictions, sensor development, operations and maintenance. The group also visited facilities at the National Technology Centre for Ports, Waterways and Coasts. - -The meeting linked researchers working on physical infrastructure, AI and engineering. A digital twin used for operational decisions needs more than a good forecast: it also needs clear assumptions, an account of uncertainty and checks that its outputs continue to match observations. - -## Where explanations can help - -When a prediction changes, engineers need to know whether a new sensor reading, a change in operating conditions or a modelling assumption drove it. A local explanation can help investigate a particular output, provided its fidelity and stability are checked. It is one form of evidence alongside engineering constraints and direct measurements. - -The visit was an exchange about research directions, not a report of a newly deployed digital twin or a measured improvement in turbine performance. - -**Original report:** [Responsible Digital Twin Research Theme in Chennai, India](https://www.responsibleaihull.com/post/responsible-digital-twin-research-theme-in-chennai-india). This page is an original XWhy summary of the visit and its research context. diff --git a/docs/research/reddit/blogs/responsible-ai-chennai.md b/docs/research/reddit/blogs/responsible-ai-chennai.md deleted file mode 100644 index fe0f7fd..0000000 --- a/docs/research/reddit/blogs/responsible-ai-chennai.md +++ /dev/null @@ -1,28 +0,0 @@ ---- -title: Responsible AI Research in Chennai -description: Lessons from a University of Hull research exchange about trustworthy AI in healthcare. ---- - -[← All research and insights](../blogs.md){ .xwhy-blog-back } - -# Responsible AI research in Chennai - -![An original illustration of a city skyline and research collaborators](../../../assets/images/blogs/chennai-healthcare.svg) - -*Research visit · Research note based on [Koorosh Aslansefat's Hull research group report](https://www.responsibleaihull.com/post/research-visits-of-responsible-ai-team-to-chennai-india).* - -Researchers from the University of Hull visited Chennai in October 2024 to discuss how AI could be developed and evaluated responsibly in healthcare. Meetings with researchers and clinicians explored what happens when a model that appears promising in a laboratory meets the variety of patients, images and clinical practices in real services. - -## Three connected questions - -At IIT Madras, discussions addressed responsible AI research, privacy and the use of AI in clinical tasks. Clinicians from the Manipal Academy of Higher Education described the difficulty of transferring advice across different healthcare policies and patient populations. At the Madras Diabetes Research Foundation and Sankara Nethralaya, the conversations also covered representative data, diagnostic applications and the need for meaningful visual evidence in eye care. - -These settings pose practical questions for any model: Does its training data represent the people and conditions it will encounter? Can clinicians understand the evidence for an output? What happens when the input is poor quality or a recommendation is outside the system's competence? - -## Bringing explainability into the evaluation - -A method such as SMILE can help examine which input regions or terms are associated with changes in a particular prediction. It should be checked against clinical knowledge and paired with tests of accuracy, uncertainty and failure under changing conditions. A local explanation cannot replace a clinician's judgement or a well-designed evaluation across sites. - -The visit report describes an exchange of perspectives and prospective collaborations. It does not claim that a medical AI system was clinically validated during the visit. - -**Original report:** [Research Visits of Responsible AI Team to Chennai, India](https://www.responsibleaihull.com/post/research-visits-of-responsible-ai-team-to-chennai-india). This page is an original XWhy summary, with further context in the source report. diff --git a/properdocs.yml b/properdocs.yml index 0521eec..e16e2fc 100644 --- a/properdocs.yml +++ b/properdocs.yml @@ -95,8 +95,6 @@ plugins: - research/reddit/blogs.md: Research blogs and XWhy explainers - research/reddit/blogs/image-editing-smile.md: Explaining image-editing instructions - research/reddit/blogs/fairness-skin-cancer-ai.md: Fairness in skin cancer AI - - research/reddit/blogs/responsible-ai-chennai.md: Responsible AI in healthcare research visit - - research/reddit/blogs/digital-twins-chennai.md: Digital twin research visit - research/reddit/blogs/how-smile-works.md: How SMILE works - research/reddit/blogs/reading-local-explanations.md: Interpreting local explanations - research/reddit/blogs/explaining-llm-responses.md: Explaining LLM responses