The integration of machine learning with automated experimentation in self-driving laboratories (SDL) offers a powerful approach to accelerate discovery and optimization tasks in science and engineering applications. When supported by findable, accessible, interoperable, and reusable (FAIR) data infrastructure, SDLs with overlapping interests can collaborate more effectively. This work presents a distributed SDL implementation built on nanoHUB services for online simulation and FAIR data management. In this framework, geographically dispersed collaborators conducting independent optimization tasks contribute raw experimental data to a shared central database. These researchers can then benefit from analysis tools and machine learning models that automatically update as additional data become available. New data points are submitted through a simple web interface and automatically processed using a nanoHUB Sim2L, which extracts derived quantities and indexes all inputs and outputs in a FAIR data repository called ResultsDB. A separate nanoHUB workflow enables sequential optimization using active learning, where researchers define the optimization objective, and machine learning models are trained on-the-fly with all existing data, guiding the selection of future experiments. Inspired by the concept of ``frugal twin", the optimization task seeks to find the optimal recipe to combine food dyes to achieve the desired target color. With easily accessible and inexpensive materials, researchers and students can set up their own experiments, share data with collaborators, and explore the combination of FAIR data, predictive ML models, and sequential optimization. The tools introduced are generally applicable and can easily be extended to other optimization problems.

AI algorithms can still come loaded with racial bias, even if they're trained on data more representative of different ethnic groups, according to new research. An international team of researchers analyzed how accurate algorithms were at predicting various cognitive behaviors and health measurements from brain fMRI scans, such as memory, mood, and even grip strength. Medical datasets are often skewed – they're not collected from a diverse enough sample size, and certain groups of the population are left out or misrepresented. It's not surprising if predictive models that try to detect skin cancer, for example, aren't as effective when analyzing darker skin tones than lighter ones. Biased datasets are often the source for why AI models are also biased.

In machine learning, an important balance to maintain is that between adjusting the search space and environment specification (see e.g. If the problem is not well defined the learning outcomes will likely not be useful. However, in practice defining the search space is a large part of the work of a data scientist, and another approach could be to formulate the data requirements for every model version. This would mean starting from the learning goals rather than from the existing data, and describing the requirements for data content, quantity and quality in a systematic way. These requirements can be used to identify or generate the datasets needed to develop the model. The FAIR principles are an excellent starting point to facilitate the creation of this feedback loop between data generators (data entry systems, lab equipment, data processing pipelines, et cetera) and data scientists.

For life science companies, healthcare providers, patients, and consumers, AI offers great potential to streamline processes and achieve better treatment results. Dr. Anna Bauer-Mehren describes the role of real-world data, data science or data analysis in pharmaceutical research and the resulting new opportunities for personalized medicine. In particular, she addresses the importance of high-quality data and Roche's efforts to make data FAIR. In their view, this is essential for the success of AI methods in R&D. Using several examples, she shows in which areas of pharmaceutical research AI is already being used successfully, but also discusses which areas still have great challenges.