# A Helping Hand: A Survey About AI-Driven Experimental Design for Accelerating Scientific Research
## 3 Results
### Discussion
Particularly, AL is capable of integrating human knowledge for dealing with scarce data, enhancing learning efficiency and speed. Other general but potential solutions to overcome the data bottleneck include the use of transfer learning, few-shot learning, and data augmentation techniques [236; 237]. An approach for integrating both human knowledge and generative AI to deal with scarce data is membership query synthesis. Here, generative AI is applied to generate synthetic data based on real experimental data while incorporating the knowledge of human scientists in terms of AL to enhance data quality [238]. In addition to data scarcity, the complexity of the data is often high due to the high-dimensional parameters and search spaces. Multi-objective-based optimization frameworks and reinforcement learning aim to handle this problem. Although MOBO is suitable for low-data environments, it may struggle to achieve a good trade-off between multiple objectives when data are too scarce or with insufficient computational resources. An advantage of RL over optimization methods is the planning of multi-step strategies, while optimization methods suggest one experiment at a time. Apart from this, [PERSON] et al. [233] showed that RL is also able to learn experimental design without prior knowledge, leading to high generalizability. In conclusion, an ideal framework could integrate AL with RL, referred to as reinforcement active learning, to address both limited data availability and the complexity arising from high-dimensional parameter and search spaces. A preferable improvement for the methodologies discussed, besides data augmentation, is DL as a state of the art methodology. It enhances the adaptation to newly received data, capturing complex patterns in high-dimensional spaces, thereby increasing predictive accuracy. However, the reviewed literature lacks a framework that incorporates DL. One reason is the previously mentioned data scarcity. DL models typically require large, labeled datasets to generalize effectively. Moreover, the computational cost and infrastructure requirements for training and deploying DL models can be prohibitive in laboratory settings that prioritize real-time decision-making and flexibility. Additionally, DL models are often treated as black boxes, which raises concerns about interpretability and explainabilitywhen AI is expected to guide scientific decisions. In general, the explainability of AI-driven experimental design decisions remains largely unaddressed, with only one of the reviewed frameworks ([PERSON] et al. [224]) considering this aspect as a main research objective by incorporating a combination of an RF classifier and LASSO regression. However, some frameworks, even if not explicitly stated, deploy explainable and interpretable methodologies and are therefore considered explainable. Examples include purely tree-based frameworks ([PERSON] et al. [217], [PERSON] et al. [221], [PERSON] et al. [232]) or approaches that achieve partial explainability by integrating interpretable surrogate models, such as GP regression ([PERSON] et al. [218], [PERSON] et al. [219], [PERSON] et al. [234]) or tree-based models ([PERSON] et al. [231]). Future research should prioritize explainability to develop reliable and trustworthy frameworks and ensure acceptance among scientists. An important aspect to consider when implementing AI-driven experimental design frameworks is their ethical dimension. In the case of fully autonomous frameworks, they offer tremendous advantages, but several critical questions arise: Who is responsible if AI designs and conducts harmful, unnecessary, or costly experiments? Does the use of fully autonomous systems assist scientists by relieving them of monotonous, time-consuming tasks, or does it risk excluding them from the creative experimentation process? Furthermore, how can the risk be mitigated that AI frameworks inherent biases from their training data? High transparency in the AI's decisions and the operational process is needed to ensure the trust of scientists.