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Numerous actively evolving AI deployment models in Europe and around the world are moving from the initial emerging stage to a wide range of social science’s sectors. There are already AI-ML algorithmic models and platforms developed specifically to and/or adapted for most active and demanding social/natural sciences’ fields. It is well worth mentioning some most promising theoretical models and some quasi-practical AI’s transitive actions, e.g. in recent European AIs.
Modern AIs: political and economic background
The EU-wide process in adaptation of AI models is in full speed: the deployment of numerous AI in the ever-increasing spheres of science already greatly affects economics and politics, law and employment; the extensive use of AIs is no longer a speculative issue. problem. The AIs are already widespread, and they will only accelerate in the months and years to come: however, the AI use in adaptive political economies and legal/regulatory simplification, meanwhile, will take years to yield results; however, if governments, industries and academic community want to progress, they must fiercely act now!
Just some recent examples in AI’s advantages: i.e. involving AI adoption in some employment and prices issues. For instance, AI reduced smartphone prices by 50 percent while eliminating 25 percent of factory jobs and creating 25 percent more data science positions; in another example, prices remained unchanged while customer service jobs decreased by 25 percent and factory employment stayed constant.
However, AI’s regulatory picture is still quite grim: despite the AI boom, few states around the world have passed comprehensive AI and/or digital legislation: the EU’s AI Act being the notable exception. Thus, if governments want to respond to be on the progressive path, they should quickly pass legislations establishing publicly funded retraining programs to teach workers in dealing with the AI systems, to develop skills necessary for digital transition and automation in sectors susceptible for adaptation of newly created AIs.
Source: https://www.foreignaffairs.com/united-states/coming-ai-backlash?s=EDZZZ005ZX#
In early 2025, the Chinese company DeepSeek released its so-called R1AI model, sending shock waves throughout policy circles around the world and in the US particularly; despite the US export controls on advanced semiconductors, the company managed to develop an open digital technology that could compete with some of the most advanced proprietary American AI models. Many feared that the US leadership in AI might soon be eclipsed: recently, another Chinese company, Moonshot AI, has released a state-of-the-art open model, Kimi K2, that is capable of autonomously achieving complex tasks, prompting some to call it another “DeepSeek-moment”.
https://www.foreignaffairs.com/united-states/chinas-overlooked-ai-strategy
The European breakthroughs in AI deployment
= The EU-funded GRAPHIA project is to create a comprehensive “Social Sciences and Humanities Knowledge Graph-SSH”; part of the project involves another large language model – “LLM4SSH”, tailored to social science/humanities data.
More in: https://cordis.europa.eu/project/id/101188018
= In the broader AI-in-science strategy, the EU intends of building “frontier AI models” that can be specialized to domains, including sectors like manufacturing, pharma and including social science – via the European “AI in Science” strategic framework.
More in: European Commission website – https://commission.europa.eu/news-and-media/news/keeping-european-industry-and-science-forefront-ai-2025-10-08_en.
More, in the European Pharmaceutical Review on specific practical AIs.
= Neuro-symbolic/interpretable AI model on “discoveries in social science”: in the paper “AI-Assisted Discovery of Quantitative and Formal Models in Social Science” (October 2022), the authors from the Cornell University presented a system that helps deriving interpretable and symbolic models (e.g. differential equations or compact functional relationships) from “noisy social science data”, combining machine learning and symbolic search.
Reference to: https://arxiv.org/abs/2210.00563
= In the “AI-assisted discovery of quantitative and formal models in social science” (prepared in 2023) a different attempt is proposed to bridge parametric and non-parametric modeling in social domains. The elaborated system can be used to discover interpretable models from real-world data in economics and sociology.
More in: Balla, J., Huang, S., Dugan, O. et al. AI-assisted discovery of quantitative and formal models in social science. – Humanities and Social Science Community. Vol. 12, 114 (2025). https://doi.org/10.1057/s41599-025-04405-x
= Platforms/experimental systems integrating AI and social science methods: e.g. the Epitome, the first in the world experimental model and an open platform (Cornell University, January 2025) that allows designing experiments to study human–AI interaction, embed “foundation models → applications → feedback” loops, and run controlled social science experiments (dialogues, group chats, agent environments). It is explicitly built at the intersection of AI + social science.
More in: https://arxiv.org/abs/2507.01061
= Another example is the EthicAlly, a prototype system for AI-powered support for research ethics in the social sciences/humanities (January 2025), combining generative AI with structured ethical reasoning to assist in the design of socially sensitive research.
More in: https://arxiv.org/abs/2508.00856
= In evidence synthesis/systematic reviews (a method often used in social science and policy), AI tools are now used to assist search strategy, screening, extraction, summarization, etc. The AI tools can be very useful in different stages of the systematic or other evidence review but that it is important to fully understand any bias and weakness they may bring to the process. In many cases using new AI tools, which previous researchers has not assessed rigorously, should appear in conjunction with existing validated methods. It is also essential to consider ethical, copyright and intellectual property issues, i.e. for example if the process involves uploading data or full text of articles to an AI tool.
Reference to: https://libguides.kcl.ac.uk/systematicreview/ai; it also includes a selection of recently published 14 articles exploring AI tools in evidence synthesis.
= “Traditional” social science models augmented by AI/ML. Tools like EUROMOD (a microsimulation/tax-benefit model, is not actually an “AI models” per se, but can be augmented with machine learning to estimate behavioral responses or more complex relationships. It is a EU-wide tax-benefit model originally maintained, developed and managed by the Institute for Social and Economic Research at the University of Essex. Since 2021 Euromod is maintained, developed and managed by the Commission’s Joint Research Centre, JRC in collaboration with Eurostat and national teams from the EU member states. The model belongs to the class of static microsimulation models and has modules for all 27 EU member states (and the UK until 2021).
Source and citation from: https://en.wikipedia.org/wiki/Euromod
More broadly, standard statistical/econometric/network/agent-based models in social science are being hybridized with machine learning, ML (e.g. embedding features, using regularization and non-linear methods) in many studies; though these are not always “models for social science” in the same way as, say, a domain-tuned LLM.
The US impetus to AI’s deployment
= Supporting toolsets: a team of researchers from Mississippi State University, has developed a new database to support the “production and usage” of social science research. This database houses more 250 different useful AI applications/tools that can help change the way researchers conduct social science research. The program, called “AI tools for social science research”, can be used in social sciences, e.g. for tasks like literature review, data collection, analysis, visualization and dissemination maintained by social science researchers (July,2024).
More in: Social Science Space/Sage – https://www.socialsciencespace.com/2024/07/ai-database-created-specifically-to-support-social-science-research/
= AI models prepared by the Institute for Societal Decision Making (AI-SDM) are used for public health and disaster policy domains; they are not exactly a single approach, but a “research hub” producing human-centric AI tools tailored for complex social decisions.
More in Carnegie Mellon University at: https://www.cmu.edu/ai-sdm/
Challenges and caveats in deploying AI in social sciences
= Interpretability and transparency: in social science, it’s often important to understand why a model gives a result (e.g. for causal inference or policy). Black-box deep models are less helpful unless interpretability is explicitly built in.
= Data heterogeneity and sparsity: social science data are often messy, multilevel, longitudinal, contextual, missing, and with low sample sizes. That makes training large models harder or more prone to overfitting.
= Bias/fairness/ethics: social data has social biases inherently; AI models risk reinforcing or amplifying them. Also, using AI in social science often involves sensitive personal / demographic / identity data, which raises privacy, ethics, and legal concerns.
= Generalization and external validity: an AI model trained on data from one social / cultural / temporal context may not generalize well to another models.
= Integration with domain theory: social sciences often rely on existing theories; purely data-driven AI may conflict or fail to capture theoretically meaningful structure.
AI models for supporting decision-making
There are presently numerous real-time AI decision-support models usable by decision-makers today (in domains like planning, participatory democracy, policy simulation), though many are domain-specific, experimental, or still in hybrid/human-in-loop mode.
The use of such AIs typically requires calibration, context adaptation and embedding into institutional processes.
There are some already existing (and evolving AI “decision-support” models and applications that could be used specifically in public policy, planning, governance and related political-economic domains. However, they generally do not replace human judgment but serve as aides, scenario tools, forecasting engines, and/or structured decision support.
Below are some concrete AI examples, along with caveats and considerations:
= AI Pol.is in civic deliberation and public opinion aggregation platform; it uses AI clustering and ML to surface consensus statements and positions from large-scale input of public opinions; it helps governments and civic platforms see what matters across different opinion groups.
More in: https://en.wikipedia.org/wiki/Pol.is
= LEAM (Land Use Evolution and Impact Assessment Model) for urban/regional planning and land-use/policy impact decisions; it simulates land‐use change under different policy or development scenarios, and integrates with impact assessment modules.
Source: https://en.wikipedia.org/wiki/Land_Use_Evolution_and_Impact_Assessment_Model
= Spatial Decision Support Systems (SDSS) is used for environmental planning, infrastructure and land use; it combines GIS, databases, modeling modules, scenario analysis to support spatial/territorial decisions.
Reference to https://en.wikipedia.org/wiki/Spatial_decision_support_system
= Reflective “hybrid intelligence” frameworks used for the decision support systems, as well as more broadly for proposals and prototypes for hybrid AI + human systems that are self-reflective and provide reasoning/normative insight to decision makers (created in July 2023). The “hybrid human+AI” systems (“human in the loop”) often used in research rather than in fully automated decisions: many tools provide recommendations, counterfactuals, evidential arguments, trade-off visualizations, etc. where the human concept remains central; with reflective hybrid intelligence as another direction.
Source: https://arxiv.org/abs/2307.06159
=Symbolic/hybrid model discovery in social science (created in January 2025) is aimed at deriving interpretable models from social data. Recent research has shown that AI can help generate interpretable models from social/behavioral data (bridging parametric and non-parametric approach) which decision-makers can later inspect and use.
Source: https://www.nature.com/articles/s41599-025-04405-x
Structured models: characteristics and architecture
There are some common patterns in decision-support AI for social contexts:
= Scenario and simulation models: they allow decision-makers to “play out” different policy or intervention scenarios (e.g. “if we invest X here, what happens to land use/traffic/emissions/social outcomes, etc. over 10 years”). LEAM mentioned above is an example.
= Explainability, normative reasoning, and accountability (March 2023). Applicable for use by real decision-makers, in systems often embed modules for explaining why certain suggestions or simulations behave the way they do (in transparency and interpretability). Some newer researchers argued that the trend might shift from “explanations” to “evaluative AI”: i.e. presenting evidence for and against decisions, rather than pushing for a recommendation.
Reference to: https://arxiv.org/abs/2302.12389
= Deliberative-consensus-participatory tools. Systems like Pol.is use machine learning, ML to mediate and filter public opinion, helping decision-makers see cleavages, consensus points and/or latent structures in large-scale feedback.
= Model-discovery and symbolic tools. When decision-makers need interpretable, theory-compatible models (e.g. in social policy), AI tools can help uncover compact functional forms or symbolic relationships from data; recent research called “AI-assisted discovery of quantitative and formal models in social science” is a valuable example (January 2025).
Source: https://www.nature.com/articles/s41599-025-04405-x
Constraints, adoption, barriers and risks
Exiting numerous AI tools/models are still quite limited in their adoption in real decision settings for the following “reasonable complexes”:
= Trust, legitimacy and explainability: decision-makers often require humanly understandable reasoning, provenance of inputs and accountability pathways.
= Regulation and risk: in most regions around the world, the AI systems used in public functions may be subject to regulation (e.g. “high risk” classification under the EU AI Act).
= Data issues and bias: social data is messy, partial, biased and contextual; hence, AI models may pick up unintended associations or amplify inequities.
= Context dependence: AI models built in one region and/or culture may fail elsewhere; hence, attention to possible transfers and adaptations.
= Ethical and democratic oversight: in public policy there is a common temptation “to let AI decide everything”; however, there must be oversight, contestability and human control.
Note. The OpenAI ChatGPT (in 4-5 versions) can be quite useful in e.g. literature exploration and research, as well in concept’s formulations in in the AI-assisted queries using so-called digital “thinking models”.