Artificial Intelligence (AI) is quickly becoming one of science’s most powerful tools. From modeling climate systems to diagnosing rare diseases, AI is helping researchers process more data, faster, and uncover patterns we simply couldn’t see before.
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Thanks to deep learning and the explosion of big data, scientific research is now running at a different pace. We’re seeing breakthroughs in fields like healthcare, where AI helps detect diseases earlier and speeds up drug discovery, and in environmental science, where it's improving how we model climate systems. One standout moment is DeepMind’s AlphaFold, which cracked the long-standing protein-folding challenge—a milestone that could transform biology and medicine.
Generative AI is also gaining ground in drug development, especially for rare diseases. Companies like Recursion and Insilico Medicine are leading the way, and over in Bonn, researchers are using AI to analyze facial features and spot ultra-rare genetic conditions. In short: AI isn’t just helping scientists work faster—it’s helping them ask smarter questions.1
But with all this power, who's making sure AI is used responsibly?
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Why Governance Matters in Scientific AI
With AI touching so many areas of scientific research, the stakes are high. It's not just about faster results anymore; it’s about whether those results are fair, ethical, and transparent.
There are some big challenges here. For starters, AI models rely on massive datasets, many of which aren’t representative of the global population. If most of the training data comes from Western sources, the risk is that models may reinforce existing inequalities or overlook key differences. On top of that, AI systems are often “black boxes”—meaning it’s hard to understand how they reach their conclusions. That’s a serious problem in science, where transparency and reproducibility are non-negotiable.
This is where Responsible AI comes in.
Think of it as a mindset and a set of practices focused on building systems that are fair, accountable, and safe. But good intentions aren’t enough. We need governance—clear rules, strong institutions, and practical processes to make sure AI supports science without undermining it.1-3
Breaking Down the Core Principles of AI Governance
Governance might sound like bureaucracy, but it’s really about building trust. Here are six principles at the heart of AI governance:4
- Transparency
- Technical robustness and safety
- Privacy and data governance
- Human agency and oversight
- Diversity, non-discrimination, and fairness
- Accountability
Let’s unpack what those actually mean in real-world terms.
Transparency
A major concern with AI systems is the lack of understanding about how they function, especially as they become more integrated into daily life. Explainability, a core aspect of transparency, aims to clarify both the processes and decisions of AI systems.
This need has led to the emergence of explainable AI (XAI), which focuses on making complex AI decisions understandable to humans. However, improving explainability can reduce accuracy and vice versa, requiring trade-offs. When AI significantly impacts individuals, timely, clear, and audience-appropriate explanations are essential. Full documentation of data sources, labeling, and algorithms is crucial for transparency, enabling traceability and helping identify and correct errors to prevent future issues.4
Technical Robustness and Safety
Technical robustness in AI focuses on ensuring systems function reliably, proactively managing risks, and preventing anticipated and unanticipated harm. This includes resilience to attacks like data poisoning, model leakage, and infrastructure breaches that can compromise system behavior and outcomes.
To maintain operational integrity, AI systems must have contingency plans to address such threats. The level of safety measures required depends on the system’s capabilities and the potential severity of consequences. Ultimately, robust design is essential for minimizing vulnerabilities and ensuring AI systems remain secure, stable, and trustworthy under various conditions.4
Privacy and Data Governance
AI eats data for breakfast—but not all data should be treated the same.
Scientists need to think about how data is collected, stored, shared, and protected. This is especially important in fields like medicine, where privacy is the law. Good governance means mapping the full data lifecycle and making sure everyone involved knows the rules of the road.4
Human Agency and Oversight
AI can assist, but it shouldn’t replace human judgment, especially in science.
These principles ensure AI systems align with democratic and equitable values. Human agency emphasizes user understanding of AI outcomes, while oversight involves human involvement in AI decision-making.
Key strategies include planning oversight—evaluating technologies before implementation; continuous monitoring—regular system checks in dynamic environments; and retrospective disaster analysis—investigating systems after major failures. These practices ensure responsible, transparent, and adaptable AI use in complex real-world contexts.4
Fairness and Inclusion
Bias in AI isn’t a bug—it’s a reflection of biased data. If we’re not careful, AI systems can end up excluding or misrepresenting whole groups of people. Fairness means designing systems that are inclusive from the start. That includes detecting and correcting for bias in training data and making sure models don’t discriminate based on language, gender, ethnicity, or ability.
The European Commission advocates using mathematical and statistical methods to detect unintended bias, emphasizing fairness as accessibility without unjust prejudice. AI must be user-centric, inclusive of all individuals regardless of age, gender, or abilities. Language-based biases, common in natural language datasets, are especially challenging due to complex linguistic features. Such biases can lead to unpredictable, unfair outcomes when processed by “black-box” models.
To combat this, bias awareness involves addressing data distribution, updating datasets, and grounding data processing in ethical values to minimize disparities and ensure equitable AI outcomes.4
Accountability
When AI makes a mistake, who’s responsible? That question still doesn’t have a clear answer in many cases.
Accountability in AI ensures responsibility and auditability throughout development and deployment. It requires thorough data inspection, focusing on usage, distribution, and representation, alongside error reporting mechanisms for ongoing comparison.
Despite AI’s accuracy, implementing accountability is challenging due to its “black-box” nature, making transparency and auditing crucial for trustworthy AI systems.4
Governance means assigning responsibility, so that when things go wrong, there’s a process for tracing what happened and fixing it. It also means having clear documentation and audit trails from development through deployment.4
How the World is Approaching AI Governance
Around the world, governments and organizations are developing frameworks to guide responsible AI governance and address the risks and opportunities of emerging technologies. So, let's take a look at a few of the main ones.
OECD AI Principles
The Organisation for Economic Co-operation and Development (OECD) has established a set of principles for trustworthy AI that center on human values, fairness, transparency, safety, and accountability. These guidelines aim to support ethical and innovative AI development across sectors.
The principles emphasize inclusive growth, sustainable development, explainability, and respect for human rights. Widely adopted by countries and institutions, they serve as a foundation for building national risk frameworks and shaping both domestic and international AI policies. Notably, the European Union, the United States, and the United Nations align their regulations with the OECD’s definitions and lifecycle model. Together, these elements form the OECD Recommendation on Artificial Intelligence, helping foster global consistency in AI governance.5
NIST AI Risk Management Framework
In the US, the National Institute of Standards and Technology (NIST) released the AI Risk Management Framework (AI RMF) to help organizations tackle AI-related risks head-on. Developed through open collaboration with industry, academia, and government, the framework encourages trust and accountability throughout the AI lifecycle—from concept to deployment.
Since its debut in January 2023, NIST has rolled out a suite of helpful tools like the AI RMF Playbook and Roadmap, and launched the Trustworthy and Responsible AI Resource Center to support implementation. Notably, in July 2024, NIST introduced NIST-AI-600-1, a new profile focused specifically on the unique risks of generative AI, helping organizations align AI risk management with their business goals.6
IEEE Ethically Aligned Design
The IEEE has taken a more values-driven route with its Ethically Aligned Design framework. Rather than prescribing hard rules, it focuses on empowering everyone involved in AI—developers, researchers, companies—to build ethics into their process from day one.
The goal is to ensure AI technologies are designed with humanity in mind. Whether it's a chatbot or an autonomous system, the IEEE framework encourages creators to think deeply about the social impact of their work and the long-term implications of their choices.7
UNESCO Recommendation on the Ethics of Artificial Intelligence
UNESCO’s 2021 Recommendation on the Ethics of Artificial Intelligence has been adopted by 193 countries, making it one of the most widely supported global efforts on AI ethics to date. The recommendation includes tools like a Readiness Assessment Methodology to help governments figure out where they stand and what steps they need to take.
Beyond that, UNESCO’s Global AI Ethics and Governance Observatory acts as a hub for research, policy guidance, and best practices. It even features an AI Ethics and Governance Lab focused on hot-button issues like generative AI, neurotech, and the evolving standards for responsible innovation.8
So What Does This Mean for Scientific Research?
Science has its own values and workflows, like reproducibility, peer review, and open data. If we want AI to really support science, governance needs to fit that context. That means creating rules and processes that reflect how researchers actually work.
Right now, awareness of AI ethics is still limited in the research world. Many scientists simply aren’t trained to think about the broader risks or responsibilities of using AI. And without that awareness, it’s easy to overlook important ethical trade-offs.
Tailored governance strategies can help bridge that gap. Whether it’s adapting oversight processes for lab environments or building better data-sharing agreements across institutions, the goal is to make AI both useful and trustworthy in scientific settings.1,9
The Real-World Challenges (and Why They Matter)
Of course, implementing AI governance is easier said than done. There are some serious roadblocks—like siloed academic disciplines, mismatched data standards, and clunky infrastructure. And in areas like healthcare or climate science, sharing data while respecting privacy is an ongoing challenge.
Take interdisciplinary collaboration, for example. It sounds great on paper, but in practice it runs into a web of complications: academic disciplines often work in silos, data systems aren't always compatible, and research cultures can vary wildly. Add in different publication models and limited collaboration with non-STEM fields, and you’ve got a real coordination problem.
Then there’s the data itself. AI systems rely on massive, well-labeled datasets—but the reality is often messy. The diversity in how data is collected makes human annotation and standardization a real challenge. Getting different institutions and sectors to agree on how to share and govern that data is even harder.
In healthcare, this plays out in especially high-stakes ways. Sharing rare disease data could drive major breakthroughs, but it also has to be done in a way that protects patient privacy and complies with strict regulations. Climate science faces similar obstacles—bringing together environmental data from around the world means dealing with inconsistent time and space scales, patchy security protocols, and limited regulatory oversight.
All of this makes it harder to see the big picture. And when data is fragmented or unreliable, AI tools built on top of it are less trustworthy, less accurate, and ultimately less useful.
Toward Responsible and Trustworthy Scientific AI
AI is here to stay, and its role in science will only grow. But if we want it to serve the public good, we need to make sure it’s aligned with scientific values: transparency, integrity, and equity.
That means investing in open science, building systems that are explainable and auditable, and making sure everyone—not just elite institutions—has access to the tools, data, and expertise needed to use AI well.
AI can help unlock new discoveries. But only if we keep it grounded in the values that make science work in the first place.
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References and Further Reading
- Science in the age of AI [Online] Available at https://royalsociety.org/-/media/policy/projects/science-in-the-age-of-ai/science-in-the-age-of-ai-report.pdf (Accessed on 09 June 2025)
- Ribeiro, D., Rocha, T., Pinto, G., Cartaxo, B., Amaral, M., Davila, N., & Camargo, A. (2025). Toward Effective AI Governance: A Review of Principles. ArXiv. DOI: 10.48550/arXiv.2505.23417, https://arxiv.org/abs/2505.23417
- Batool, A., Zowghi, D., & Bano, M. (2023). Responsible AI Governance: A Systematic Literature Review. ArXiv. DOI: 10.48550/arXiv.2401.10896, https://arxiv.org/abs/2401.10896
- Papagiannidis, E., Mikalef, P., & Conboy, K. (2025). Responsible artificial intelligence governance: A review and research framework. The Journal of Strategic Information Systems, 34(2), 101885. DOI: 10.1016/j.jsis.2024.101885, https://www.sciencedirect.com/science/article/pii/S0963868724000672
- OECD AI Principles overview [Online] Available at https://oecd.ai/en/ai-principles (Accessed on 09 June 2025)
- Overview of the AI RMF [Online] Available at https://www.nist.gov/itl/ai-risk-management-framework (Accessed on 09 June 2025)
- Ethically Aligned Design [Online] Available at https://standards.ieee.org/wp-content/uploads/import/documents/other/ead_v2.pdf (Accessed on 09 June 2025)
- Global AI Ethics and Governance Observatory [Online] Available at https://www.unesco.org/ethics-ai/en (Accessed on 09 June 2025)
- Bano, M., Zowghi, D., Shea, P., & Ibarra, G. (2023). Investigating Responsible AI for Scientific Research: An Empirical Study. ArXiv. DOI: 10.48550/arXiv.2312.09561, https://arxiv.org/abs/2312.09561
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