New research warns of risks of bias as AI shapes decision-making on offshore energy development

The use of artificial intelligence (AI) is becoming increasingly common when deciding where offshore energy projects such as wind farms, oil platforms and pipelines should be placed in the ocean.

New research co-authored by Jason Chuah Professor of Commercial and Maritime Law at The City Law School suggests that while beneficial, the downsides and potential risks associated with using AI need to be examined further, specifically the potential effects of introducing hidden biases into marine spatial planning (MSP).

Marine spatial planning

UNESCO defines MSP as ‘a public process of analysing and allocating the spatial and temporal distribution of human activities in marine areas to achieve ecological, economic and social objectives that have been specified through a political process’.

Essentially, MSP assigns uses for different parts of the ocean that can allow offshore energy projects to be carried out after taking into consideration the multiple stakeholders involved and their respective goals. It therefore can have significant consequences on the use of the marine space for various stakeholders and communities. If decisions are made on the basis of bias, certain marine communities or users could well be disadvantaged.

Benefits of AI usage

Of course, AI systems hold many potential benefits over human-led marine planning. During planning, AI is readily able to identify underground structures such as faults, folds and traps, can detect potential hydrocarbon accumulations (i.e. striking oil), and significantly reduce the speed of interpretation time of large amounts of data from weeks to hours.

Undoubtedly, the introduction of AI systems has provided ground-breaking developments within MSP, making planning far more efficient.

Reducing bias

Nevertheless, the risk of certain biases must be accounted for when examining the reliability of these systems – the quality of their decision-making will only be as good as the quality of the data they receive.

The current study, supported by the Taiwanese Ministry of Science and Technology, explored approaches that may be harnessed and put in place so that AI can be used effectively within MSP with reduced bias - maximising its accuracy and utility.

To reduce the risk of bias, the study stresses the importance of human oversight when using AI generated ‘recommendations’ rather than permitting these models to entirely dictate decision making. Being transparent with the use of AI can also build trust and provide a better understanding of the science needed for a proper implementation of MSP.

For example, including local communities who possess ‘long-term ecological knowledge’ that AI models might miss, is one solution suggested from the research.

More broadly, ensuring that under-represented groups such as fishers, coastal communities, port authorities and conservation groups are consulted prior to adopting AI-generated plans could also enable greater data governance.

As many stakeholders do not currently have equal access to data, this may exclude them from decision making in a valuable manner, jeopardising the validity of the execution and implementation of MSP policies at all levels.

Regulating bias

One approach the research explored was that of the EU AI Act which was developed in 2024 to address the risks associated with the use of AI technologies more generally.

The Act uses a hierarchy of ‘risk’ associated with AI product products classified as unacceptable, high, limited, and minimal - determining whether products should be prohibited. While it was not formed with directly addressing bias in mind, the authors have used its analysis of risk as a starting point for trying to reducing bias in MSP.

This research states that it is yet to be clarified whether AI systems used in MSP fall within the high-risk category.

Other methods of regulating bias were explored which included regional and local governance of marine activity, with legal frameworks enforced upon companies involved in MSP to mitigate the entry of bias into their data that may be used in AI systems in MSP.

In summary

The study highlights the growing need for an increase in transparency and reduction of bias when implementing AI within marine governance.

Reflecting on the study, Professor Chuah said:

AI can definitely pick up offshore oil and gas patterns faster than any geologist — but it can't spot its own blind spots. My concern is that if we let algorithms quietly redraw the marine spatial map, we'd better make sure fishers and coastal communities still hold the pen, not just the eraser.

Read the article in the Journal of World Energy Law & Business

Article by Neo Valverde-Sebunya, Press Communications Assistant