How the energy crisis is unfolding the deep-rooted biases

By Dominique Keizer

The current energy crisis is evident in every aspect of our daily lives. Rising electricity bills and increased food prices are just examples of how we are becoming forced to rethink the sources of our energy consumption. Efi Koutsokosta & Jorge Liboreiro, two columnists from Euronews, looked at how institutions such as the European Commission are now challenged to develop measures to procure future prices and seek economic stability. One such initiative involves a joint procurement scheme to increase the purchasing power of the European Union (EU) and lower the prices. Yet, given the diversity of energy sources and mixes, most EU countries still seem to be resistant to signing the so-called “solidarity agreement”. With rising awareness of the challenges behind conventional power grids perhaps this is the time to address sustainability and innovative solutions. Yet, one should remember that a solution is not always applicable to everyone. Undoubtedly this energy crisis will spark creative schemes as to how to lessen the dependency on fuel-based energy sources and instead integrate renewable energy sources (RES) with the already existing conventional ones. As shall be discussed in this article, the current innovations regarding energy use might have unprecedented social consequences if uncritically applied. Specifically, Artificial Intelligence (AI) and its solution-driven role in the field of engineering might create a way forwards in the current crisis if inequity shall be appropriately included. Without taking into account gender or race, AI could exaggerate the already existing structural barriers.

At this exact moment, the engineering industry is highly pressured to sustainably manage the fragile balance of supply and demand within the energy sector. The challenge lies in conventional power grids not being designed to efficiently integrate renewable energy sources such as wind, solar or hydrogen. Practically speaking, the threat is in meeting the variable loads of the power grid.

For that rea- son, scholars such as Tanveer Ahmad and Dongdong Zhang have researched how the energy industry can incorporate AI son, scholars such as Tanveer technology to perform the task of forecasting and operating the power system efficiently. The forecast is expected to reduce uncertainty through purchase management and distribution schedules. At first, one might argue this could be a solution to the current power crisis. However, it is relevant to not oversee that technologies also affect the way individuals interact and understand their reality. Before diving into the social aspect, it is imperative to mention that technological developments definitely have the potential to benefit both individuals and the environment in a sustainable way. The implementation of AI in projects on biodiversity has allowed for instance to map the global biodiversity loss. Dr. Bistra Dilkina, a computer scientist at Georgia Tech, is applying computing skills to optimise biodiversity corridor planning for different species. When looking at the field of energy engineering, AI has also helped in processing complex decision-making such as the environmental impact of solar and wind farm sites.

AI shall undoubtedly become the future of how we plan our way forwards in the post-energy crisis reality. As mentioned at the beginning of the article, the question remains whether everyone shall equally benefit from these innovations. It is seldom discussed that the unaddressed bias in data used to train AI algorithms might create new problems. Ricardo Vinuesa & Hossein Azizpour together with other scholars have recently published a paper on how both racial and gender bias in, for instance, face recognition tools portray the problems that AI practitioners still have to overcome. A positive future of AI is only possible if we start questioning our perceptions of reality. In the end, the bias found in machine learning is a mirror of what we believe to be the truth.

When putting the gender bias of energy consumption into a broader context, it can be noted how energy poverty is unequally experienced within homes. One of the areas in which AI is implemented involves smart home management. This is to allow customers easier access to energy management through virtual power systems. Yet, as has been researched by Saska Petrova & Neil Simcock, ‘the home’ continues to be addressed as a homogenous entity without gender differences. What the current energy crisis has shown is that energy vulnerability is gendered. Specifically, the increase in energy prices is what has revealed and reiterated traditional gender roles.

Women are culturally expected to take care of household responsibilities. As a result, they face a disproportionate consequence of energy poverty. Limiting energy consumption in the house includes tactics such as adjusting the heating, limiting vacuuming or doing laundry during specific times. These activities are mostly undertaken by women which leads to increased emotional and physical labour. Feminised pro-environmental behaviour is happening at the expense of women’s time. It is the different energy-related roles of household members that eventually reshape vulnerabilities.

With that in mind, one could ask why women cannot change their reality by joining the decision-making process. Given their experiences of energy saving would it not be efficient to hear their voices? It is not that easy, however, as it may seem. Glass-ceiling into leadership positions is still a reality for many women. Even though the recruitment of women and girls is seemingly on the rise, they leave at a crucial point in their careers. Barriers are still there when it comes to career advancement. According to the International Renewable Energy Agency (IRENA) in fields such as science, technology, engineering or mathematics (STEM) the average share of women accounts for only 28%. In the oil and gas industry, the share of women is only 22%.

Social and cultural norms tend to be the main barrier to diversifying the workforce with women being expected to take care of the household and childcare. Studies done by IRENA have shown that the work-life balance becomes problematic for women if the work schedule is not adjusted to family and household duties through for instance flexible work hours. Lack of career information and networks, misperceptions of career pathways and mobility requirements form another obstacle. Now with the fastgrowing advancement of AI in many engineering fields, it is more relevant than ever to create opportunities for women’s engagement.

The relevance of questioning the inclusivity of the new energy solutions can be seen in how Machine Learning (ML) tends to re- inforce the already visible gender bias. As covered by Tom Simonite, a previous San Francisco bureau chief at MIT Technology, a simple example of this bias has been shown by databases that portray women with domestic chores and men with sports. Besides the gender dynamic, racial bias is still a dominant problem as well. Computer scientist Joy Buolamwini wrote for TIME how most facial analysis softwares do not recognise dark-skinned faces, given the system is trained on light-skinned men. That is how the assumption of machines being neutral is wrong.

Partnership with gender experts and participatory design could help to recognise the gender and racial impacts of an algorithm. This is the critical time to address how technologies affect individuals before AI can become part of the new every day. Scientist Joy Buolamwini also described how failed machine learning might also amplify sexist hiring practices, spread false information or make the criminal justice procedures racist. The same applies to the energy sector. Energy vulnerability is just a continuation of the invisible discursive script of what is considered the norm. Energy infrastructure is not gender-neutral, as some would assume. Without a critical assessment of how reality is experienced the promised breakthrough of technological solutions could reproduce systematic discrimination.

The already direct impact of this exclusionary decision-making in the transition from fossil fuel to renewable energy can be seen in the distributional inequity faced by

Sámi reindeer herders in the north of Sweden. Columnist for The Guardian Karen McVeigh has written that forestry and mining projects are already expanding into traditional grazing grounds. In the last century alone the undisturbed reindeer habitat has become smaller by approximately 70%. This includes the floods of grazing lands while building the infrastructure for hydroelectric power. Besides the shrinking of the natural habitat of the reindeer, the Sámi people are also troubled by climate change. Scholar Dorothee Cambou from the University of Helsinki has shown in her research that the Arctic is affected by the warming temperatures of climate change four times more than the rest of the world. Meaning, it is the Sámi people who understand the impact of human expansion into untouched environments. Yet, similarly as in the case of women’s contri- bution to energy projects, the Sami communities face recognition obstacles to participate in the development of initiatives such as wind energy.We definitely need to change how we consume energy. However, let's not forget that the challenges faced by the Sami community could be a future mirror of how exclusion might look like.

Without a doubt, AI will be the new norm in our efforts to become more sustainable and energy efficient. The already existing initiatives such as the corridor planning for different animal species show that solutions do exist and there is still hope. But these solutions will only become solutions if we start questioning the algorithms. ♦