AI, The Good, The Bad and The Ugly 

The Intellectual and Environmental Ethics of Artificial Intelligence 

For the past years, artificial intelligence (AI) has had a rather prevalent impact on our lives: from assembling cars to determining which ads one is exposed to on social media. However, the emergence of generative AI, as a new category of technological resources, has taken the world by storm, with OpenAI’s ChatGPT alone reaching 300 million weekly active users in December 2024 (Singh, 2025) and, thus, having major implications not only on the environment but also on the unique human ability to envision and create. According to Gartner, AI-driven data analysis is set to account for more than 50% of all business analytics by 2025, while Forbes reports that AI-powered advertising tools can increase ROI by up to 30% compared to traditional methods.  

In fact, as you read this sentence, generative AI programs may already be developing email prompts, debugging your code, and even preparing your dinner’s recipe simultaneously.  

With the of AI usage re-shaping the way one works and interacts, as well as the possible rise of DeepSeek, which is projected to surpass ChatGPT’s performance, (Wiggers, 2025) clear benefits are defined, as studies predict 40% productivity improvements (MIT Sloan, 2023). Nevertheless, its groundbreaking promise to improve performance has been tempered, as of late, with growing concerns that these intricate and mystifying systems may do more societal harm than economic good, namely regarding creative outlooks and academic integrity (UNESCO, n.d). 

As people progressively feel the immense rush of having more and more automated activities in their lives while companies hurry to improve efficiency, one should stop to think and ask: 

What are the trade-offs for such benefits?

Intellectual Property

“And your novel?” 
“Oh, I put in my hand and rummage in the bran pie.” 
“That’s so wonderful. And it’s all different.” 
“Yes, I’m 20 people.” 

– Virginia Woolf and Lytton Strachey

 Retrieved from In the Margins: On the Pleasures of Reading and Writing 

Creation is a complex and often unappreciated place, where the creative must give shape to wild, wanderer, unstructured ideas – many times, rummaging in the bran pie to see what comes out – to form a cohesive original piece. The realization that this type of work must be protected, so as to justify its high stakes, gave birth to the concept of intellectual property.  

According to the World Intellectual Property Organization (WIPO), intellectual property (IP) refers to “creations of the mind, such as inventions; literary and artistic works; designs; and symbols, names and images used in commerce”. IP is protected by law: the Intellectual Property Rights (IPR), which encompass the right to be credited for their own work; to uphold their integrity; for others not to use the artists’ work without permission… Generative AI comes to challenge those pre-established rules.  

By giving birth to unseen imagery with the utilization of prompts, creating adapted screenplays set up on the scenery of your favorite novels, and even developing catchy songs about the dean of your school – always surprisingly fast –, AI is increasingly taking its place at the creatives’ desk. But there is a catch: GenAI does not materialize exactly original elements. Rather, the tools are based on massive amounts of data, which are used to train them into recovering patterns that then enable the response to the prompt (MIT Sloan 2021).  

This can become problematic when one starts to ask if there is ownership of the content that is provided to train Generative AI. This matter has already been brough up in the courtrooms. For example, Andersen v. Stability AI et al., in 2022. Various artists filed a class-action copyright infringement lawsuit against several AI organizations, claiming unauthorized use of their work for AI training (Harvard Business Review 2023). Ultimately, the courts’ decisions are going to add to the interpretation of the fair use doctrine.   

Artists around the world are also starting to take the matter into their own hands. One of the most impactful cases of such traces back to the Writers Guild of America strike, that marked 2023. The culmination of this event consisted of an agreement which, among other things, laid ground for the establishment of artificial intelligence use. Although artists may use AI tools in their work, companies are prohibited from forcing them to do so – which would probably result in the drafting of lower paying contracts. More importantly, now “the WGA reserves the right to assert that exploitation of writers’ material to train AI is prohibited by MBA or other law” (Vox 2023). 

AI’s Role in Academic Integrity 

One has to be honest in one’s work, acknowledge others’ work properly, and give credit where one has used other people’s ideas or data.”  

– Campbell & Waddington, 2024 

Academic integrity is a critical component in education and research work within today’s rapidly evolving academic landscape as it reflects the value of the qualifications offered by an institute, as well as the ethical conduct of students. It regards the collective activity of students and teachers to demonstrate courtesy toward intellectual property and uphold moral and ethical standards in academic works. According to the European Network for Academic Integrity (ENAI), this concept includes “compliance with ethical and professional principles, standards, practices and consistent system of values that serves as guidance for making decisions and taking action in education, research, and scholarship.”. 

With the growing presence of generative AI, students and academic researchers are supported in various aspects, including data analysis, decision-making and writing. AI has, in this sense, revolutionized the academic world, offering unmatched assistance. Nevertheless, its rapid integration into the sector, as well as its inability to understand and produce authentic scholarly work, raises concerns on students’ critical thinking capacities, plagiarism and overall academic integrity.   

In fact, a study conducted with a sample of 5894 students across Swedish universities highlights a growing dependency on AI tools, with over 50% of positive responses to the use of chatbots, and over a third of students affirming the regular reliance on Large Language Models (LLM), such as ChatGPT in education (Malmström et al. 2023). As AI tools are becoming progressively user-friendly, barriers to its wide adoption are significantly reduced. Namely, ChatGPT and similar AI applications can serve as self-learning tools, assisting students in acquiring information, answering questions and resolving problems instantaneously, thereby enriching learning experiences and offering personalized support.  

However, despite its potential to enhance academic work, people’s perceptions around its misuse for academic shortcuts still indicate mixed responses (Schei et al. 2024). The debate further extends to ethical territory, as AI-facilitated plagiarism and academic misconduct becomes increasingly prevalent and possibly encourages a culture of intellectual laziness and plagiarism practices, such as Mosaic Plagiarism: which involves taking phrases from a source without crediting them or copying another person’s ideas and replacing these with synonymic phrase structures but for proper crediting (Farazouli et al. 2023). 

Data sets used by LLMs often rely on information collected through data scraping from third-party websites and published work. While this practice is not necessarily considered misconduct, it may be obtained without explicit consent from the sources, meaning that it is possible for one’s AI-generated work or writing material to contain non-credited phrases and ideas. One example of such occurrence lies within the lawsuit infringed upon Open AI by the New York Times for copyright issues and unauthorized use of published content to train AI models (The New York Times 2023). Furthermore, critics also point out generative AI’s technical limitations and existing bias dependent on its training data, as it may create incorrect or outdated information, leading to extended reliability concerns.  
As AI becomes more deeply integrated in academia, without proper education, its misuse and over-reliance are a prominent motive for concern. 

Environmental Impact and Water Consumption  

Another factor to account for when addressing AI usage and reliance is its environmental impact, which is not often considered by end-users.  

As worldwide corporate AI investments experienced exponential growth in the past years, from $12.75B in 2015 to $91.9B in 2022 (Statista 2024), so does its impact on water consumption since AI models (especially GPT-4) require significant energy and water resources to its function. 

When assessing water consumption in data centers, one should account for both its “onsite” direct use to cool servers, and its indirect use as an energy generator.  (OECD.AI n.d.) 

Furthermore, the data centers require the use of fresh water for refrigeration through cooling towers, liquid cooling, or air conditioning, while power plants supplying electricity also need large amounts of water. Thus, training and running AI models can consume millions of liters with even small AI questioning using significant amounts, as these consume 1.8 to 12 liters of water per kWh of energy.  

AI’s water usage is, thereby, a growing concern, its growing water demands outpacing energy efficiency and being projected to reach up to 6.6B cubic meters (approximately 6 times of Denmark’s annual water withdrawal) (Li et al. 2025). 

The hazard that AI imposes on the environment goes far beyond the hydrological issue discussed. 

In a study carried out by Strubell et al. (2020), it was demonstrated that the carbon dioxide emissions associated with the training of a single type of common natural language processing (NLP) model greatly surpassed the values that are attributed to familiar consumption. Namely, the training of an AI model under such conditions yields approximately 600,000 lb of carbon dioxide emissions, whereas using a car for a lifetime produces one fifth of the same amount. 

Of course, there is also a concern with the amount of energy used by artificial intelligence facilities. In such regard, Alex De Vries (2023) found out in a study that, by 2027, the AI industry could be consuming between 85 to 134 terawatt hours (Twh) annually, which compares to the amount of energy used by a small country such as the Netherlands. Additionally, GenAI tools may use nearly 33 times more energy to carry out a task than task-specific software would (World Economic Forum 2024). What is more, the extraction of natural resources that integrate the components of AI hardware can constitute a source of worry. In an interview, Yale’s Associate Professor Yuan Yao explains that the supply chain of these parts requires partaking in activities such as mining and metal production, that may lead to soil erosion and pollution.  

Interestingly, Wang et al. (2024) suggest that the amount of e-waste (discarded electrical or electronic devices) generated could end up comprising a total of 1.2–5.0 million tons until 2030, depending on the pace of the industry’s growth. According to the World Health Organization, if e-waste is unreliably recycled, it can release up to a thousand different chemical substances, including known neurotoxicants such as lead.  

As one becomes aware of the ethical concerns that come with AI development, and therefore its use, we can start to address these issues: by both reflecting on policies that can be implemented to mitigate the harm of such outbreaking technology and aiming to make more considerate and sustainable use of GenAI.  

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Sources:

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