---
title: "Ethics of artificial intelligence"
chunk: 4/12
source: "https://en.wikipedia.org/wiki/Ethics_of_artificial_intelligence"
category: "reference"
tags: "science, encyclopedia"
date_saved: "2026-05-05T04:23:03.965241+00:00"
instance: "kb-cron"
---

==== Water consumption ====
In addition to carbon emissions, these data centers also need water for cooling AI chips. Locally, this can lead to water scarcity and the disruption of ecosystems. Around two liters of water are needed per each kilowatt hour of energy used in a data center. While data centers use water for cooling AI chips, there are also many indirect uses that negatively impact the environment. Over 80% of total water consumption comes from electricity generation that is used to fuel these large-scale data centers. In addition to this, around 2/3 of data centers built are placed in water-scarce regions. Because of this, AI development can compete with local communities and agriculture for water usage. A lot of companies do not fully disclose the severity of their impact on water consumption, which raises ethical concerns on whether these companies are truly for the people or if they are looking for maximum profit. A solution these data centers have implemented is to use zero-water air-cooling systems, but this results in higher carbon emissions and increased electricity usage. Companies have to decide to prioritize the local concern of water usage or the global concern of carbon emissions. With only a single AI query, 16.9mL of water is used, but only 2.2mL goes towards the cooling of the systems. This is less than 15% of the total water used in the interaction, which exemplifies the severity of indirect water usage.

==== Electronic waste ====
Another problem is the resulting electronic waste (or e-waste). This can include hazardous materials and chemicals, such as lead and mercury, resulting in the contamination of soil and water. In order to prevent the environmental effects of AI-related e-waste, better disposal practices and stricter laws may be put in place.

==== Prospective ====
The rising popularity of AI increases the need for data centers and intensifies these problems. There is also a lack of transparency from AI companies about the environmental impacts. Some applications can also indirectly affect the environment. For example, AI advertising can increase consumption of fast fashion, an industry that already produces significant emissions.
However, AI can also be used in a positive way by helping to mitigate the environmental damages. Different AI technologies can help monitor emissions and develop algorithms to help companies lower their emissions.

=== Open source ===
Bill Hibbard argues that because AI will have such a profound effect on humanity, AI developers are representatives of future humanity and thus have an ethical obligation to be transparent in their efforts. Organizations like Hugging Face and EleutherAI have been actively open-sourcing AI software. Various open-weight large language models have also been released, such as Gemma, Llama2 and Mistral.
However, making code open source does not make it comprehensible, which by many definitions means that the AI code is not transparent. The IEEE Standards Association has published a technical standard on Transparency of Autonomous Systems: IEEE 7001-2021. The IEEE effort identifies multiple scales of transparency for different stakeholders.
There are also concerns that releasing AI models may lead to misuse. For example, Microsoft has expressed concern about allowing universal access to its face recognition software, even for those who can pay for it. Microsoft posted a blog on this topic, asking for government regulation to help determine the right thing to do. Furthermore, open-weight AI models can be fine-tuned to remove any countermeasure, until the AI model complies with dangerous requests, without any filtering. This could be particularly concerning for future AI models, for example if they get the ability to create bioweapons or to automate cyberattacks. OpenAI, initially committed to an open-source approach to the development of artificial general intelligence (AGI), eventually switched to a closed-source approach, citing competitiveness and safety reasons. Ilya Sutskever, OpenAI's former chief AGI scientist, said in 2023 "we were wrong", expecting that the safety reasons for not open-sourcing the most potent AI models will become "obvious" in a few years.

=== Strain on open knowledge platforms ===
In April 2023, Wired reported that Stack Overflow, a popular programming help forum with over 50 million questions and answers, planned to begin charging large AI developers for access to its content. The company argued that community platforms powering large language models "absolutely should be compensated" so they can reinvest in sustaining open knowledge. Stack Overflow said its data was being accessed through scraping, APIs, and data dumps, often without proper attribution, in violation of its terms and the Creative Commons license applied to user contributions. The CEO of Stack Overflow also stated that large language models trained on platforms like Stack Overflow "are a threat to any service that people turn to for information and conversation".
Aggressive AI crawlers have increasingly overloaded open-source infrastructure, "causing what amounts to persistent distributed denial-of-service (DDoS) attacks on vital public resources", according to a March 2025 Ars Technica article. Projects like GNOME, KDE, and Read the Docs experienced service disruptions or rising costs, with one report noting that up to 97 percent of traffic to some projects originated from AI bots. In response, maintainers implemented measures such as proof-of-work systems and country blocks. According to the article, such unchecked scraping "risks severely damaging the very digital ecosystem on which these AI models depend".
In April 2025, the Wikimedia Foundation reported that automated scraping by AI bots was placing strain on its infrastructure. Since early 2024, bandwidth usage had increased by 50 percent due to large-scale downloading of multimedia content by bots collecting training data for AI models. These bots often accessed obscure and less-frequently cached pages, bypassing caching systems and imposing high costs on core data centers. According to Wikimedia, bots made up 35 percent of total page views but accounted for 65 percent of the most expensive requests. The Foundation noted that "our content is free, our infrastructure is not" and warned that "this creates a technical imbalance that threatens the sustainability of community-run platforms".