---
title: "Ethics of artificial intelligence"
chunk: 3/12
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"
---

==== Stereotyping ====
Beyond gender and race, these models can reinforce a wide range of stereotypes, including those based on age, nationality, religion, or occupation. This can lead to outputs that unfairly generalize or caricature groups of people, sometimes in harmful or derogatory ways. For instance, scholars highlighted how AI systems can reproduce and amplify global inequalities, particularly when data and model development are concentrated in Western countries, raising concerns about fairness and representation in AI systems.
Such stereotypes stem directly from the design of AI systems and programmatic models from which they are trained. Stereotypes that target specific demographics originate from societal biases embedded during the programming process, outdated datasets, and algorithmic architectures that prioritize high-ranking and majority groups rather than underrepresented ones. Research also amplifies user feedback as a primary contributor to stereotypes within AI, as human interactions introduce bias. Additionally, the AI industry is a male-dominant field, primarily young adult males, creating a lack of diversity that cultivates inequalities in AI databases. Word embeddings reveal that the use of "person/people" within AI algorithms displays gender inequality, as it prioritizes men over women rather than neutrality.

==== Language bias ====
AI is primarily trained on English. Celeste Rodriguez Louro has argued that mainstream American English is the primary variety of English used to train generative AI systems, resulting in a linguistic bias toward homogeneity and the exclusion of other varieties of English. Since current large language models are predominantly trained on English-language data, they often present Western views as truth, while systematically downplaying non-English perspectives. As of 2024, most AI systems are trained on only 100 of the 7,000 world languages.

==== Political bias ====
Language models may also exhibit political biases. Since the training data includes a wide range of political opinions and coverage, the models might generate responses that lean towards particular political ideologies or viewpoints, depending on the prevalence of those views in the data. This skewing of the data is known as algorithmic bias, or when an AI has a predisposition to certain answers based on the data that the AI was trained on. This can create an AI system that is not giving objective answers, but rather skewed answers that lean towards differing ends of the political spectrum. It is said that ChatGPT is a more liberal skewed AI model. It has been found that users are more likely to agree with answers that coincide with their existing political beliefs. Some AI systems try to gauge the political affiliation of the user so that the generated answers can be politically skewed to align with the user, leading to a never-ending confirmation bias loop. It is more difficult for users to perceive a political bias if they already align with the answer, allowing these AI companies and programmers to ultimately get away with their politically biased AI models.

=== Dominance by tech giants ===
The commercial AI scene is dominated by Big Tech companies, including Alphabet Inc., Amazon, Apple Inc., Meta Platforms, Microsoft,  and SpaceX. Some of these players already own the vast majority of existing cloud infrastructure and computing power from data centers, allowing them to entrench further in the marketplace. Their current dominance within the market of technology makes it very hard for newer companies to compete and be successful in the long-run within the industry. It has been suggested by competition law scholars that the tech giants of the world may be using their power within the market to foreclose the market from potential competitors and, in turn, charge higher prices to consumers. In light of some of these concerns, governments around the world have been considering and implementing laws that would prevent large companies from continuing or executing these practices. These tech giants have the money that it takes to build the infrastructure needed nowadays. The five biggest are projected to spend $602 billion in 2026 on capital expenditures alone, which would be a 32% increase from the year prior. In this spending, it is estimated that 75% will go towards AI-specific infrastructure. With the significant growth that has been seen in the tech industry with AI, it is important to keep the industry competitive and fair. 

=== Climate impacts ===

The largest generative AI models require significant computing resources to train and use. These computing resources are often concentrated in massive data centers. The resulting environmental impacts include greenhouse gas emissions, water consumption, and electronic waste. Despite improved energy efficiency, the energy needs are expected to increase, as AI gets more broadly used.

==== Electricity consumption and carbon footprint ====
These resources are often concentrated in massive data centers, which require demanding amounts of energy, resulting in increased greenhouse gas emissions. A 2023 study suggests that the amount of energy required to train large AI models was equivalent to 626,000 pounds of carbon dioxide or the same as 300 round-trip flights between New York and San Francisco.