As machine learning technology continues to shock the world, popular artificial intelligence tools such as natural language processing may generate unforeseen issues for humanity.
For instance, natural language processing can have implicit biases, create a significant carbon footprint, and stoke concerns about AI sentience. Natural language processing is a field in machine learning where a computer processes human language through vast amounts of data to understand, translate, extract, and organize information. However, the language processing tools such as Open AI’s Chat GPT and other tools run into some challenges, such as misspellings, speech recognition, and the ability of a computer to understand the nuances of human language.
One of the biggest rising concerns regarding natural language processing is artificial intelligence programs’ ability to have implicit bias and perpetuate stereotypes. One of the most essential tasks of natural language learning models is to study and learn patterns from data sets in order to understand how humans communicate with one another. Sometimes, these data sets can have implicit bias thinking that may affect how an AI learns the language and communicates its findings.
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For example, suppose a dataset has language that assigns certain roles to men, such as computer programmers or doctors but assigns roles, like homemaker or nurse, to women. In that case, the AI program will implicitly apply those terms to men and women when communicating in real time. Therefore, stereotypes existing within the data set can lead to algorithms having language that applies unfair stereotypes based on race, gender, and sexual preference.
Political bias is another real concern for natural language processing programs that may lead to the impression of information based on the political preference of the data set used to train the AI. For instance, in February 2023, ChatGPT users discovered that the language processing program refused to communicate information about the Hunter Biden laptop story and speak about former President Donald Trump positively despite doing the same for President Joe Biden.
The political biases of machine learning language processing tools often result directly from the programmer or the dataset it is trained with. If the programmer refuses to correct those biases, it often leads to the suppression of news and information that may anger one side of the political spectrum.
Read below to discover other controversies and concerns regarding natural language processing.
Coherence versus sentience
One concern that individuals have had about the AI industry for years is a machine learning programs’ ability to seemingly think for themselves and express feelings. Natural language processing models are often the version of AI that concerns individuals in this regard due to the computer’s ability to mimic and present written text in a way that expresses the same emotions and thought patterns as humans.
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However, just because an AI program is coherent or as the ability to readily generate information does not mean the machine is sentient. It is not possible for AI to register experiences or feelings because it does not have the ability to think, feel, or perceive the world with a sentient mind.
Artificial intelligence, in general, but specifically natural language processing models, creates an environmental footprint that is comparable to the oil industry. Data mining, which is essential for the existence of artificial intelligence, consumes a large amount of electricity which releases carbon dioxide into the air. For instance, the data mining generated from cryptocurrency and AI-related programs between 2021-22 was responsible for an excess of 27.4 million tons of carbon dioxide into the air.
Natural language processing is a lucrative commodity yet has one of the largest environmental impacts out of all the other fields in the artificial intelligence realm. The process used to train, experiment, and fine-tune a natural language process model has been estimated to create on average more CO2 emissions than two Americans annually.
Some natural language processing programs that use neural architecture search created even more CO2 emissions that experts have estimated to be nearly five times more than the carbon footprint of a normal American car driver.
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