Debunking Technological Neutrality: Exposing and Countering Technological Biases

Debunking Technological Neutrality; Exposing and Countering Technological Biases
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Abstract
The term "technological neutrality" refers to the idea that developers should design and use technology without bias or prejudice. It is often used to describe technology application in society or its neutrality in decision-making. Even though technological neutrality is a widely accepted concept, there is increasing evidence that technology can lead to biased decisions, especially regarding the development of artificial intelligence (AI). Therefore, it is essential to debunk the technological neutrality myth and expose and counter the involved biases. The assumption many people hold about neutrality is that technologies are neutral because they are designed to be unbiased. However, it is challenging for technology to be impartial because its designers use algorithms that limit its activities. They might display the biases of a program ranging from personal experiences to interconnection and neutrality. This write-up will debunk technological neutrality, exposing the prejudices and measures to counter technological tendencies.
The Myth of Technological Neutrality
The myth of technological neutrality is that technology does not have any bias, is neutral, and is up to the user to determine its use, outcomes, implications, and effects. Technological neutrality is a common belief, especially among those within the technology sector, but it is only sometimes accurate. Technologies are designed and developed by people with certain biases, values, and worldviews that influence how a technology works (Delvenne et al., 2019). The preferences could be through the decisions made regarding which features to emphasize, platforms to target, data to collect and how it is used, or even something seemingly innocuous as the language used to interact with a particular piece of technology.
The values will inherently inform the decisions of the people designing and developing them. Those values will be transmitted to users, leading to certain advantages and disadvantages for specific users. Bias could be in terms of the user's gender or race, geographical location, economic standing, or other factors (Delvenne et al., 2019). Furthermore, technology exists within a culture and system informed by bias and values. For example, a student assessment technology may be considered 'neutral.' Still, when used within a school system containing systemic racism, it can perpetuate racism. Therefore, it is essential to acknowledge that technology is not neutral and that it is impossible to create a technology entirely free of bias. It is up to those within the technology sector to be mindful of their preferences and aware of potential technologies' implications.
Debunking Technological Neutrality
Debunking Technological Neutrality is the idea that technology is not neutral, as it can significantly impact society. The concept assumes that technology is unbiased and its effects depend on how it is used. The idea is often used as a justification for not engaging in regulatory activities, implying that regulation would be an unnecessary interference in the "natural" course of technological development. Proponents of debunking technological neutrality argue that technology is affected and shaped by social and political forces. Besides, its effects can be determined by how it is implemented. They suggest that technology is not a neutral actor in society. However, it can reinforce existing power structures and oppression or create new ones. This argument is based on the idea that technology is socially constructed. This means it is designed, manufactured, and used in ways that reflect the target market's values and interests and the cultures in which it is used.
Critics of debunking technological neutrality argue that it ignores the potential benefits of technological development. Therefore, it can lead to overly restrictive regulation that harms innovation and stifles technological progress. They note that technology has a neutral effect because its effects can be positive or negative. Thus, technicians should use regulation to ensure that technological development serves the interests of all parties, including those who benefit from it. Ultimately, the debate over technological neutrality is about who decides how technology is used and the extent to which certain technologies should be regulated by the government or technical industries. Proponents of debunking technological neutrality argue that technology has a social context and that its effects should be considered when developing policies. On the other hand, critics maintain that technological development should be left to the market. Any regulation should be limited to ensuring that technology serves the interests of all parties, including those affected by it.
Technological neutrality is an essential concept in the development and regulation of technology. The article "What Is Technological Neutrality?: Blog Xnoccio Viavansi" by Azahara Benito Carrillo examines the idea of technological neutrality and explains why it matters. The author begins by defining technological neutrality as a concept that "aims to guarantee the same basic rights for all users, regardless of the type of technological equipment they use." The definition highlights the importance of technological neutrality in creating a level playing field for all users (Carrillo, 2018). The article then discusses the importance of technological neutrality in regulation. It argues that rules should be tech-neutral to ensure that all technologies are treated equally. In this regard, neutrality is essential because technology is constantly changing and evolving, and regulations should not favor one type of technology over another.
Carrillo et al. (2018) further discuss the need for technological neutrality in technology development. They argue that technological neutrality should be a guiding principle when it comes to the development of new technologies. It is essential because it ensures that new technology is developed to create a level playing field for all users. The article concludes by emphasizing the importance of technological neutrality in providing a fair and equal society (Carrillo, 2018). It argues that developers must embrace technological neutrality to create a culture where everyone is given the same rights and opportunities regardless of their technology. Overall, the article "What Is Technological Neutrality?: Blog Xnoccio Viavansi" examines the concept of technological neutrality and its importance in the development and regulation of technology. It argues that the government and developers should embrace technological neutrality to ensure fairness and equality in society.
Debunking Technological Neutrality, exposing and countering technological biases refers to recognizing and addressing technology-based biases that can exist in society. While historically a tool for progress, technology often takes on an unintended role as a force for prejudice, inequity, and discrimination. Therefore, debunking technological neutrality is vital in creating a more equitable and inclusive digital landscape.
A great example of this is the concept of algorithmic bias. Algorithms are often used to power decision-making processes, such as job recruitment, mortgage applications, and loan evaluations. However, they can be programmed to include bias. For example, a study conducted by the National Bureau of Economic Research found that online job postings in the United States are more likely to be shown to male applicants than to female applicants, with no difference in qualifications. Another study done by Carnegie Mellon University found that online job applicants with traditionally African-American names were half as likely to receive a response as applicants with traditionally white names. The system of job recruitment is a form of discrimination and inequity, which has the potential to be perpetuated through the use of biased algorithms.
To address technological biases, organizations must first identify areas of potential bias and take steps to manage them. This includes reviewing existing algorithms and data sets for errors, flaws, and inconsistencies and altering algorithms to reduce or eliminate bias (Galván-López et al., 2011). Organizations should also develop practices and protocols for monitoring and auditing algorithms on an ongoing basis. Doing so can help them ensure they remain free of discrimination. In addition, businesses should use ethical frameworks, such as the EU's General Data Protection Regulation (GDPR) and the OECD's Guidelines for Artificial Intelligence, to avoid bias in developing and manipulating algorithms. All of these steps are necessary to ensure that technology is used responsibly and in a manner that promotes equity and inclusion.
Additionally, it is crucial to recognize the "inequality of access" to technology as an essential factor in understanding the implications of technological neutrality. The reason behind the inequality of access is that developers can use technology to strengthen existing structural disparities and biases, stressing the need to ensure that those without access to technology are not disadvantaged.
Common Types of Technological Bias
Technological bias can assume many forms ranging from gender prejudice, racial bias, age discrimination, and recruiting inequality. The underlying reason for technological bias is conscious and unconscious human prejudice in AI algorithms through their development. The second reason is prejudiced assumptions and hypotheses when designing and developing certain technologies. Thirdly is the low quality of data on which artificial intelligence technology is trained, which may incorporate echo societal inequalities and biased human decisions (Osoba et al., 2017). For example, suppose an employer includes an AI recruiting tool introduced in the history of a dominant male society. In that case, the device may duplicate the same results as the recruitment. It also applies to natural language processing algorithms whereby the technological tool will likely display language bias and reinforce existing prejudice. The fourth reason that results in technological bias is how training data is collected and processed, whereby mistakes made by data scientists may lead to various biases ranging from excluding valuable information to sampling, oversampling, and inconsistent labeling. Lastly is the feedback of technology users, which may reinforce bias baked in already existing technological devices.
The first type of technological bias is sampling bias, which occurs when data used to train an AI model does not represent the real-world population. In this case, it can lead to a skewed model since it is only trained on a specific subset of data. For example, if an AI model is trained on a dataset that consists exclusively of men, then it will not be able to predict the behavior of women accurately. The second type of technological bias is reporting bias. The biases arise when the events in the training dataset are entirely different from that in real-world happenings. For instance, it is a fraud detection technological tool that underperforms in remote areas and marks the place's inhabitants with a false high fraud score following a historical investigation of the place as a f...