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Viés algorítmico

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Algorithmic bias is a term that refers to the systematic and repeatable errors in a computador[4] system that create unfair outcomes, such as privileging one group to the detriment of others. This type of bias can be introduced at various stages, including during data collection, programming, and when making design choices. It can mirror pre-existing social and institutional biases, or emerge uniquely within digital contexts. Notable examples include biases in motor de busca[2] results, redes sociais[3] algorithms, and machine learning[1] systems. Algorithmic bias can also manifest in more specific forms, such as gender, political, or technical biases. Furthermore, it has significant impacts in various sectors, including commercial spheres, legal systems, and online platforms, leading to discriminatory practices and violations of rights.

Definições de termos
1. ↑ machine learning. Machine learning, a term coined by Arthur Samuel in 1959, is a field of study that originated from the pursuit of artificial intelligence. It employs techniques that allow computers to improve their performance over time through experience. This learning process often mimics the human cognitive process. Machine learning applies to various areas such as natural language processing, computer vision, and speech recognition. It also finds use in practical sectors like agriculture, medicine, and business for predictive analytics. Theoretical frameworks such as the Probably Approximately Correct learning and concepts like data mining and mathematical optimization form the foundation of machine learning. Specialized techniques include supervised and unsupervised learning, reinforcement learning, and dimensionality reduction, among others.
2. ↑ motor de busca. Um motor de pesquisa é uma ferramenta vital que funciona como parte de um sistema de computação distribuído. É um sistema de software que responde às consultas dos utilizadores fornecendo uma lista de hiperligações, resumos e imagens. Utiliza um sistema de indexação complexo, que é continuamente atualizado por web crawlers que extraem dados de servidores Web. No entanto, alguns conteúdos permanecem inacessíveis a estes rastreadores. A velocidade e a eficiência de um motor de pesquisa dependem muito do seu sistema de indexação. Os utilizadores interagem com os motores de pesquisa através de um navegador Web ou de uma aplicação, introduzindo consultas e recebendo sugestões à medida que escrevem. Os resultados podem ser filtrados para tipos específicos e o sistema pode ser acedido em vários dispositivos. Esta ferramenta é importante porque permite aos utilizadores navegar na vasta Web, encontrar conteúdos relevantes e recuperar informações de forma eficiente.
Artigos relacionados:
Viés algorítmico (Wikipédia)

Viés algorítmico describes systematic and repeatable errors in a sistema informático that create "unfair" outcomes, such as "privileging" one category over another in ways different from the intended function of the algorithm.

A flow chart showing the decisions made by a recommendation engine, c. 2001

Bias can emerge from many factors, including but not limited to the design of the algorithm or the unintended or unanticipated use or decisions relating to the way data is coded, collected, selected or used to train the algorithm. For example, algorithmic bias has been observed in search engine results e social media platforms. This bias can have impacts ranging from inadvertent privacy violations to reinforcing social biases of race, gender, sexuality, and ethnicity. The study of algorithmic bias is most concerned with algorithms that reflect "systematic and unfair" discrimination. This bias has only recently been addressed in legal frameworks, such as the European Union's Regulamento Geral sobre a Proteção de Dados (2018) and the proposed Artificial Intelligence Act (2021).

As algorithms expand their ability to organize society, politics, institutions, and behavior, sociologists have become concerned with the ways in which unanticipated output and manipulation of data can impact the physical world. Because algorithms are often considered to be neutral and unbiased, they can inaccurately project greater authority than human expertise (in part due to the psychological phenomenon of automation bias), and in some cases, reliance on algorithms can displace human responsibility for their outcomes. Bias can enter into algorithmic systems as a result of pre-existing cultural, social, or institutional expectations; by how features and labels are chosen; because of technical limitations of their design; or by being used in unanticipated contexts or by audiences who are not considered in the software's initial design.

Algorithmic bias has been cited in cases ranging from election outcomes to the spread of online hate speech. It has also arisen in criminal justice, healthcare, and hiring, compounding existing racial, socioeconomic, and gender biases. The relative inability of facial recognition technology to accurately identify darker-skinned faces has been linked to multiple wrongful arrests of black men, an issue stemming from imbalanced datasets. Problems in understanding, researching, and discovering algorithmic bias persist due to the proprietary nature of algorithms, which are typically treated as trade secrets. Even when full transparency is provided, the complexity of certain algorithms poses a barrier to understanding their functioning. Furthermore, algorithms may change, or respond to input or output in ways that cannot be anticipated or easily reproduced for analysis. In many cases, even within a single website or application, there is no single "algorithm" to examine, but a network of many interrelated programs and data inputs, even between users of the same service.

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