Algorithmic bias, a term denoting the consistent and repeatable inaccuracies in a digital system leading to unjust results, often favors one demographic at the expense of others. Such bias can infiltrate at different points, such as during data gathering, coding, or while making design decisions. It can reflect existing social and institutional biases, or it can uniquely develop within digital environments. Prominent instances are seen in biases within search engine outputs, social media algorithms, and machine learning systems. Algorithmic bias can also appear in more specific forms, like gender, political, or technical biases. Moreover, it considerably influences various sectors, including business realms, judicial systems, and online platforms, resulting in discriminatory actions and rights infringements.
Biais algorithmique describes systematic and repeatable errors in a système informatique that create "unfair" outcomes, such as "privileging" one category over another in ways different from the intended function of the algorithm.
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 résultats des moteurs de recherche et les plateformes de médias sociaux. 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 Règlement général sur la protection des données (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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