Algorithmic bias

Algorithmic bias occurs when a computer system behaves in ways that reflects the implicit values of humans involved in that data collection, selection, or use. Algorithmic bias has been identified and critiqued for its impact on search engine results, social media platforms, privacy, and racial profiling. In search results, this bias can create results reflecting racist, sexist, or other social biases, despite the presumed neutrality of the data. The study of algorithmic bias is most concerned with algorithms that reflect "systematic and unfair" discrimination.As algorithms expand their ability to organize society, politics, institutions, and behavior, sociologists have become concerned with the ways unanticipated output and manipulation can impact the physical world. Because algorithms are often considered to be neutral and unbiased, they can inaccurately project greater authority than human expertise, and in some cases, reliance on algorithms can displace human responsibility for their outcomes. Nonetheless, bias can enter into algorithmic systems as a result of pre-existing cultural, social, or institutional expectations; because of technical limitations of their design; or through use in unanticipated contexts or by audiences not considered in their initial design.Algorithmic bias has been discovered or theorized in cases ranging from election outcomes to the spread of online hate speech. Problems in understanding, researching, and discovering algorithmic bias may come from the proprietary nature of algorithms, which are typically treated as trade secrets. Even with full transparency, understanding algorithms can be difficult because of their complexity, and because not every permutation of an algorithm's input or output can be anticipated or reproduced. 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.