# Dealing with Gender Bias Issues in Data-Algorithmic Processes: A Social-Statistical Perspective
## 3 Data-Algorithmic Bias: Definitions and Classifications  
Bias is a concept used in statistics and ML to refer to the assumptions made by a specific model [PERSON] (2016). Viewing algorithms as socio-technical systems implies, however, a broader understanding of bias in information systems. [PERSON] and [PERSON] [PERSON] and [PERSON] (2016) use the term bias to refer to computer systems that systematically and unfairly discriminate against certain individuals or groups of individuals in favor of others. According to these authors, a system discriminates unfairly if it denies an opportunity, or assigns an undesirable outcome to an individual, on grounds that are unreasonable or inappropriate. In the same vein, [PERSON] et al. [PERSON] et al. (2018) define bias as the inclination or prejudice of a decision made by an AI system that is in favor of, or against to, an individual or group, especially in a way that is considered unfair. We find general agreement in the scientific literature when it comes to defining computer biases on the basis of the injustices they are likely to generate. Despite this, neither unfair discrimination alone gives rise to bias unless it occurs systematically, nor does systematic discrimination generate bias unless it is accompanied by an unfair outcome [PERSON] and [PERSON] (2016). Since AI relies heavily on data generated by humans -or collected through human-created systems-, it is not strange that any bias that exists in humans is also reflected in our systems. As algorithms are part of existing biased institutions and structures, they are susceptible to reproduce and amplify these biases by favoring those phenomena and aspects of human behavior that are easily quantifiable over those that are difficult or even impossible to measure [PERSON] et al. (2018). [PERSON] and [PERSON] [PERSON] and [PERSON] (2016) were among the first to offer a classification of the types of biases related to algorithms from a socio-technical perspective. Their analysis was based on an analysis of seventeen computer systems from various fields, including banking, commerce, computer science, education, medicine, and law. These authors categorize three types of biases in their work. The first is the pre-existing bias, i.e., when computer systems incorporate biases that exist independently to the creation of the system. The second type of bias described is the technical bias. In contrast to pre-existing bias, technical bias arises from problem solving in technical design. Technical bias can be identified in computer tools, in the de-contextualization of algorithms, in the generation of pseudo-random numbers, or in the formalization of human constructs. Finally, the third type of bias is the emergent bias, which arises only in a context of use, usually some time after a design is completed, as a result of changes in social knowledge, population, or cultural values. Emergent bias may be caused by new knowledge in society that cannot be incorporated into the system design. It can also be a bias that arises when the population using the system differs in some significant dimension from the population assumed as users during the design stage. [PERSON] et al. [PERSON] et al. (2018) present a classification that considerably expands these types of bias. This work offers a particularly complete and updated definition of the biases affecting AI applications. However, its main contribution is the classification of biases beyond the different phases of the algorithmic process in which they fall, i.e.,: in the data, in the creation of the algorithms, or in the interaction with the users. These authors analyze biases in ML from the phenomenon of the feedback loop. This feedback loop is understood as a situation in which the trained ML model makes decisions that produce results, and these same results affect future data to be collected for subsequent training rounds or models.