# Dealing with Gender Bias Issues in Data-Algorithmic Processes: A Social-Statistical Perspective
## 1 Introduction  
As pointed out by some of the pioneers of machine learning, one of the main problems inherent to the logic of computer systems is their inability to dissociate the information they handle from the context in which they are created [1]. This generates the need for overcoming the dichotomy between the social and technical perspectives, which makes it difficult to address social issues such as the existence of bias in the development of data-algorithmic processes [2]. The conception of data-algorithmic processes as complex socio-technical systems, and not just as autonomous technical methods, contributes to add a social perspective to the debate [3]. Algorithmic-related bias refers to systematic and repeatable errors in a mathematical or computer system that lead to 'unfair' outputs, privileging one or more groups over others. Gender bias in data-algorithmic processes is a particular type of bias where one of the genders is discriminated. Some authors have associated the presence of gender bias with the under-representation of women in the design and production of artificial intelligence (AI) products and services [4, 5, 6]. The number of AI applications has been increasingly growing during the last decades, which cover a wide range of fields: from natural language generation to face recognition. At the same time, the concern regarding AI/machine learning (ML) and gender bias has also increased significantly (Figure 1). For example, the widespread use of popular word embedding algorithms exhibiting stereotypical biases--including gender bias-in ML systems can thus amplify stereotypes in several contexts. For this reason, some methods have been developed to mitigate this problem [7]. Examples of methods for evaluating bias in text are the word embedding association test (WEAT) and the word embedding factual association test (WEFAT). Thesehave implications not only for AI/ML, but also for other fields, such as Psychology, Sociology, and Human Ethics, since these methods raise the possibility that mere exposure to everyday language can account for the biases replicated by ML techniques [8]. This reinforces the importance of developing an interdisciplinary analysis on the presence of biases in data-algorithmic processes, and how these biases might guide decisions that do not represent the diversity and complexity of modern societies. Likewise, recent technological advances and the extensive use of algorithms raise ethical problems, particularly those prompted by algorithmic decision-making [9]. The potential biases in algorithm decision-making have encouraged several research on the effects of AI in the development of the different UNESCO's sustainable goals. Authors such as [PERSON] et al. [10] and [PERSON] and [PERSON] [11] formulate affirmation such as \"algorithms are not ethically neutral\". In this context, our paper analyzes whether data-algorithmic processes are sexist -by first providing definitions of the algorithm concept, considering both social and technical perspectives-, and an introduction to the concept of data-algorithmic bias. In addition, examples of gender bias in AI applications, based on different fields such as natural language processing (NLP), speech recognition, decision management, and face recognition are explained. Afterwards, a review of works presenting methods to detect and mitigate gender bias in AI applications is offered, as well as a list of private initiatives and recommendations from international organizations. Likewise, a numerical example regarding discrimination due to data bias, a simple yet effective methodology to solve the issue, and a set of general recommendations for any AI practitioner interested in detecting and mitigating gender bias are also discussed. The rest of the paper is structured as follows: Section 2 defines the concept of algorithm, both from a technical and a social scientist's point of view, and reviews an algorithm classification by function, implementation, and design paradigm.