## 2 Definitions and Related Work  
**Measuring algorithmic biases in machine learning:** Different popular metrics exist to measure algorithmic biases in the machine learning literature. In this paper, we used the True Positive Rate gap (TPRg) [9], which is one of the classic fairness metrics for NLP. Other metrics such as the Statistical Parity [10] or Equalised Odds [11] are also very popular. Over 20 different fairness metrics were compared in [12]. Very important for us, each metric shows specific algorithmic bias properties, and not all of them are compatible with each other [13; 14; 15]. For instance, the True Positive Rate gap quantifies the difference between the portion of positive predictions (\(\hat{Y}=1\) using common ML notation) in two groups, by only considering the observations that should be classified as positive (\(\gamma=1\) using common ML notation). Another popular metric such as the disparate impact will also quantify this difference, but for all observations. This makes their practical interpretation different. **Impact of AI biases in society:** The use of Artificial Intelligence (AI) in decision-making systems has become increasingly widespread in recent years and, with it, concerns about the potential for discriminating biases to affect the outcomes of these decisions. We review below different key studies that have explored the impacts of such biases in AI on society. One such study [16] focused on the criminal justice system and found that AI algorithms can produce biased outcomes, particularly when trained on non-representative datasets. This can result in higher incarceration rates for certain groups, such as racial minorities, and perpetuate systemic racism in the criminal justice system. Another study by [17] explored how gender differences and biases can affect the development and use of artificial intelligence in the field of bio-medicine and healthcare. The paper discussed the potential consequences of these differences and biases, including unequal access to healthcare and inaccurate medical diagnoses. Another important area in which algorithmic biases can impact society is the case of online advertisements. Ad targeting based on demographic factors such as race, gender, and age rather than interests or behaviours can perpetuate negative stereotypes and result in discrimination by limiting access to job or housing announcements for certain groups. For example, Facebook's ad delivery algorithms, by optimising for maximum engagement, can lead to biased outcomes, which result in the amplification of certain groups or messages over others. This can lead to discrimination against certain groups, as advertisers may target their ads to specific demographics or exclude certain groups from seeing their housing and employment advertising, as highlighted by studies such as [18; 19]. **Bias mitigation in NLP:** Bias in NLP systems has received significant attention in recent years, with researchers and practitioners exploring various methods for mitigating bias in NLP models. In this subsection, we review some of the existing work on bias mitigation in NLP. The first approach to mitigate bias is to apply _pre-processing_ techniques to the data used to train the model. Some researchers have proposed methods for removing or neutralising sensitive attributes from the training data, such as gender or race, in order to reduce the likelihood that the model will learn to make decisions based on these attributes. We can reduce the bias directly in the text of the training dataset. For example, in the case of gender bias, like the study in this paper, the most-classic technique is to remove explicit gender indicators [9]. This technique is the one we used to compare our proposed strategy to another one commonly used in industry. This technique is indeed simple to implement and makes it possible to reduce the bias, but in a partial and not very localised manner. Other classical techniques can be used, such as identifying biased data in word embeddings, which represent words in a vector space.