# A Survey on Bias in Deep NLP
## 1 Introduction  
In sociology, bias is a prejudice in favor or against a person, group or thing that is considered to be unfair. Since, on one hand, it is a extremely pervasive phenomena, and on the other hand, deep neural networks are intended to discover patterns in existing data, it is known that human-like semantic biases are found when applying machine learning to ordinary human related results, such as computer vision [1], audio processing [2] and text corpora [3, 4]. All these fields are relevant as constituents of automated decision systems. An \"automated decision system\" is any software, system or process that aims to automate, aid or replace human decision-making. Automated decision systems can include both tools that analyze datasets to generate scores, predictions, classifications or some recommended action(s) that are used by agencies to make decisions that impact human welfare, which includes but is not limited to decisions that affect sensitive aspects of life such as educational opportunities, health outcomes, work performance, job opportunities, mobility, interests, behavior and personal autonomy. In this context, biased artificial intelligence models may make decisions that are skewed towards certain groups of people in these applications [5]. [PERSON] et al. [6] found that an algorithm widely used in US hospitals to allocate health care to patients has been systematically discriminating against black people, since it was less likely to refer black people than white people who were equally sick to programs that aim to improve care for patients with complex medical needs. In the field of computer vision, some face recognition algorithms fail to detect faces of black users [7] or labeling black people as \"gorillas\" [1]. In the field of audio processing, it is found that voice-dictation systems recognize a voice from a male more accurately than that from a female [2]. Moreover, regarding predicting criminal recidivism, risk assessment systems are likely to predict that people of some certain races are more presumably to commit a crime [8]. In the field of deep natural language processing (deep NLP), word embeddings and related language models are massively used nowadays. These models are often trained onlarge databases from the Internet and may encode stereotyped biased knowledge and generate biased language. Such is the case of dialog assistants and chatbots when using biased language [9], or resume-review systems that ranks female candidates as less qualified for computer programming jobs because of biases present in training text, among other NLP applications. [PERSON] et al. [10] propose the Word Embedding Association Test (WEAT) as a way to examine the associations in word embeddings between concepts captured in the Implicit Association Test (IAT) [11], in the field of social psychology, intended to assess implicit stereotypes held by test subjects, such as unconsciously associating stereotyped black names with words consistent with black stereotypes. This problem is far from being solved, or at least attenuated. Currently, there are no standardized documentation procedures to communicate the performance characteristics of language models in spite of some efforts to provide transparent model reporting such model cards [12] or Data Statements [13]. In addition, the new models use document collections that are getting larger and larger during their training, and they are better able to capture the latent semantics in these documents, it is to be expected that biases will become part of the new model. This is the case of GPT-3 [14], a state-of-the-art contextual language model. GPT-3 uses 175 billion parameters, more than 100x more than GPT-2 [15], which used 1.5 billion parameters. Thus, [PERSON] et al. [14] report findings in societal bias, more concisely regarding gender, race and religion. Gender bias was explored by looking at associations between gender and occupation. They found that 83% of 388 occupations tested were more likely to be associated with a male identifier by GPT-3.