# Discrimination, Bias, Fairness, and Trustworthy AI
## 4 Analysis of the Variable Bias
The statistical reference is expressed as the deviation of the prediction in contrast with the event's actual occurrence, and the social reference is from the evidence of statistical bias within the data representing a social bias. Second, it recognizes that decisions/predictions reflecting bias and discrimination should not be normatively unfair. This means that decisions which are unfair and reflect biases must not only be assessed quantitatively but also evaluated with regard to their context with a case-by-case approach. This is to understand how to avoid them and create a norm/standard rather than being the exception to the rule. Additionally, third, it clarifies that the single evaluation of the outcomes (previously mentioned algorithm calibration) is not enough to determine the fairness of the algorithm or model. This idea was first explored in [3]. Consequently, Access Now Organization [34] and The Public Voice Coalition [35] propose the evaluation of pre-existing conditions in the data that can be further amplified by the AI system before its design is even considered. This report shows an inclination towards the emerging trend of recognizing in the data an origin for discriminatory and biased decisions, in contrast with the rooted trend of solely holding the algorithms accountable for the negative outcomes produced by AIS. Additionally, the House of Lords Select Committee on Artificial Intelligence [36] and [PERSON] et al. [37] criticize the methods of learning developed in machine learning, specifically how data is used during training. Per the House of Lords Select Committee on Artificial Intelligence [36], while learning, systems are designed to spot patterns, and if the training data is unrepresentative, then the resulting identified patterns will reflect those same patterns of prejudice and, consequently, they will produce unrepresentative or discriminatory decisions/predictions as well. [PERSON] et al. [37] highlight that good-quality data is essential for the widespread implementation of AI technologies; how ever, the study argues that if the data is nonrepresentative, poorly structured, or incomplete, then there exists the potential for the AI to make the wrong decisions. Both reports define bias over the basis of misleading decisions produced from such compromised datasets. Acknowledging the role of data in the introduction of bias is a relatively new approach (This is different from the Garbage In Garbage Out (GIGO) approach to explain the relation of trashy data input with faulty outputs. The GIGO approach links specific data issues such as duplicity of information, absence of information, and noise in information, just to provide a few examples, and bad programming with faulty output from systems. The relatively new approach of pointing out the datasets as an origin for discriminatory decisions refers to those datasets that, even when not being trashy, are biased and triggers discriminatory patterns in ADM systems. It is a new approach as the origin of discriminatory ADM systems' outcomes were mainly linked to biased algorithms, ignoring that datasets and the development team had a role in introducing bias into the system.). [PERSON]'s [PERSON] (2018) comprehensive survey provides several definitions of types of biases originating in the data. The author enriches upon the already mentioned historical and representation biases by providing further classifications. From the definitions provided by [PERSON] (2018), we thought it pertinent to highlight the following due to the focus not on the data distribution per se but on the introduced bias resulting from a misuse of the dataset. First, we wanted to note measurement bias, which takes place when using a particular feature of the object of the decision when building judgment, just because that feature has been historically over-measured. This particular action has a fuzzy line with human-introduced bias, as is explained later in the classifications provided by IBM.