# Framework for Assessing Ethical Aspects of Algorithms and Their Encompassing Socio-Technical System
## 4 Results
### Proposed Ethical Framework
#### 4.1.4 Justice  
(The development of) algorithms should promote global justice. The foundation for this can be found in the laws and regulations. Note again that laws and regulations need not always be ethically good. In the detailed questions below, we highlight the main aspects for obtaining global justice when algorithms are involved. Algorithm Resects the Rights and Interests of All Parties Impacted  
It is important to realize that the rights, interests, and priorities of different parties may be different but should nevertheless all be respected. For example, a data scientist may be mainly interested in making the 'best' algorithm, a tax payer may be mainly interested in a fair social security system, while a benefactor of social security may be mainly interested in their privacy. All such interests and priorities should be properly weighed in the design of the whole socio-technical system. Algorithm Resects the Rights and Interests of All Parties Impacted: Algorithm Is Fair  
An important aspect here is fairness of the algorithm: the algorithm should have no bias and should not discriminate. This is laid down in the GDPR, but it may require substantial effort to achieve. Examples abound of (usually unintentional) bias of algorithms. For example, a well-known case is from Amazon who attempted to use an algorithm evaluating CVs to reduce manual effort in recruitment. By using historic training data of employees of Amazon, they unintentionally taught the algorithm that the established employment patterns are the desired ones, which is not what they intended. For example, women are under-represented in technical positions, but it was not their intention that it should stay this way, i.e., that the algorithm should discriminate against them. [PERSON] et al. [12] mention that an algorithm has the potential to eliminate past discrimination in the long run, if implemented properly. Methods and tools exist for checking for bias, for example PROBAST [49] which has been effectively used to show that of the 62 algorithms for diagnosing or prognosing COVID-19 passing other quality criteria, only six had a low risk of bias whereas the others either had high (45) or unclear (11) risk of bias [10]. It was discovered in the Amersfoort case that a similar risk was also present in their data. In the past, they used a project-based work practice based on selecting a theme and then within the project specifically investigate the subjects related to the theme. For example, a project checking up on people who did not request special social assistance for more than 3 years or people of whom (one of) the parents are unknown. An algorithm using historic data may detect these patterns and erroneously use them for new cases thereby discriminating against people who do not know how to ask for special social security assistance. Another example was given by one of the law enforcers in the Amersfoort case. It is a fact that people with a non-Dutch origin have a house abroad more often. Although having a house abroad may in general be an indicator for monetary gains of fraud or crime, using this indicate may risk discrimination against citizens with a foreign origin. So, not only ethnicity is not a desirable indicator, also proxies for ethnicity may not be desirable as well. Additionally, very subtle injustices may happen. To illustrate from a personal story of a reputable person, he investigated after having been denied a credit card several times and it turned out that due to an algorithmic decision, their address was blacklisted primarily based on overdue payments by someone living in their house. The rule has some logic to it: people living together on one address are typically families and if one has payment problems, they tend to all do, but it is surely not generally true, hence may unjustly deny some people a credit card. Preventive measures include identifying discriminating indicators or variables, including proxies for them, and exclude these from the (training) data.