# Framework for Assessing Ethical Aspects of Algorithms and Their Encompassing Socio-Technical System
## 3 Method
### Related Research on AI Issues  
The ethical frameworks refer to several different ethical issues such as bias and privacy. Although it is beyond the scope of this paper to provide an overview of scientific literature on these ethical issues, we provide a brief overview here of surveys and other important papers on these ethical issues.  
One prominent issue is _privacy_. Interesting surveys are [20; 21] which emphasize that privacy is not only an issue for learning algorithms, but that learning algorithms may also be used to help protect privacy as well. The different kinds and purposes of AI ask for their own specific perspectives on privacy protection [22]. An important warning is issued by [23] that any attribute can be identifying in combination with others, hence that a separation between personally identifiable (quasi-identifiers) and non-identifying attributes is meaningless. They propose that differential privacy is a promising solution direction [24], where data are more aggregated. Another interesting solution direction is federated learning: instead of sending the data to the model, the model is distributed and sent to the data thus avoiding certain privacy risks [25; 26].  
The issues of benefence and non-maleficence are not specific to algorithms. It has been argued early on that in the design of computer technology we should pay attention to human values while designing systems as understood from an ethical standpoint [27; 28]. An interesting survey is [29] which discusses the relationship between AI and human rights, sustainability and benefence in general. A commonly overlooked issue with advanced machine learning methods is their energy consumption: model training and development likely make up a substantial portion of the greenhouse gas emissions because of excessively large models used, for example, in natural language processing [30]. An important solution direction is to use special hardware designed for energy-efficient learning [31; 32].  
A well-studied AI issue is algorithmic bias: Outcomes may be skewed by a range of factors and thus might be considered unfair to certain groups or individuals [33]. For example, learned algorithms are not perfect--they also do make mistakes just like humans--but they should not be more error-prone for one group versus another. Being trained on natural human language, learned algorithms in natural language processing exhibit similar biases and are equally prone to discrimination as humans are [34; 35]. Sources of bias can be found in the data, the annotation process, input representations, models, and the research design [36]. Removing such biases, i.e., obtaining truly fair algorithms, is still an open problem (see, for example, [37]). Note that AI can also be used to battle human bias and discrimination and other maliciousness humans cause, for example, in detecting discriminating posts and fake news.  
The AI issue that is gaining more traction in research is about transparency and explicability. It is well-known that this issue is one of the main obstacles for adoption of AI in high-stake domains such as healthcare [38]. Part of the research on transparency and explicability was sparked by the European GDPR legislation which states that subjects have a right to an explanation [39]. The field of Explainable AI (XAI) actively researches how to explain the outcomes of learned algorithms [40]. Most approaches, however, provide post hoc explanations, i.e., attempts at explaining a model or outcome after the model has been learned in an interpretive manner. An important new direction in this area are the intrinsically interpretive models that develop machine learning methods that by design are explainable [41; 42]. To be able to communicate well to users of algorithms, consumerlabels are envisioned to, for example, enable lay persons to assess when human intelligence should overrule artificial intelligence [43].