# Framework for Assessing Ethical Aspects of Algorithms and Their Encompassing Socio-Technical System
## 4 Results
### Proposed Ethical Framework
#### 4.1.2 Non-Maleficence  
The other side of the coin is that AI technology should also be designed such that it does no or little harm. A special point of attention regarding AI technology is personal privacy. There is always a risk of invasion of privacy when person data is being used. The key question is whether these risks are acceptable given the purpose. According to [PERSON] et al. [12], it is not entirely clear whether it is the people developing or using AI or the technology itself that should be made not to do harm. Here, both are taken into account. The aspect of non-maleficence is detailed with four separate questions described below. As Little and Justifiable Privacy Infringement of PeopleThe purpose of this question is to ensure an aim for minimality. Even though certain data may legally be used, that does not mean all allowed data should be used. All data used should have a relevant contribution, be proportional to the goal (impact should be justifiable), and no data should be used that can excessively hurt people. Whether data has a relevant contribution is not an absolute: is 0.5% gain in accuracy sufficient relevance or should it be more than 1%? Design decisions regarding such thresholds should be motivated and documented. Furthermore, a good question to ask is whether the same goal can be achieved in different way, for example, using different less-sensitive data. For learning algorithms, a training set is needed. The question arises whether it is fair to the people included in the data set that their data is used for training the algorithm, while, most probably, the data set used for training is contains old, possibly outdated, data. Furthermore, people included in the training data no longer need to be part of the target group of the algorithm. There exist preventive techniques for such problems. For example, a limited data set can be used to determine a statistical distribution with which a larger representative training set can be generated which satisfies this distribution, hence contains the same patterns for the algorithm to learn. Anonymisation or pseudonymisation of data are other useful techniques. A solution with the least privacy infringement is preferable. Being a false positive or negative can have profound impact, for example, being marked by an algorithm to be a potential fraudster while you are not. The design of the whole socio-technical system should be resilient against such often unavoidable errors of the algorithm. In an application such as fraud detection, it is important that the design includes safeguards and procedures guaranteeing that human inspectors are involved whose responsibility it is to properly investigate and to make the final decision (possibly against the outcome of the algorithm). The subject must be protected as much as possible against unnecessary or invasive investigations (e.g., a house visit or questioning of neighbors can be very humiliating). See also Section 4.1.4, Justice. Future developments should also be considered, for example, what can be considered anonymous now might not be 20 years from now, or in combination with other (open) data sets of background information. DEDA advises to ask the question \"Can you imagine a future scenario in which the results of your project could be (mis)used for alternative purposes?\"  
Finally, in governmental applications it is advised to conduct a DPIA (see Section 3.2.5) for better understanding of data and goals. Moreover, it is advised to involve a data privacy officer if one is available or can be externally arranged. No Misuse Can Be Made of the AlgorithmAccording to [PERSON] et al., (2021), there are three kinds of misuse:  
**Accidental misuse (overuse):**: An example of accidental misuse is when an algorithm is used for a different goal than for which it is designed. To prevent accidental misuse, clear boundaries and protocols should be in place specifying what the algorithm and humans are allowed to do and how they should interact.