# Framework for Assessing Ethical Aspects of Algorithms and Their Encompassing Socio-Technical System
## 5 Validation
### The Algorithm of the Municipality of Amersfoort  
A summary of the completeness results on the comparison of the analysis of the media on the algorithm of Amersfoort with the application of the proposed framework by the municipality of Amersfoort can be found in Figure 5. More information on the topics can be found in Figure 6.  
Figure 5: Results of a comparison of the analysis of the media on Amersfoort with the application of the proposed framework by the municipality of Amersfoort on whether questions in the framework could be answered with a yes, maybe, no, or were not answerable. (**a**) Exact numbers. (**b**) Percentages. Figure 6: Comparison of specific ethical concerns between framework and the media. The application of the proposed framework was done by interviewing various stakeholders involved in the design and development of the algorithm. These included law enforcers, project management, team management, information provision advisor, data scientist and lawyers. As part of the interviews, an evaluation of 12 cases was done: 10 cases were selected by a preliminary prototype algorithm and two cases were selected randomly (hence most likely non-fraudulent). The interviewees did not know which were the randomly selected cases. We present the analysis again guided by the main aspects of Figure 2.  
**Beneficence:**: Analogous to SyRI, it is clear why the municipality of Amersfoort chooses for a fraud detection algorithm: to aid in the reduction of fraud within the social security assistance context. This, in turn, to increase trust in the government and the tax system as a whole. The mindset of the municipality of Amersfoort is that they feel that the earlier fraud is detected, the earlier people can be helped. This focus on helping rather than punishing is strongly addressing beneficence. They also aim to reduce the differences between the human law enforcers. **Non-maleficence:**: As with any learning algorithm, historic data is needed for training the algorithm, which is not necessarily a bad thing, but awareness of this fact should be created among the stakeholders involved. There are a few factors in the algorithm that could be considered sensitive information, such as gender and special notices, as identified by the team. It still has to be evaluated whether or not these criteria sufficiently add to the accuracy of the algorithm; if they do not, then they may be excluded from the data. As this is also a fraud detection algorithm, being considered a high risk case may have significant impact on someone. The difference with SyRI is in the protocol: when customers, as they are called, are identified by the algorithm, the first step is a desk investigation by a human law enforcer. Only if suspicion is raised by the law enforcer is the customer invited for a conversation. The impact hence is not necessarily larger than a system without an algorithm. This point also illustrates the importance of a socio-technical approach to design: the involvement of a human as explicitly designed as a protocol in the system effectively addresses an important ethical concern with the use of algorithms. A final concern under non-maleficence, is that the algorithm has not been tested on real-world cases yet, so some risk of an ill-behaving algorithm is there; but this falls under 'no innovation without risk'. **Autonomy:**: The intended protocol addresses the autonomy concerns: humans stay in control. The desk investigation by the law enforcer ensures that there is an opportunity for a knowledgeable user to go against the outcome of the algorithm, even before the customer is impacted. The subsequent conversation with the customer also provides an opportunity for the customer to defend themselves by providing logical explanations for suspicious data or even counter-evidence. **Justice:**: Practically every algorithm is imperfect: it will produce false positives and negatives.