“Deployment is the process of getting an IT system to be operational in its environment, including installation, configuration, running, testing, and making necessary changes. Deployment is usually not done by the developers of a system but by the IT team of the customer. Nevertheless, even if this is the case, developers will have a responsibility to supply the customer with sufficient information for successful deployment of the model. This will normally include a (generic) deployment plan, with necessary steps for successful deployment and how to perform them, and a (generic) monitoring and maintenance plan for maintenance of the system, and for monitoring the deployment and correct usage of data mining results.”[1]
These are the main actions that need to be addressed in this stage:
References
1SHERPA project (2020) Guidelines for the ethical development of AI and big data systems: an ethics by design approach. SHERPA, p.13. Available at: www.project-sherpa.eu/wp-content/uploads/2019/12/development-final.pdf (accessed 15 May 2020). ↑
| Checklist: deployment
☐ The controllers have deleted all unnecessary personal data or, on the contrary, justified the impossibility of doing so. ☐ The controllers have informed data subjects about additional processing at this stage. ☐ The controllers have determined the adequate legal basis for carrying out the communication of personal data to third parties, especially if special categories of data are involved. ☐ The controllers have considered conducting a DPIA ☐ The controllers have made sure that the algorithm does not include personal data in a hidden way (or taken necessary measures if this is unavoidable). ☐ The AI developers have implemented tools aimed at communicating the results of the validation and monitoring system employed and offered their collaboration to continue ☐ The AI developers have compromised to offering real time information to the end user about the values of accuracy and/or quality of the inferred information at each stage. |