A/78/310 • Traceability: The data sets and the processes that yield the [artificial intelligence] system’s decision, including those of data gathering and data labelling as well as the algorithms used, should be documented to the best possible standard to allow for traceability and an increase in transparency. This also applies to the decisions made by the [artificial intelligence] system. This enables identification of the reasons why an [artificial intelligence] -decision was erroneous which, in turn, could help prevent future mistakes. Traceability facilitates auditability as well as explainability. • Explainability: Explainability concerns the ability to explain both the technical processes of an [artificial intelligence] system and the related human decisions (e.g. application areas of a system). Technical explainability requires that the decisions made by an [artificial intelligence] system can be understood and traced by human beings. Moreover, trade-offs might have to be made between enhancing a system’s explainability (which may reduce its accuracy) or increasing its accuracy (at the cost of explainability). Whenever an [artificial intelligence] system has a significant impact on people’s lives, it should be possible to demand a suitable explanation of the [artificial intelligence] system’s decision-making process. Such explanation should be timely and adapted to the expertise of the stakeholder concerned (e.g. layperson, regulator or researcher). In addition, explanations of the degree to which an [artificial intelligence] system influences and shapes the organisational decision-making process, design choices of the system, and the rationale for deploying it, should be available (hence ensuring business model transparency). • Communication. [Artificial intelligence] systems should not represent themselves as humans to users; humans have the right to be informed that they are interacting with an [artificial intelligence] system. This entails that [artificial intelligence] systems must be identifiable as such. In addition, the option to decide against this interaction in favour of human interaction should be provided where needed to ensure compliance with fundamental rights. Beyond this, the [artificial intelligence] system’s capabilities and limitations should be communicated to [artificial intelligence] practitioners or end -users in a manner appropriate to the use case at hand. This could encompass communication of the [artificial intelligence] system’s level of accuracy, as well as its limitations. 30 33. The European Data Protection Board and the European Data Protection Supervisor have issued a joint opinion in which they stated that: Data subjects should always be informed when their data is used for [artificial intelligence] training and/or prediction, of the legal basis for such processing, general explanation of the logic (procedure) and scop e of the [artificial intelligence] system. In that regard, the individuals’ right of restriction of processing (article 18 GDPR and article 20 EUDPR as well as of deletion/erasure of data (article 16 GDPR and article 19 EUDPR should always be guaranteed in those cases. Furthermore, the controller should have the explicit obligation to inform the data subject of the applicable periods for objection, restriction, deletion of data, etc. The [artificial intelligence] system must be able to meet all data protection requirements through adequate technical __________________ 30 23-15851 Ibid. 11/20