 prevent some goals being achieved  Practitioners may not be able to navigate from the tool back to source documents  Skills and knowledge gap and the provision of consistent guidance in developing areas, this is linked to the pace of innovation in technologies, including how this interacts with information and data governance (e.g., cultural shifts about legal basis for data processing/sharing - consent vs public task) 43 OFFICIAL - SENSITIVE  There is a risk associated with data fragmentation, where data assets are split it can cause multiple problems (nothing that data is fragmented at source the work was looking to mitigate this). Examples include:  Data fragmentation linked to technical challenges around filetypes that cannot be processed by AI services (including .doc files which make up 25% of the overall documents)  Solution to overcome character limits has not been fully tested  Presenting information in a new way and the inclusion, exclusion or prioritisation of particular information which may not provide a fully accurate picture or may not be a fair representation  Updates from Microsoft could result in the need to reconfigure/re-build some or all of the components (e.g., the current solution may become unsupported) and/or a lack of awareness that updates applied by Microsoft might affect ethics or data governance impact assessments  Press coverage around use of Artificial Intelligence Ongoing risks – open, little or no ability to influence:  Known risks associated with artificial intelligence/machine learning tools/processes, such as: o They can produce inadvertent algorithmic biases and inequalities o Distribution/dataset shift can occur where a machine learning model is deployed on a data distribution that is different to what it was trained on N