Companies increasingly implement automatic decision-making systems using predictions made by machine learning models. Decisions from automatic systems can seem impartial and correct, but they reflect problems in the data used to train the machine learning models and issues in the models themselves. Flawed decisions may harm consumers and have a negative business impact on the companies that left their decisions unchecked. Across the various use cases considered in the project, the impact of AI system issues can be very damaging, ranging from disrupted supply chains as seen from previous shortage of household supplies, bankrupt businesses and lost jobs to destroyed assets, or even injustice and harm brought to individuals. The lack of transparency in automated decisions has a negative impact on the level of trust in our society. The black box approach, delivering decisions without explaining the driving factors, erodes trust in the system and its predictions. Etiq recognizes that companies in sectors dealing with customer data, especially the financial sector, struggle with these risks. As such we're developing an ML/AI risk management platform providing error detection, root cause analysis and repair recommendations across pre-production and production stages. Our platform will boost that trust further by providing a list of driving factors with each model prediction as well as a confidence interval. It will allow organisations to quickly and easily run tests on machine learning models and data pipelines, then identify data and AI errors, unintended biases, areas of improvement. This way companies will be able to monitor model performance as well as diagnose the issue to help root problems early, repair issues and thus develop trusted machine learning models and data pipelines.