A software solution for trustworthy AI

越来越多的公司使用机器学习模型做出的预测来实施自动决策系统。来自自动化系统的决定似乎是公正的，但它们反映了用于训练机器学习模型的数据以及设计模型的工程师所做的选择中的问题。黑匣子方法在不解释驱动因素的情况下做出决策，侵蚀了人们对系统及其预测的信任。Etiq认识到，处理客户数据的部门，特别是金融部门的公司，正在与这些风险作斗争。因此，我们正在开发一个ML/AI风险管理平台，提供预生产和生产阶段的错误检测、根本原因分析和修复建议。我们的平台将通过提供每个模型预测的驱动因素列表以及可信区间来进一步增强这种信任。它将允许组织快速、轻松地对机器学习模型和数据管道进行测试，然后识别数据和人工智能错误、意外偏差和需要改进的领域。通过这种方式，公司将能够监控模型性能并诊断问题，以帮助及早找出问题的根源，修复问题，从而开发可信的机器学习模型和数据管道。

Companies increasingly implement automatic decision-making systems using predictions made by machine learning models. Decisions from automatic systems can seem impartial, but they reflect problems in the data used to train the machine learning models and the choices made by engineers designing the models. Flawed decisions may harm already vulnerable consumers and, in turn, have a negative business impact on the companies that left their decisions unchecked.The lack of transparency in automatic 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.