{"id":53094,"date":"2021-11-03T14:47:36","date_gmt":"2021-11-03T13:47:36","guid":{"rendered":"https:\/\/www.microsoft.com\/en-gb\/industry\/blog\/?p=53094"},"modified":"2022-02-10T20:44:22","modified_gmt":"2022-02-10T19:44:22","slug":"responsible-ai-implementing-ethical-and-unbiased-algorithms","status":"publish","type":"post","link":"https:\/\/www.microsoft.com\/en-gb\/industry\/blog\/technetuk\/2021\/11\/03\/responsible-ai-implementing-ethical-and-unbiased-algorithms\/","title":{"rendered":"Responsible AI – Implementing Ethical and Unbiased Algorithms"},"content":{"rendered":"

\"An<\/p>\n

Until very recently, machine ethics has been a topic reserved for science fiction written before the twenty-first century. The machines don\u2019t rule our society yet, at least not in the way that science fiction would like us to believe it would happen. Driven by easy access and the low cost of data storage and computing capabilities, the use of machine-driven intelligence has increased significantly in the last two decades. Machine learning algorithms, or AI, now impacts our lives in innumerable ways \u2013 making decisions that can have material impact on the lives of the individuals affected by those decisions.<\/p>\n

These can range from deciding whether an applicant is eligible for a mortgage, if a patient\u2019s scan shows cancer, the route you should take for your commute and even influencing which jar of peanut butter you buy based on the results from a recommender engine. It’s therefore no surprise that the performance of machine learning models behind these applications can have a significant impact on the lives of the individuals that it makes predictions for.<\/p>\n

When machine learning gets utilised for solving any of these problems, any model being deployed needs to be trained on historical data \u2013 things such as past mortgage decisions and labelled scans for cancer. Based on the users covered, or rather not covered, in the training data and any historical biases present in the training data, the model can perform better for one user group as compared to the other. This introduces biases in the model predictions that can then lead to discriminatory decisions made by the application utilising such a model.<\/p>\n

The responsibility of building the model often lies with the data scientists in the team. However, the responsibility to make the model and the resulting software product fair and ethical should belong to all roles within the team. The process should really begin when we first describe the business problem we want to solve and not when the data scientists write the loss function.<\/p>\n

In our book, we propose that the responsible AI lifecycle is not different from the standard data science lifecycle. It’s composed of investigations and actions that we need to perform in order to identify biases and discrimination, such as:<\/p>\n