Advances in artificial intelligence and machine learning have led to technological revolutions across several domains that are rapidly changing our lives. AI systems at the forefront of such innovations have garnered a growing barrage of concerns: lack of robustness, fairness, and transparency as well as ethical and societal implications.

Such problems can be attributed to data problems to a large extent: for models to learn comprehensive, fine-grained, and unbiased patterns, they have to be trained on a large number of high-quality data instances with the right distribution that is representative of real application scenarios.

In this course, we introduce Crowdsourcing, an important method that can be used to gather data for building AI systems by leveraging human intelligence at scale for data creation, enrichment, and interpretation.

By the end of this course, you will be able to understand and apply crowdsourcing methods to elicit human input as a means to gather high-quality data for machine learning. You will be able to identify biases in datasets as a result of how they are gathered or created, and select from task design choices that can optimize data quality. You will be able to understand and apply human computation for data creation and to reduce worker effort. Furthermore, you will also see how crowdsourcing can be used for interpreting, evaluating, and debugging machine learning models and pipelines.

Let’s get started!