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A one-stop shop to get started on the key considerations about data for AI! Learn how crowdsourcing offers a viable means to leverage human intelligence at scale for data creation, enrichment and interpretation, demonstrating a great potential to improve both the performance of AI systems and their trustworthiness and increase the adoption of AI in general.
Advances in Artificial Intelligence and Machine Learning have led to technological revolutions. Yet, AI systems at the forefront of such innovations have been the center of growing concerns. These involve reports of system failure when conditions are only slightly different from the training phase and they also trigger ethical and societal considerations that arise as a result of their use.
Machine learning models have been criticized for lacking robustness, fairness and transparency. Such model-related problems can generally be attributed to a large extent to issues with data. In order to learn comprehensive, fine-grained and unbiased patterns, models have to be trained on a large number of high-quality data instances with distribution that accurately represents real application scenarios. Creating such data is not only a long, laborious and expensive process, but sometimes even impossible when the data is extremely imbalanced, or the distribution constantly evolves over time.
This course will introduce an important method that can be used to gather data for training machine learning models and building AI systems. Crowdsourcing offers a viable means of leveraging human intelligence at scale for data creation, enrichment and interpretation with great potential to improve the performance of AI systems and increase the wider adoption of AI in general.
By the end of this course you will be able to understand and apply crowdsourcing methods to elicit human input as a means of gathering 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. These learnings will contribute to an important set of skills that are essential for career trajectories in the field of Data Science, Machine Learning, and the broader realms of Artificial Intelligence.
Some prior experience with a programming language (e.g. Python, Java) is recommended but not required.
At the end of this course you will be able to:
Week 1: Crowdsourcing for High-quality Data Collection and The ImageNet Story
Artificial Intelligence is at the center of many recent advancements across areas such as transportation and finance. One of the reasons for this is that in the past decade we have designed methods to harness human intelligence at scale.
We will introduce and discuss the crowdsourcing paradigm and the importance of high-quality data.
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Week 2: Quality Control Mechanisms for Crowdsourcing
The quality of crowdsourced human input is one of the most crucial aspects affecting the overall value of the paradigm. In this week we will discuss the challenges that make quality control difficult to guarantee.
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Week 3: Factors Affecting Quality in Crowdsourcing
Researchers and practitioners in human computation and crowdsourcing have identified several factors that affect the quality of crowdsourced data. In this week we will discuss some of the recent works in this regard.
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Week 4: Human Input for Data Creation and Model Evaluation in AI
In this week, we will cover the importance of data collection, annotation and engineering.
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Week 5: Reducing Worker Effort: Active Learning
In this week we explore the challenges of collecting large scale data and how to overcome them.
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Week 6: Interpreting, Evaluating, and Debugging ML models
In this week, we discuss strategies for evaluating, debugging, and interpreting machine learning models.
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Who can take this course?
Unfortunately, learners residing in one or more of the following countries or regions will not be able to register for this course: Iran, Cuba and the Crimea region of Ukraine. While edX has sought licenses from the U.S. Office of Foreign Assets Control (OFAC) to offer our courses to learners in these countries and regions, the licenses we have received are not broad enough to allow us to offer this course in all locations. edX truly regrets that U.S. sanctions prevent us from offering all of our courses to everyone, no matter where they live.
Who can take this course?
Unfortunately, learners residing in one or more of the following countries or regions will not be able to register for this course: Iran, Cuba and the Crimea region of Ukraine. While edX has sought licenses from the U.S. Office of Foreign Assets Control (OFAC) to offer our courses to learners in these countries and regions, the licenses we have received are not broad enough to allow us to offer this course in all locations. edX truly regrets that U.S. sanctions prevent us from offering all of our courses to everyone, no matter where they live.
