Data Creation and Collection for Artificial Intelligence via Crowdsourcing

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Examine the use of crowdsourcing for gathering data.
Explain how cognitive biases and other human factors influence data quality.
Describe the use of active learning in the creation of crowdsourced training data.
Demonstrate the design of crowdsourcing tasks with quality control mechanisms.
Discuss the evaluation of ML models with humans in the loop.

Free

Type Course
Location Online
Pacing Starts anytime / Self-paced
Length

For instructor paced courses this is the length of the course.

For self-paced courses this is the length of the course if you spend the amount of time per week as specified. You're free to go faster or slower as you see fit.

6 Weeks - Effort 4 - 5 Hours per week

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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. - Details

##### Course Syllabus

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.