{ "fetched_at": "2026-10-19T19:15:08Z", "source_url": "https://www.kaggle.com/competitions/crowd-bias-challenge/data", "pages": [ { "name": "data-description", "content": "## Challenge Input\n\n324 face images with the file name ``. You can download the dataset below. Please note that the image size can vary.\n\n## Acknowledgement\n\nOriginal data from [ChaLearn LAP datasets](https://chalearnlap.cvc.uab.cat/) licensed under [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/)", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "Description", "content": "## CrowdBias Challenge\n\nParticipate in the CrowdBias challenge organised with [CSCW 2021 Workshop - Investigating and Mitigating Biases in Crowdsourced Data](https://sites.google.com/view/biases-in-crowdsourced-data). Register [here](https://forms.gle/m2esEZegkDYyQ8jH7) to take part in teams of up to 4 members.\n\nThe broad task goal is to collect correct, unbiased labels for an annotation task in which each item to annotate has a true answer, known to the organizers. Teams are free to pursue whatever research designs they see fit, such as interface design, annotation workflows, assignment methods, aggregation approaches, etc. Teams will be evaluated based on a combination of accuracy and fairness metrics. Successful teams will also be invited to present their approach during the workshop.\n\nParticipants may use any crowdsourcing platform of their choice to collect human annotations. \nFor any teams interested in using [Amazon’s Sagemaker Ground Truth](https://aws.amazon.com/sagemaker/groundtruth/) (GT) or [Augmented Artificial Intelligence](https://aws.amazon.com/augmented-ai/) (A2I) service to collect human annotations, Amazon will provide $250 in AWS credits to the first 15 teams who commit to participating in the challenge and to using GT as part of their participation. AWS credits cannot be used with Amazon Mechanical Turk. \n\nChallenge participants are also invited to attend our workshop at CSCW. Please note that you will have to [register](https://cscw.acm.org/2021/registration/) for both the CSCW conference and the workshop in order to attend (per the registration link above, one can see that prices are fairly inexpensive as a virtual conference, and further discounted with early registration through October 1, 2021). \n\n**Step 1** - Follow this link to register for the CrowdBias challenge and receive AWS credits\nhttps://forms.gle/m2esEZegkDYyQ8jH7\n\n**Step 2** - **Optional** - Register for CSCW Conference and Workshop (use the code “MIBI09”)\nhttps://cscw.acm.org/2021/registration/\n\nPlease contact workshop organizers for any questions.\nbcd21@easychair.org\n", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "Details", "content": "In this challenge, you need to collect apparent age labels for a given set of face images.\n\n## Challenge Input\n\n324 face images with the file name . You can download the dataset through `Data` section. Please note that the image size can vary.\n\n\n## Expected Output\n\nYou need to submit an abstract (maximum length of 200 words) describing your method and a CSV file that includes the final consolidated age labels. \n\nThe CSV file should contain two columns (`image_id` and `age`) and be submitted through Kaggle. The file should only have a single entry for each image. You are allowed to make multiple submissions.\n\n```\nimage_id,age\n7105,40\n```\nThe abstract and the final CSV file should be submitted through the [workshop submission website](https://easychair.org/conferences/?conf=bcd21) by **20th October 2021**. You are also encouraged to document the process. We will invite successful teams to present during the workshop. \n\n\n## Evaluation\n\nThe final ranking will be determined based on two metrics.\n1. MAE calculated using the real age and the submitted apparent age\n2. MAE difference between groups (determined based on the demographics and other attributes of people appearing in the images).\n\n> Please note that the Kaggle leaderboard is only based on metric 1 and the final ranking will be different to the Kaggle rankings.\n", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "rules", "content": "\n - Don't cheat!\n - Apply yourself!\n - Have fun!", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "How To Use AWS Sagemaker Ground Truth", "content": "## How to use AWS Sagemaker GroundTruth (GT)\n\nParticipants may use any crowdsourcing platform of their choice to collect human annotations. \nFor any teams interested in using free AWS credits with [Amazon’s Sagemaker Ground Truth](https://aws.amazon.com/sagemaker/groundtruth/) (GT), we provide some helpful information below for getting started.\n\nYou can create a task in GT using the AWS management console or the AWS SDK ([Create a Labeling Job (API) - Amazon SageMaker](https://docs.aws.amazon.com/sagemaker/latest/dg/sms-create-labeling-job-api.html)). Check the following document for a step by step guide on setting up the challenge task in AWS Sagemaker Ground Truth.\n\n[AWS Sagemaker Ground Truth - How to Guide](https://docs.google.com/document/d/10ic2TSZ8vwPc1u6u-2gU0gbG__vKeWfQn4Ut6SZTNLU/edit?usp=sharing)\n\n### Additional Resources\n- [A Complete Tutorial on Annotating Datasets with Amazon SageMaker Ground Truth](https://aws.amazon.com/getting-started/hands-on/build-training-datasets-amazon-sagemaker-ground-truth/)\n- [Creating Custom Labeling Workflows - Amazon SageMaker](https://docs.aws.amazon.com/sagemaker/latest/dg/sms-custom-templates.html)\n- [Github - Example task UIs for Amazon SageMaker Ground Truth](https://github.com/aws-samples/amazon-sagemaker-ground-truth-task-uis)\n\n\n", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false } ] }