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Industry Landscape
The human data crowdsourcing industry is experiencing significant growth, driven by the escalating demand for high-quality, diverse human feedback, particularly for AI model training and evaluation. While traditional market research remains a core component, the surge in AI development, especially in areas like RLHF and bias testing, positions this industry at the forefront of technological advancement. Companies like Prolific are pivotal in connecting researchers and AI developers with a global pool of verified participants, emphasizing quality and speed in data collection.
Total Assets Under Management (AUM)
Global Crowdsourcing Market Size in United States
~The global crowdsourcing market size was valued at USD 131.6 billion in 2022.
(23.4% CAGR)
• Driven by increasing demand for AI training data.
• Rise of digital platforms and remote work.
• Growing need for diverse and specialized human input.
Total Addressable Market
596.5 billion USD
Market Growth Stage
Pace of Market Growth
Emerging Technologies
Reinforcement Learning from Human Feedback (RLHF)
RLHF is a technique that uses human preferences to fine-tune AI models, particularly large language models, improving their alignment with human values and intentions.
Synthetic Data Generation
Synthetic data generation involves creating artificial datasets that mimic real-world data's statistical properties, reducing reliance on actual human data for some AI training and testing.
Federated Learning
Federated learning enables AI models to be trained on decentralized datasets at the edge, allowing data privacy to be maintained by not centralizing raw data.
Explainable AI (XAI)
XAI focuses on making AI model decisions more transparent and understandable to humans, increasing trust and facilitating human oversight.
Impactful Policy Frameworks
AI Bill of Rights (2022)
The White House's 'Blueprint for an AI Bill of Rights' (2022) outlines five principles for the design, use, and deployment of automated systems, emphasizing safe and effective systems, algorithmic discrimination protection, data privacy, notice and explanation, and human alternatives, consideration, and fallback.
This non-binding blueprint will increase the demand for human data to test for bias and ensure ethical AI development.
EU AI Act (2024)
The EU AI Act (expected to be fully implemented by 2026), is a comprehensive legal framework for artificial intelligence, classifying AI systems by risk level and imposing strict requirements on high-risk AI, including data governance, transparency, human oversight, and conformity assessments.
This regulation will significantly increase the need for human validation and oversight data to ensure AI models comply with strict safety and ethical standards.
California Privacy Rights Act (CPRA, 2023)
The CPRA (effective January 1, 2023) amends and expands the California Consumer Privacy Act (CCPA), granting consumers more control over their personal information and establishing the California Privacy Protection Agency (CPPA) to enforce privacy laws.
This law necessitates more stringent data handling and consent mechanisms for collecting human data, impacting participant recruitment and data storage practices.
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