Classification
Data Management and Curation
Overview
A corpus is a large, structured set of texts or data used to train, validate, or evaluate AI and machine learning models. Corpora can consist of various data types, including text, images, audio, or multimodal content. They are foundational for developing AI systems, as the quality, diversity, and representativeness of the corpus directly influence model performance and bias. Some well-known corpora include Common Crawl, Wikipedia, and ImageNet. While corpora enable rapid model development, they also introduce challenges: data may contain biases, inaccuracies, or inappropriate content, and assembling a high-quality, representative corpus can be resource-intensive. Additionally, the use of copyrighted or sensitive data raises legal and ethical concerns. The selection and curation of a corpus require careful consideration to ensure the resulting AI system is robust, fair, and compliant with relevant regulations.
Governance Context
Governance frameworks such as the EU AI Act and ISO/IEC 23894 require organizations to document and assess the provenance, quality, and representativeness of corpora used for AI model development. For example, the EU AI Act mandates risk assessment and transparency regarding training data, including documenting sources, preprocessing steps, and potential biases. The NIST AI Risk Management Framework (AI RMF) further obligates organizations to establish controls for data quality, privacy, and ethical use, such as regular audits and bias mitigation procedures. Both frameworks require organizations to implement access controls, data minimization, and mechanisms for data subject rights, ensuring corpora do not inadvertently introduce legal, ethical, or societal risks. Concrete obligations include: (1) maintaining detailed documentation of corpus sources and preprocessing activities, and (2) conducting regular bias and quality audits to detect and mitigate risks.
Ethical & Societal Implications
The construction and use of corpora in AI systems raise significant ethical and societal concerns. Poorly curated corpora can perpetuate or amplify biases, leading to unfair or discriminatory outcomes. Inadequate privacy safeguards may result in unauthorized disclosure of personal or sensitive information. The use of copyrighted or proprietary content without proper rights can lead to intellectual property violations. Furthermore, the lack of transparency about corpus composition can undermine public trust and accountability. Addressing these issues requires careful curation, consent management, and ongoing monitoring to ensure ethical and lawful use of data.
Key Takeaways
A corpus is a foundational dataset for AI model training and evaluation.; Quality, diversity, and representativeness of corpora are critical for trustworthy AI.; Governance frameworks mandate documentation, risk assessment, and bias mitigation for corpora.; Improperly managed corpora can introduce bias, privacy, and legal risks.; Ongoing monitoring and transparency are essential for ethical corpus use.; Concrete controls like documentation and regular audits are required by major frameworks.