AI Provenance Pitfalls

A critical guide on avoiding ambiguous provenance in AI usage, with references to notable legal cases and practical tips on selecting trustworthy AI vendors and understanding their data sources.
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The Provenance Predicament in AI

In the evolving landscape of legal technology, understanding the provenance of AI tools is crucial for attorneys. Ambiguous provenance, or the uncertain origin of data used in AI training, poses significant legal and ethical concerns. This article explores key cases and offers guidance on navigating these issues.

The Risk of Ambiguous Provenance

Ambiguous provenance refers to the unclear origins of data used to train AI models. Imagine using a legal precedent without knowing its source; similarly, using AI tools without clear data provenance can lead to unreliable or legally questionable outcomes.

Landmark Cases Highlighting Provenance Issues

IBM and Flickr: IBM's use of Creative Commons licensed photos from Flickr for AI training raised concerns about consent and data usage. For attorneys, this case underscores the importance of understanding how and where AI training data is sourced.

Authors Guild v. Google: This case, involving the fair use of digital content, highlights the complexities of using publicly available data in AI models. It serves as a cautionary tale about the legal nuances of data use.

New York Times v. Microsoft, OpenAI: This recent case involving copyright infringement allegations showcases the ongoing legal challenges surrounding the use of copyrighted material in AI development.

As a legal practitioner, ensuring the ethical and legal use of AI tools is paramount. This involves scrutinizing the AI vendor and understanding the data sources.

Know Your Vendor: Trust is key. Investigate the vendor's reputation, their data sourcing practices, and their compliance with legal standards.

Training Data Origins: Inquire specifically about the data used to train the model. Was it ethically sourced? Is it compliant with copyright and privacy laws?

Fine-Tuning Data: Often, AI models are fine-tuned on specific datasets. Understanding what these datasets are and their legal implications is crucial.

Current Data Access: Determine what data the AI model can currently access and use. This knowledge is critical in assessing the legal risks and the reliability of the AI's output.

The importance of provenance clarity

Navigating the complexities of AI provenance requires a balanced approach. By thoroughly understanding the source and nature of the data used in AI tools, lawyers can effectively leverage these technologies while mitigating legal and ethical risks. The key lies in informed decision-making and choosing the right technology partners. As AI continues to reshape the legal landscape, staying informed and vigilant about provenance is essential for modern legal practice

About the author
Rafael Green-Arnone

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Counsel Stack Learn is a comprehensive tech education center built to help attorneys maintain professional competence.

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