Imagine an innovative new AI system that promises to revolutionise healthcare with its ability to analyse patient data and identify life-saving treatment plans. But a shocking exposé reveals the AI was trained with datasets containing sensitive medical history without consent. Its predictions are skewed against marginalised groups. Public trust plummets. The fallout is potentially devastating for both individuals and organisations.

This cautionary tale illuminates the immense legal and ethical hazards surrounding the data used to train AI systems. As AI now inhabits various facets of life, what are the key considerations around training data and how can we steer towards responsible practices?

Fuel for the AI Engine

First, why is training data so crucial to AI in the first place? Modern AI relies heavily on machine learning, where algorithms "learn" by analysing large sets of sample data. The AI is only as good as its training diet. Flawed input perpetuates flawed intelligence.

Consider an AI designed to interpret CT scans to diagnose cancer. It must be fed thousands of labelled scans showing both malignant and benign tissues. This teaches the AI what visual markers indicate cancer. Training with incomplete data leaves dangerous blind spots in its detection capabilities.

That’s why curating regulatory compliant, unbiased and accurate training data is an immense undertaking, yet also a strategic opportunity. Those who get it right unlock huge competitive advantage. Fail, and risk legal violations or distorted AI. High stakes, but how exactly does responsible data practice translate?

Obtaining Data Legally

Let’s start with arguably the most fundamental criterion: ensuring training data is legally obtained and used. This hinges on a number of elements including:

Consent & Privacy: If training with personal data, explicit consent must be obtained from individuals. Details like datasets and use cases should be clearly communicated. Privacy must be tightly controlled.

Intellectual Property: Using copyrighted data like articles or images without permission can violate IP rights. Licensing agreements must allow the intended usage.

Data Protection Laws: Nations like the UK and EU have strict regulations around data collection, storage and usage that AI training processes must abide by. Non-compliance risks heavy penalties.

While daunting, legal teams can conduct in-depth assessments to identify obligations and implement compliant frameworks for obtaining, managing and licencing training data.

Some best practices to put in place include:

• Performing privacy impact assessments before data collection

• Anonymising personal information wherever possible

• Communicating transparently around AI training procedures

• Securing airtight data licensing agreements