AI in Disclosure and eDiscovery: Practical Uses and Red Lines

Disclosure and eDiscovery are natural homes for AI: lots of documents, repetitive tasks and huge scope for pattern recognition. They are also areas where procedural rules and duties to the court bite hard.

This article looks at practical ways UK firms can use AI in disclosure and eDiscovery while respecting:

the duty to preserve and disclose relevant documents;
proportionality and cooperation obligations; and
the need to explain your process if challenged.

Where AI already helps in disclosure

The tools themselves vary, but common AI-enabled features include:

Email threading and deduplication – grouping conversations and removing duplicates.
Concept clustering – grouping documents by subject matter rather than simple keyword hits.
Technology-assisted review (TAR) – models that learn from reviewers’ coding decisions to prioritise likely relevant documents.
Entity extraction – surfacing names, organisations, dates and locations.

None of these change the fundamental duties under the CPR, Practice Direction 57AD or equivalent regimes. They just change how you meet them.

Designing an AI-assisted review that you can defend

When courts or opponents criticise disclosure, the problem is rarely “you used AI”. It is usually that:

the scope of search was unclear or unreasonable;
human review was too thin; or
no-one can now explain what was done.

A defensible AI-assisted review process should:

Start with a clear protocol – custodians, date ranges, data sources, search terms, and TAR settings if used.
Involve experienced lawyers in training and supervising models.
Maintain validation sets and sampling to test performance.
Produce documentation you can show the court if necessary.

Example: using TAR sensibly

A simple TAR workflow may include:

an initial seed set of documents coded by experienced reviewers;
iterative training rounds where the system reprioritises the collection;
periodic random samples to check precision and recall;
human review of high-risk categories (privilege, confidentiality, reputational issues).

Your aim is not perfection but reasonable, proportionate efforts documented in a way that withstands scrutiny.

Privilege and confidentiality in AI-powered review

AI-assisted platforms are typically run by specialised vendors. Treat them much like any other disclosure provider:

ensure robust confidentiality clauses and DPAs;
check where data will be hosted and processed;
understand whether the provider uses your data to train general models.

Pay particular attention to:

how privileged documents are flagged and segregated;
who can access them within the provider’s team; and
how accidental disclosure is detected and remedied.

Clear protocols and training for your reviewers remain essential – AI does not replace the judgment needed to identify privilege and sensitivity.

Cooperation and transparency with opponents

Practice Direction 57AD emphasises cooperation in disclosure. When using AI tools, that often means:

being ready to explain your chosen approach in general terms;
sharing protocol documents that describe, at a high level, how TAR or clustering has been used; and
engaging constructively with sensible suggestions from the other side.

You do not have to divulge every technical detail, but being defensive or opaque about technology tends to increase, not reduce, conflict.

Common red lines

Even with good tools, there are some lines most firms will not want to cross:

leaving bulk coding of relevance or privilege entirely to an un-reviewed model;
relying on opaque “relevance scores” without checking samples of documents that scored low;
failing to preserve raw data or audit logs that would allow you to reconstruct what was done.

A good internal rule of thumb is:

“If we could not explain our process to a judge in a short case management conference, we need to simplify it.”

How OrdoLux sits alongside specialist platforms

For large matters, you are likely to use dedicated eDiscovery platforms. OrdoLux is not trying to replace those. Instead, the aim is to:

keep instructions, protocols and key decisions in the matter file;
record which external tools were used and for what; and
capture high-level outcomes (for example, volumes reviewed, numbers of documents disclosed) in a way that links to time recording and reporting.

For smaller matters where firms do not want full eDiscovery stacks, in-time OrdoLux workflows may help:

triage collections of documents;
generate simple chronologies; and
assist in identifying obvious hot documents, always under human supervision.

This article is general information for practitioners — not legal advice.

Looking for legal case management software?

OrdoLux is legal case management software for UK solicitors, designed to make matter management, documents, time recording and AI assistance feel like one joined-up system. Learn more on the OrdoLux website.