aiR for Review results

When aiR for Review analyzes documents, it makes predictions about the relevance of documents to different topics or issues. If it predicts that a document is relevant or relates to an issue, it includes a written justification of that prediction, as well as a counterargument and in-text citations. You can view these predictions, citations, and justifications either from the Viewer, or as fields on document lists.

See these related pages:

How aiR for Review results work

When aiR for Review finishes its analysis of a document, it returns a prediction about how the document should be categorized, as well as its reasons for that prediction. This analysis has several parts:

Score—a numerical score that indicates how strongly relevant the document is or how well it matches the predicted issue.
Prediction—the relevance, key, or issue label that aiR predicts should apply to the document.
Rationale—an explanation of why aiR chose this score and prediction.
Considerations—a counterargument explaining why the prediction might possibly be wrong.
Citations—excerpts from the document that support the prediction and rationale.

In general, citations are left empty for non-relevant documents and documents that don't match an issue. However, aiR occasionally provides a citation for low-scoring documents if it helps to clarify why it was marked non-relevant. For example, if aiR is searching for changes of venue, it might cite an email that ends with "Hang on, gotta run, more later" as worth noting, even though it does not consider this a true change of venue request.

Predictions versus document coding

Even though aiR refers to the relevance, key, and issue fields during its analysis, it does not actually write to these fields. All of aiR's results are stored in aiR-specific fields such as the Prediction field. This makes it easier to compare aiR's predictions to human coding while refining the Prompt Criteria.

If you have refined a set of Prompt Criteria to the point that you are comfortable adopting those predictions, you can copy those predictions to the coding fields using mass-tagging or other methods.

For ideas on how to integrate aiR for Review results into a larger review workflow, see Using aiR for Review with Review Center.

Variability of results

Because of how large language models work, results can vary slightly from run to run. aiR's results for an individual document can potentially change even when given the same set of inputs. However, this is relatively rare; from our testing, it happens about 4% of the time.

Understanding document scores

aiR scores documents from 0 to 4 according to how relevant they are or how well they match an issue. The higher the number, the more relevant the document is predicted to be. In addition, aiR assigns a score of -1 to any errored documents. Because these were not properly analyzed, they cannot receive a normal score.

The aiR for Review scores are:

Score	Description
-1	The document either encountered an error or could not be analyzed. For more information, see How document errors are handled.
0	The document is “junk” data such as system files or sets of random characters.
1	The document is predicted not relevant. aiR did not find any evidence that it relates to the case or issue.
2	The document is predicted borderline relevant. aiR found some content that might relate to the case or issue. It usually has citations.
3	The document is predicted relevant to the issue. Citations show the relevant text.
4	The document is predicted very relevant to the issue. aiR found direct, strong evidence that the content relates to the case or issue. Citations show the relevant text.

Viewing results from the dashboard

Within an aiR for Review project, you can view results using the aiR for Review dashboard. This dashboard includes not just results fields, but calculated metrics such as the number of documents with predictions that conflict with human coding.

To view the dashboard, select a project from the aiR for Review Projects tab. For detailed information on the dashboard layout, see Navigating the aiR for Review dashboard.

Viewing results for individual documents

From the Viewer, you can see the aiR for Review results for each individual document. Predictions show up in the left-hand pane, and all citations are automatically highlighted.

To view a document's aiR for Review results, click on the aiR for Review Analysis icon ( aiR for Review Analysis icon ) to expand the pane. The aiR for Review Analysis pane displays the following:

Analysis Name
Prediction
Rationale and Considerations
Citation

For more information, see aiR for Review Analysis.

Notes:

If you run a new job on documents that were part of a previous job, you may temporarily see both sets of results linked to those documents. The old results will be unlinked after the new job is complete.
To avoid seeing doubled results, clear the previous result set using the aiR for Review Jobs tab.

Citations and highlighting

To jump to a specific citation, click the citation card. You can also toggle highlighting on or off by clicking the toggle at the top of the aiR for Review Analysis pane.

The highlight colors depend on the type of citation:

Relevance citation—orange.
Key Document citation—purple.
Issue citation—color set chosen in the Color Map application. For more information, see Color Map.

If the same passage is cited by two types of results, the highlight blends their colors.

Adding aiR for Review fields to layouts

Because of how aiR for Review results fields are structured, you cannot add them directly to layouts. If the highlighting is not enough, you can add an object list to the layout that shows all linked results. For more information, see Adding and editing an object list.

Creating document views and saved searches

In addition to using the aiR for Review dashboard, you can view and compare aiR for Review results for large groups of documents by adding their fields to document views and saved searches.

Each field name is formatted as aiR <review type> Analysis::<fieldname>. For example, the Prediction field for a Relevance analysis is called aiR Relevance Analysis::Prediction.

For a full field list, see aiR for Review results fields.

Notes:

If you run a new job on documents that were part of a previous job, you may temporarily see both sets of results linked to those documents. The old results will be unlinked after the new job is complete.
To avoid seeing doubled results, clear the previous result set using the aiR for Review Jobs tab.

Creating an aiR for Review results view

When creating a view for aiR for Review results, we recommend including these fields:

Edit
Control Number
<Review Field>
aiR <Review Type> Analysis::Score
aiR <Review Type> Analysis::Prediction

Because the Rationale, Citation, and Considerations fields have larger blocks of text, those tend to be less helpful for comparing many documents. However, you can also add those if desired.

For a full field list, see aiR for Review results fields.

Filtering and sorting aiR for Review results

Documents have a one-to-many relationship with the aiR for Review's results fields. For example, a single document might be linked to several Issue results. This creates some limitations when sorting and filtering results:

Filter one column at a time in the Document list. Combining filters may include more results than you expect.
If you need to filter by more than one field at a time, we recommend using search conditions instead.
You can add these fields to views and widgets, but you cannot sort the view or the widget by these fields.

aiR for Review results fields

The results of every aiR for Review analysis are stored as part of an analysis object. Each of the three result types has its own object type to match:

aiR Relevance Analysis
aiR Key Analysis
aiR Issue Analysis

aiR also links the results to each of the documents that were analyzed. These linked fields, called reflected fields, update to link to the newest results every time the document is analyzed. However, aiR keeps a record of all previous job results, and you can link the documents to a different job at any time. For more information, see Managing jobs and document linking.

The reflected fields are the most useful for reviewing analysis results. These are formatted as aiR <review type> Analysis::<fieldname>. For example, the Prediction field for a Relevance analysis is called aiR Relevance Analysis::Prediction.

aiR Relevance Analysis fields

The fields for aiR Relevance Analysis are:

Field name	Field type	Description
Name	Fixed-length Text	The name of this specific result. This formatted as `<Document Artifact ID>_<Job ID>`.
Job ID	Fixed-length Text	The unique ID of the job this result came from.
Score	Whole Number	Numerical score indicating how strongly relevant the document is. For more information, see Understanding document scores.
Document	Multiple Object	The Control Number of the document this result is linked to. If the result is not currently linked to any documents, this field is blank.
Prediction	Fixed-length Text	aiR's prediction of whether this qualifies as a relevant document.
Rationale	Fixed-length Text	An explanation of why aiR chose this score and prediction.
Considerations	Fixed-length Text	A counterargument explaining why the prediction might possibly be wrong.
Citation 1	Fixed-length Text	Excerpt from the document that supports the prediction and rationale. This may be blank for some documents.
Citation 2	Fixed-length Text	Second excerpt from the document that supports the prediction and rationale. This may be blank for some documents.
Citation 3	Fixed-length Text	Third excerpt from the document that supports the prediction and rationale. This may be blank for some documents.
Citation 4	Fixed-length Text	Fourth excerpt from the document that supports the prediction and rationale. This may be blank for some documents.
Citation 5	Fixed-length Text	Fifth excerpt from the document that supports the prediction and rationale. This may be blank for some documents.
Error Details	Fixed-length Text	If the document encountered an error, the error message displays here. For an error list, see How document errors are handled.

aiR Issues Analysis fields

The fields for aiR Issues Analysis are:

Field name	Field type	Description
Name	Fixed-length Text	The name of this specific result. This formatted as `<Document ID>_<Job ID>`.
Job ID	Fixed-length Text	The unique ID of the job this result came from.
Choice Analyzed	Fixed-length Text	The name of the issue choice being analyzed for this result.
Choice Analyzed ID	Whole Number	The Artifact ID of the issue choice being analyzed for this result.
Document	Multiple Object	The Control Number of the document this result is linked to. If the result is not currently linked to any documents, this field is blank.
Score	Whole Number	Numerical score indicating how well the document matches an issue. For more information, see Understanding document scores.
Prediction	Fixed-length Text	aiR's predicted issue choice for this document.
Rationale	Fixed-length Text	An explanation of why aiR chose this score and prediction.
Considerations	Fixed-length Text	A counterargument explaining why the prediction might possibly be wrong.
Citation	Fixed-length Text	Excerpt from the document that supports the prediction and rationale. This may be blank for some documents.
Error Details	Fixed-length Text	If the document encountered an error, the error message displays here. For an error list, see How document errors are handled.

aiR Key Analysis fields

The fields for aiR Key Analysis are:

Field name	Field type	Description
Name	Fixed-length Text	The name of this specific result. This formatted as `<Document ID>_<Job ID>`.
Job ID	Fixed-length Text	The unique ID of the job this result came from.
Document	Multiple Object	The Control Number of the document this result is linked to. If the result is not currently linked to any documents, this field is blank.
Score	Whole Number	Numerical score indicating how strongly relevant the document is. For more information, see Understanding document scores.
Prediction	Fixed-length Text	aiR's prediction of whether this qualifies as a key document.
Rationale	Fixed-length Text	An explanation of why aiR chose this score and prediction.
Considerations	Fixed-length Text	A counterargument explaining why the prediction might possibly be wrong.
Citation 1	Fixed-length Text	Excerpt from the document that supports the prediction and rationale. This may be blank for some documents.
Citation 2	Fixed-length Text	Second excerpt from the document that supports the prediction and rationale. This may be blank for some documents.
Citation 3	Fixed-length Text	Third excerpt from the document that supports the prediction and rationale. This may be blank for some documents.
Citation 4	Fixed-length Text	Fourth excerpt from the document that supports the prediction and rationale. This may be blank for some documents.
Citation 5	Fixed-length Text	Fifth excerpt from the document that supports the prediction and rationale. This may be blank for some documents.
Error Details	Fixed-length Text	If the document encountered an error, the error message displays here. For an error list, see How document errors are handled.

Using aiR for Review with Review Center

One option for integrating aiR for Review into a larger review workflow is to combine it with Review Center. After analyzing the documents with aiR for Review, you can use aiR's predictions to prioritize which documents to include in a Review Center queue.

For example, you may want to review all documents that aiR for Review scored as borderline or above for relevance. To do that:

Set up a saved search for documents where aiR Relevance Analysis::Score is greater than 1. This returns all documents scored 2 or higher.
Create a Review Center queue using that saved search as the data source.

Because of how the aiR for Review fields are structured, you cannot sort by them. However, you can either sort by another field, or use a prioritized review queue to dynamically serve up documents that may be most relevant.

For more information, see Review Center.

How document errors are handled

If aiR encounters a problem when analyzing a document, it will not return results for that document. Instead, it scores the document as -1 and returns an error message in the Error Details column. Your organization is not charged for any errored documents.

The possible error messages are:

Error message	Description	Retry?
Failed to parse completion	The large language model (LLM) encountered an error.	Yes
Completion is not valid JSON	The large language model (LLM) encountered an error.	Yes
Hallucination detected in completion	The results for this document have a chance of including a hallucination. For more information, see Hallucinations and conflations.	Yes
Conflation detected in completion	The results for this document have a chance of including a conflation. For more information, see Hallucinations and conflations.	Yes
Document text is empty	The extracted text of the document was empty.	No
Document text is too short	There was not enough extracted text to analyze in the document.	No
Document text is too long	The document's extracted text was too long to analyze.	No
Model API error occurred	A communication error occurred between the large language model (LLM) and Relativity. This is usually a temporary problem.	Yes
Uncategorized error occurred	An unknown error occurred.	Yes

If the Retry? column says Yes, you may get better results trying to run that same document a second time. For errors that say No, you will always receive an error running that specific document.

If you retry a document and keep receiving the same error, the document may have permanent problems that aiR for Review cannot process.

Hallucinations and conflations

Two types of errors deserve special mention:

Hallucination detected—these occur when the aiR results citation cannot be found anywhere in the document text. This is usually caused by formatting issues. However, just in case the large language model (LLM) is citing sentences without a source, we mark it as a possible hallucination.
Conflation detected—these occur when the aiR results citation comes from something other than the document itself, but which is still part of the full prompt. For example, it might cite text that was part of the Prompt Criteria instead of the document's extracted text.

When aiR receives the analysis results from the LLM, it checks all citations against the prompt text. Any possible hallucinations or conflations are marked as errors, and they receive a score of -1 instead of whatever score they were originally assigned. If retrying documents with these errors does not succeed, we recommend manually reviewing them instead.

Actual hallucinations are extremely rare. However, highly structured documents such as Excel spreadsheets and PDF forms are more likely to confuse the hallucination detector and trigger these errors.