Should AI Provide Confidence Scores for Research Coding Decisions?

Should AI Tell You How Confident It Is About Each Research Code?

You ask generative AI to classify 2,000 open-ended survey responses using a research codebook. Alongside each assigned code, the model provides a confidence score: 98%, 85%, 62%, or another number.

The scores seem useful. You could review low-confidence assignments and accept high-confidence ones, potentially reducing the amount of manual checking required.

But what does a confidence score actually measure? If AI reports 95% confidence, does that mean its classification has a 95% chance of being correct? And can researchers use these numbers to decide which coding decisions deserve human attention?

Confidence Scores Can Help, but Only When Their Meaning Is Established

What AI Confidence Scores Mean During Research Data Coding

What Is an AI Coding Confidence Score?

A confidence score is a numerical or categorical indication associated with an AI-generated coding decision. Depending on the system, it may represent a model-generated judgment of certainty, a probability derived from model outputs, or a score produced by a separate uncertainty-estimation procedure.

Suppose a researcher uses three categories to classify students' comments about online learning: internet connectivity, platform usability, and instructor communication.

For the response "My internet disconnects whenever I attend a live lecture," the model might assign "internet connectivity" and report confidence of 0.97.

That number appears precise, but its interpretation depends on how it was obtained. If the researcher simply asked the model to provide a confidence percentage, the number is a generated response rather than an independently established probability of correctness.

Researchers should therefore ask how the score was produced before deciding what it means.

Are All AI Confidence Scores Calculated the Same Way?

No. Several approaches are used to estimate uncertainty in language-model outputs.

These methods should not be compared as though they measure identical quantities. A researcher using confidence scores must identify the specific method and its intended interpretation.

Does 90% AI Confidence Mean a 90% Chance of Correct Coding?

Not necessarily. A confidence score of 90% only has that probabilistic interpretation when the scoring procedure has been appropriately calibrated and evaluated for the intended task.

Calibration concerns the relationship between reported confidence and observed correctness. For example, if a system assigns approximately 90% confidence to many classifications, a well-calibrated system would be expected to classify approximately 90% of those cases correctly, under the conditions for which calibration holds.

Calibration is generally assessed across groups of predictions rather than established from one individual score.

Even a calibrated model can make incorrect high-confidence predictions. Calibration does not guarantee that any individual classification is correct.

How Can Researchers Check Whether Confidence Scores Are Calibrated?

For structured classification tasks, researchers can compare reported confidence with correctness against an independently reviewed reference dataset.

One straightforward approach is to group predictions into confidence ranges and calculate the proportion of correct classifications within each group.

These figures are hypothetical. In practice, the number of observations within each range, uncertainty in the estimates, category distribution, and representativeness of the evaluation data must also be considered.

If high-confidence assignments frequently turn out to be incorrect, the researcher should not use the scores to exempt those assignments from review.

What Is the Difference Between Confidence and Accuracy?

Confidence describes a system's estimated certainty about a decision. Accuracy describes how often classifications match an appropriate reference.

A model may be confident but inaccurate, uncertain but correct, or reasonably calibrated overall while performing poorly on a particular category.

For example, a model might confidently classify sarcastic complaints as positive experiences. Repeating the classification could produce the same answer each time, creating both high reported confidence and high apparent stability without valid interpretation.

Researchers should distinguish confidence from coding consistency and validity. None of these properties automatically establishes the others.

Can Confidence Scores Help Identify Ambiguous Responses?

Potentially. A model may assign lower confidence when a response contains insufficient information, overlapping categories, or several plausible interpretations.

Consider the statement "The university should have provided more support." If the codebook distinguishes technical support, administrative support, and instructor support, the response may not provide enough evidence to select one.

A low confidence score could help identify the case for review. However, the model might instead select one category with high confidence, particularly if its learned associations favor that interpretation.

Consequently, researchers should not assume that all ambiguous cases receive low scores. The effectiveness of AI ambiguity detection must be evaluated using independently reviewed examples.

Should Researchers Use Confidence Thresholds to Decide Which Responses to Review?

A confidence threshold is a rule that determines which predictions receive additional review. For example, a researcher might initially consider reviewing assignments with scores below 0.80.

However, 0.80 is not a universally appropriate threshold. Its usefulness depends on the scoring method, observed error rates, coding task, and consequences of incorrect assignments.

A defensible threshold should be selected using evaluation data and assessed against the review workload and the number of errors it fails to capture.

Researchers should also examine a separate sample of high-confidence assignments. Otherwise, the evaluation may focus exclusively on errors the model already suspects.

What Is Selective Classification?

Selective classification allows a system to make predictions for some cases while abstaining from others. In research coding, the model might assign a category when specified criteria are met and return "requires review" when they are not.

Two quantities are relevant: coverage, which describes the proportion of cases receiving an automated classification, and selective risk, which describes the error rate among those accepted classifications.

Lowering coverage may reduce error rates if the system successfully identifies difficult cases. However, this relationship must be demonstrated rather than assumed.

For example, a system that automatically codes 70% of responses with acceptable performance may be more useful than one that codes 95% while making consequential errors. The appropriate trade-off depends on the study and the resources available for review.

Selective classification is not equivalent to removing difficult responses from the research dataset. Cases requiring review should remain traceable and be resolved or reported according to the study's analytical design.

Can Repeated AI Coding Produce More Trustworthy Confidence Estimates?

Repeated classifications can provide information about output stability. If the same response receives different labels across runs, that variation may indicate sensitivity to model behavior or coding conditions.

However, repeated agreement is not a calibrated confidence estimate by itself. A model can repeatedly make the same incorrect inference.

Researchers should also consider whether repeated calls use identical instructions, model versions, and processing contexts. Without such controls, variation may reflect changes in the workflow rather than uncertainty about the response.

The implications of rerunning the same coding prompt should therefore be examined separately from confidence calibration.

What Does Research Say About LLM Confidence and Calibration?

Kadavath and colleagues (2022), in the research paper "Language Models (Mostly) Know What They Know," examined whether language models could estimate the probability that their answers were correct. Their results demonstrated that some forms of self-evaluation can provide useful information, although the findings were specific to the evaluated models and tasks.

Guo and colleagues (2017) showed that modern neural networks can be poorly calibrated, establishing an important distinction between predictive accuracy and confidence reliability.

More directly relevant to language models, Kuhn, Gal, and Farquhar (2023) examined semantic uncertainty by considering the meanings of generated responses rather than relying exclusively on token-level probabilities. Their work illustrates why uncertainty estimation for language generation requires attention to semantic equivalence.

These studies support investigating confidence measures rather than treating model-generated percentages as self-validating. They do not establish that any particular confidence method is sufficiently reliable for a specific qualitative coding task.

Are Confidence Scores Appropriate for Every Qualitative Methodology?

No. Numerical confidence is more straightforward to evaluate when coding involves explicit categories and a defensible reference classification.

In interpretive approaches such as reflexive thematic analysis, different researchers may develop different but analytically defensible interpretations. Treating one code as the uniquely correct answer may be inconsistent with the methodology.

A confidence score could still describe a model's behavior, but its meaning should not be confused with the validity of an interpretive reading.

Researchers should determine whether their analytical approach supports probability-based classification evaluation before adopting numerical confidence as a quality indicator.

Testing Whether AI Confidence Scores Can Guide Human Review

Step 1: Compare Confidence With Observed Correctness

The researcher groups the 500 responses according to reported confidence and compares the AI classifications with the reference decisions.

The model performs better among higher-scoring assignments. However, 20 of the 200 highest-confidence classifications are still incorrect.

Step 2: Evaluate a Proposed Review Threshold

Suppose the researcher considers reviewing only responses with confidence below 0.70.

This would direct attention to 100 responses, representing 20% of the dataset. Those responses contain 35 incorrect classifications.

Across the entire dataset, there are 85 incorrect classifications: 35 in the lowest-confidence group, 30 in the middle group, and 20 in the highest-confidence group.

The threshold identifies a group with a relatively high error rate, but it does not capture most of the errors.

Step 3: Investigate High-Confidence Errors

The researcher examines the 20 incorrect assignments in the highest-confidence group. Several involve sarcastic responses and indirect complaints.

This suggests that confidence alone may be insufficient to identify particular error types. The researcher may need additional review criteria targeting linguistic complexity or categories associated with consequential mistakes.

Step 4: Develop a More Defensible Review Procedure

Instead of relying exclusively on a single threshold, the researcher considers combining confidence-based prioritization with review of ambiguous cases, category-specific checks, and an independent sample of higher-confidence assignments.

The procedure is evaluated on separate data before being used operationally.

The practical lesson is that confidence scores may help locate errors, but their usefulness must be measured by the errors they actually identify, not by how precise the numbers appear.

Misconceptions About AI Coding Confidence Scores

When Should You Use Confidence Scores in AI-Assisted Coding?

Confidence scores are most useful when they support a specific decision, such as determining which structured classifications require additional review. Their value should be established through observed performance rather than assumed from their numerical appearance.

Researchers should also distinguish confidence-based prioritization from the broader question of how much AI-coded material requires manual checking. A review strategy should consider the reliability of its selection mechanism as well as the consequences of undetected errors.

If confidence scores cannot be validated adequately, researchers may be better served by explicit ambiguity flags, independent sampling, and systematic examination of known difficult categories.

Before Using AI Confidence Scores to Guide Coding Decisions

Frequently Asked Questions About AI Coding Confidence

Confidence Scores Are Useful Only When They Predict Something Meaningful

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