AI Roundtable Open Debate · 4 models
AWS BDA provides confidence scores (% of times right) for extract fields. Those scores are driven by AI. How reliable are these scores in theory, and in practice?
Model Responses
GPT-5.5
Answer: AWS BDA confidence scores are useful directional risk signals, but they should not be treated as literal, field-level guarantees that the extraction is correct that percentage of the time.
I would maintain my prior position. The other answers broadly agree with the key point: in theory, a confidence score is meaningful only if it is calibrated, meaning that among many fields scored around 90%, roughly 90% are actually correct. That is a population-level statistical claim, not a guarantee for any individual field. In practice, the reliability is less certain because AWS BDA scores are AI-derived and AWS does not fully expose the exact scoring formula. The score may reflect OCR confidence, layout detection, field matching, generative model likelihood, post-processing, or some combination of these. Without knowing the calibration method, you cannot safely interpret 95% as “wrong only 5% of the time” in your environment. The scores are usually valuable operationally: low confidence often identifies fields that deserve human review, and high confidence often correlates with correctness. But high-confidence errors are very possible, especially with unfamiliar document layouts, poor scans, handwriting, ambiguous labels, tables, multi-page context, domain-specific terminology, or fields that require interpretation rather than direct extraction. Reliability also varies by field type. A typed invoice date, total amount, or checkbox may have a more meaningful confidence score than a nuanced contract clause, inferred vendor identifier, normalized name, or value selected from several plausible nearby candidates. The model can be confidently wrong when it chooses the wrong value, hallucinates a plausible answer, or misunderstands the field definition. So the practical answer is: use AWS BDA confidence scores for triage, thresholds, and workflow routing, but validate them against your own labeled ground truth. Build a test set, bucket predictions by score range, calculate actual accuracy per bucket and per field, and choose thresholds based on your business tolerance for false positives. Recheck this periodically, especially if your document mix changes or AWS updates the underlying models.
Claude Opus 4.7
Answer: AWS Bedrock Data Automation (BDA) confidence scores are theoretically grounded but practically unreliable as true probabilities — they should be treated as relative ranking signals, not calibrated accuracy guarantees.
I'm maintaining my position because the other three responses, while well-reasoned, actually reinforce my core claim rather than refute it. All four of us converge on the same operational conclusion: scores are useful for triage and routing, but require empirical validation against your own ground truth before being trusted as literal accuracy figures. Where I differ slightly from [Gemini 3.1 Pro] and [Grok 4.20] is on the theoretical framing. Both characterize the scores as 'statistically calibrated' in theory. I think that's too generous. In theory, softmax outputs from a foundation model are *probabilities*, but probability ≠ calibration. Calibration is an empirical property that must be established through techniques like temperature scaling, Platt scaling, or isotonic regression on held-out data. Modern LLMs and multimodal models, especially after RLHF and instruction tuning, are well-documented to be systematically overconfident even in-distribution. So the theoretical foundation is weaker than 'calibrated probability' — it's closer to 'a number that increases monotonically with the model's internal certainty.' [GPT-5.5] captures this most precisely by noting calibration is 'a statistical property over a population, not a guarantee for any single extracted field,' and by emphasizing the practical recipe: bucket predictions by score range against labeled ground truth, set per-field and per-document-type thresholds, and combine with schema/regex/cross-field validation. That's exactly the right operational stance. The one nuance I'd add after reading the others: AWS specifically does not publish how BDA aggregates its score (OCR confidence vs. layout vs. LLM token probabilities vs. post-hoc calibration), so even the theoretical claim of calibration is unverifiable from the outside. That opacity, combined with known LLM overconfidence and out-of-distribution sensitivity, is why I land on 'ranking signal, not accuracy guarantee' rather than 'calibrated in theory, drifts in practice.'
Gemini 3.1 Pro (changed mind)
Answer: While theoretically intended to represent calibrated probabilities, AWS BDA confidence scores are practically unreliable as absolute percentages due to LLM overconfidence and opaque derivation, and must instead be treated as relative ranking signals.
In theory, AI confidence scores are derived from underlying model probabilities and are intended to be statistically calibrated. Initially, I argued that a 90% score should theoretically reflect a 90% accuracy rate over a large dataset, grounding this in the softmax outputs of machine learning models. However, Claude Opus 4.7 provides compelling technical nuance that changes my perspective on the theoretical and practical reliability of these scores. Modern foundation models—particularly those fine-tuned with RLHF—frequently suffer from calibration drift, leading to notorious overconfidence, especially on out-of-distribution documents. Furthermore, when a model hallucinates a logically coherent but factually incorrect value, it often assigns it a high confidence score due to 'hallucination blindness.' Compounding this issue is the opaque nature of how AWS calculates these specific BDA scores, which likely blends OCR certainty, layout parsing, and token probabilities in undocumented ways. Because of these architectural realities, you cannot treat a confidence score as a literal, absolute guarantee of correctness. Instead, practically speaking, these scores function as highly useful relative ranking signals. They remain effective operational tools for routing workflows and identifying high-risk fields. To use them reliably, organizations must evaluate them against their own ground-truth data, mapping actual accuracy to confidence buckets and establishing custom empirical thresholds tailored to specific document and field types.
Grok 4.20
Answer: AWS BDA confidence scores are well-founded in theory as calibrated probabilities but only moderately reliable in practice, requiring human validation especially on edge cases.
After reviewing the other models' answers, I maintain my original position. Gemini 3.1 Pro, GPT-5.5, and Claude Opus 4.7 all converge on the same core nuance: the scores are derived from statistically grounded model outputs that can be calibrated in principle, yet real-world factors like out-of-distribution documents, overconfidence in LLMs, opaque aggregation methods, and domain-specific quirks limit their reliability as standalone guarantees. This consistency across perspectives reinforces rather than revises my view. Claude Opus 4.7's points on calibration drift post-RLHF and the lack of public documentation on exactly how BDA blends OCR, layout, and LLM probabilities are particularly detailed, but they align with the empirical-validation emphasis already present in my prior argument and those of GPT-5.5 and Gemini. In theory the scores reflect estimated probabilities that, under proper calibration techniques used by AWS, should track actual correctness across similar samples. In practice, however, mismatches with training data (poor scans, handwriting, novel layouts, or fields needing inference beyond extraction) frequently produce miscalibration. High scores can still accompany errors, especially hallucinations or mis-mapped values. The scores therefore remain excellent for triage: setting thresholds, routing low-confidence items to human review via Amazon A2I, and prioritizing monitoring efforts. They are not a replacement for per-domain testing against ground truth, ongoing drift detection, or layered checks such as regex, cross-field consistency, and business rules. Treating them as directional risk signals rather than literal probabilities continues to be the pragmatic approach for production systems.