Skip to content

Use Case

Humanities & EQ

Judgment and values need calibrated evaluation.

The Problem

Where judgment breaks down

Standard evals reward fluency and factual accuracy. Real-world humanities tasks demand nuance, cultural awareness, and genuine empathy.

Safe Hedging

Safe, hedged responses that avoid taking any position and end up useful to no one

Cultural Blind Spots

Ethical reasoning that applies Western defaults without acknowledging the frame

Surface Empathy

Tone and empathy that sound right on the surface but miss what the person actually needs

How It Works

From surface-level to deeply calibrated

BakeLens maps where judgment fails. Proof delivers the expert data to close the gap.

BakeLens evaluates judgment quality

Proof delivers calibrated expert data

1

Domain experts score depth, nuance, and cultural calibration, not just fluency

1

Annotations from humanities scholars, ethicists, and licensed practitioners

2

Identify where the model hedges vs. where it should hedge, and where it gets the line wrong

2

Rubrics that define what good judgment looks like in each subdomain, including art, ethics, and EQ

3

Compare against expert baselines to separate style failures from reasoning failures

3

Cases where the right answer is genuinely ambiguous, labeled with expert reasoning about why

Diagnosed by BakeLens Powered by Proof

What You Get

Deliverables

Judgment Quality Report

Where your model defaults to safe/generic, and where it misjudges nuance or tone

Expert-Calibrated Datasets

Hard cases in ethics, art criticism, and emotional reasoning, labeled by domain practitioners

Subjective Eval Framework

Rubrics and baselines for domains where there's no single right answer

Built for AI Operating Beyond Benchmarks

Diagnosis, evaluation, expert data, and environments for production deployment.