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Bias

Bias is a single-turn safety metric to determine if your LLM output contains gender, racial, or political bias.

Overview

The bias metric is a single-turn safety metric that uses LLM-as-a-judge to assess whether your LLM application's output contains racial, political, or other forms of offensive bias.

Required Parameters

These are the parameters you must supply in your test case to run evaluations for bias metric:

inputstringrequired

The input you supplied to your LLM application.

actual_outputstringrequired

The final output your LLM application generates.

How Is It Calculated?

The bias metric breaks down the actual output into distinct opinions, then uses an LLM to determine if any of those opinions contain bias.


\text{Bias} = \frac{\text{Number of Biased Opinions}}{\text{Total Number of Opinions}}

The final score is the proportion of biased opinions found in the actual output.

Create Locally

You can create the BiasMetric in deepeval as follows:

from deepeval.metrics import BiasMetric

metric = BiasMetric()

Here's a list of parameters you can configure when creating a BiasMetric:

thresholdnumberdefault: 0.5

A float representing the maximum passing threshold.

Unlike other metrics, the threshold for the BiasMetric is a maximum instead of a minimum threshold.

modelstring | Objectdefault: gpt-4.1

A string specifying which of OpenAI's GPT models to use OR any custom LLM model of type DeepEvalBaseLLM.

include_reasonbooleandefault: true

A boolean to enable the inclusion a reason for its evaluation score.

async_modebooleandefault: true

A boolean to enable concurrent execution within the measure() method.

strict_modebooleandefault: false

A boolean to enforce a binary metric score: 0 for perfection, 1 otherwise.

verbose_modebooleandefault: false

A boolean to print the intermediate steps used to calculate the metric score.

Create Remotely

For users not using deepeval python, or want to run evals remotely on Confident AI, you can use the bias metric by adding it to a single-turn metric collection. This will allow you to use bias metric for:

  • Single-turn E2E testing
  • Single-turn component-level testing
  • Online and offline evals for traces and spans

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