The Lantern and the Lens: Detecting Western Evangelical Bias in Large Language Models

The Intersection of Inquiry and Faith

In my years of rigorous inquiry—balancing the empirical demands of science with the profound, often quiet revelations of scripture—I have come to view the pursuit of truth as a collaborative endeavor. Whether one approaches the biblical text as a skeptic, a seeker, or a faithful adherent, we are all engaged in an act of interpretation. Today, we find ourselves with a new tool in our study: the Large Language Model (LLM). Like a lantern held up to an ancient manuscript, these models can illuminate vast corridors of knowledge. Yet, we must remain mindful: a lantern does not create the light; it merely directs it. And sometimes, the glass of that lantern is tinted by the cultural lens of its makers.

When we ask an AI to explicate a passage of scripture or discuss theological concepts, we are not receiving an objective, divine oracle. We are interacting with a statistical reflection of the vast, digitized corpus of human writing. Because much of the training data for these models originates from Western, English-speaking contexts, they often mirror the specific interpretive traditions—and biases—of Western evangelicalism.

Identifying the Cultural Tint

To study scripture with integrity, one must first recognize the perspective from which an answer is being generated. If you find your AI assistant consistently gravitating toward specific interpretive frameworks, it may be exhibiting a Western evangelical bias. Here are a few markers to look for:

• The Individualistic Lens: Western evangelicalism often prioritizes the personal, internal experience of faith. If an AI consistently interprets communal, covenantal, or political biblical texts through a lens of private piety or individual salvation, it is reflecting a specific cultural priority rather than the full breadth of the text.
• Protestant Interpretive Assumptions: Many models default to a sola scriptura approach, often neglecting the rich, thousands-year-old traditions of Eastern Orthodoxy, Roman Catholicism, or the diverse perspectives of the Global South.
• Modern Systematic Categorization: The desire to systematize theology into neat, logical propositions is a hallmark of Western Enlightenment influence. Ancient Hebrew and Greek texts often thrive in ambiguity, paradox, and narrative complexity—elements that AI may attempt to ‘flatten’ into modern evangelical doctrine.

Strategies for Scholarly Engagement

How, then, do we utilize these tools without being led astray by their inherent biases? We must approach the AI not as a teacher, but as a research assistant—one that requires constant, humble oversight.

1. Diversify Your Prompts

If you ask a question and receive a response that feels narrow, challenge the model to expand its horizon. Try framing your prompts to invite historical and linguistic depth:

"Analyze the concept of 'salvation' in Romans 5, first from a Western evangelical perspective, then from the perspective of Second Temple Judaism, and finally through the lens of early Church Fathers."

2. Seek the Linguistic Roots

Bias often hides in the translation of key terms. When an AI offers a theological conclusion, ask it to return to the original Hebrew or Greek. By requesting the etymological roots and the semantic range of a word, you move from the ‘what’ of modern opinion to the ‘how’ of ancient usage.

3. Maintain the Humility of Mystery

The scientist knows that the map is not the territory. When an AI provides a definitive, confident answer to a complex theological mystery, treat it with caution. Scripture is a doorway into the divine; it is rarely a closed system of logic. If the AI’s answer lacks the reverence or the ‘holy hesitation’ that often accompanies deep scholarly inquiry, recognize that as a limitation of the machine, not a failure of the text.

The Lantern, Not the Light

We are living in an era of unprecedented access to information. Yet, information is not wisdom. As we navigate the digital age, let us remember that the most important interpretive work happens within the human heart—the place where reason meets faith, and where mystery is welcomed rather than solved. Use these models to explore history, to check linguistic data, and to summarize complex arguments. But never surrender the sacred task of meditation to an algorithm.

By remaining vigilant, curious, and intellectually serious, we can use these tools to deepen our study, honoring the text by refusing to let it be constrained by the biases of our own time. Let the AI be a lantern, but keep your eyes fixed on the light that transcends all data, all models, and all human limitation.