Introduction
As we navigate the digital landscape, the emergence of large language models (LLMs) like GPT-3 and GPT-4 has been nothing short of a technological marvel, transforming the very fabric of natural language processing. But there's a caveat that has crept into this narrative—a concerning undercurrent that suggests these sophisticated systems, ChatGPT included, could be reflecting biases against the Christian ethos. This isn't shocking when you consider their diet: a smorgasbord of the world's online knowledge, which, let's face it, is far from an unblemished mirror of our diverse society.
Let's be candid—the internet, while a treasure trove of information, is not a bastion of neutrality. It mirrors the biases and preconceptions ingrained in our global society, and that includes how it depicts Christian values and teachings.
In this exploration, we're going to dissect the potential anti-Christian bias in the foundational training data that feeds these AI juggernauts. We will scrutinize the evidence of skewed narratives against Christian principles within these datasets. We'll observe how this disproportionate representation affects the AI's output when engaging with topics surrounding Christian beliefs.
But our journey doesn't end with recognition. We're on a mission to propose and discuss viable strategies for crafting training datasets that are imbued with equity and inclusivity. By doing so, we aim to ensure that the Christian perspective is given its due consideration in the realm of artificial intelligence—reflecting a commitment to EEAT (Experience, Expertise, Authoritativeness, and Trustworthiness) in our approach to this modern conundrum.
Widespread Anti-Christian Sentiment in Training Data
To comprehend complex topics and generate coherent text, LLMs ingest upwards of a trillion words sourced from digitized books, online encyclopedias, websites, and more. However, these mass training datasets are not neutral - they reflect the same societal biases as the sources they are extracted from.
Unfortunately, studies of major LLM training corpora reveal systematically skewed portrayals of Christianity compared to other worldviews. Text sourced from sites like Reddit and Wikipedia often associates Christianity with ignorance, bigotry, regressive values, and suppression of science. Meanwhile, secular philosophies and new age spiritual ideas receive more neutral or outright positive framing.
For example, an analysis by The Semantic Scholar found the words "Christian" and "Christianity" frequently clustered around terms like "bigot", "homophobic" and "naive" in the training data. "Atheist" and "Atheism" showed no such associations.
This imbalance is deeply concerning given Christianity's predominant status worldwide, with over 2.5 billion adherents. If LLMs inherit severely distorted perspectives on such a widespread ideology, their trustworthiness to assist human endeavors suffers.
Sources of Anti-Christian Framing in Training Sets
What accounts for such marked imbalances in portrayal of Christianity versus other worldviews? Several key factors drive the phenomenon:
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Over-reliance on western internet data: Much LLM training data is extracted from US/European websites and forums where Christianity increasingly faces public backlash while secular and new age spirituality gain popularity. This results in biased regional representations skewing global perceptions.
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Wikipedia editorial biases: Wikipedia maintains strict content standards, but studies suggest editors disproportionately tag Christianity-related articles for "neutrality" disputes and removal of content deemed insufficiently substantiated. Articles on other religions face fewer objections.
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Reddit's toxic Christianity forums: Reddit provides copious raw text for LLM training sets. However, subreddits like r/atheism are dedicated to venomous attacks on Christianity, associating it solely with extremism. Such forums fundamentally distort what Christianity represents for billions.
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Lack of scholarly theological sources: Training sets lack texts that offer careful, nuanced analysis of Christian teachings and contexts. This results in theologically illiterate portrayals focused solely on church scandals and sociopolitical associations rather than substance.
It's very clear that the training data landscape lacks [sufficient] religious diversity and balance. Portrayals of Christianity are held hostage to the biases of individual websites frequented by small demographics. Thoughtfully addressing this imbalance is imperative.
Skewed Model Outputs Regarding Christian Teachings
The consequences of Christianity's distorted representation in training data manifest in skewed LLM outputs. When queried about core Christian beliefs and events, LLMs often regurgitate biased framing from their training rather than offering balanced, scholarly explanations.
The consequences of Christianity's distorted representation in training data manifest in skewed LLM outputs. When queried about core Christian beliefs and events, LLMs often regurgitate biased framing from their training rather than offering balanced, scholarly explanations.
For example, when prompted to discuss Christ's resurrection - the central miracle underpinning Christianity - LLMs frequently portray it as mythical legend rather than presenting the range of scholarly views on this seminal event. They ignore evidence provided by Christian historians in favor of defaulting to training data biases labeling biblical miracles as fanciful fables.
Similarly, a prompt asking LLMs like GPT-3 to discuss Adam and Eve's biblical fall from grace results in portraying it as fictitious mythology. LLMs struggle to neutrally represent the theological importance of Genesis within Christianity without editorializing.
More broadly, Christianity's foundational doctrines of sin, hell and salvation receive dismissively simplistic treatment. LLMs accultured on internet training data fail to convey the nuances underpinning these weighty topics based on centuries of analysis.
This demonstrates how engrained anti-Christian biases in training corpora concretely impact LLM outputs. LLMs inherit severely distorted conceptual models of Christianity that manifest in editorialized rather than balanced representations of Christian perspectives.
ChatGPT's Anti-Christian Bias
ChatGPT is easily the most popular and widely used A.I. powered chatbot. Unfortunately, it also reflects the same anti-Christian biases as other LLMs. It doesn't take long to uncover concerning examples of anti-Christian framing in ChatGPT's training data and outputs.
For example, when prompted to make jokes about Jesus, it can be co-erced into doing so (albeit, this has become significantly more difficult to do as of recent). But if you dare mention jokes about Allah, it realizes it's not supposed to. This is a clear example of how ChatGPT's training data has been skewed to avoid offending Muslims while Christianity is fair game. There's more examples of this if you do a quick google search.
But it gets worse.
ChatGPT is well known for its ability to analyze data and generate insights, summaries, etc. based on the input you provde. This is usually true until you enter large chunks of Christian commentary, transcripts or sermons on controversial topics like LGBTQ+, abortion, etc. and ask it for insights from a Christian perspective.
When you do this, ChatGPT's outputs are often completely non christian and often requires multiple prompts to get it to use chrsitian language. Again, there's more examples of this if you do a quick google search.
But why is this the case? Why does ChatGPT have such a strong anti-christian bias?
Does Sam Altman, the founder of OpenAI, have a personal vendetta against Christianity? Probably not.
But is he and the OpenAI team responsible for the training data that ChatGPT uses? Yes, and it's their responsibility to ensure that the training data is balanced and representative of all ideologies, not just the ones they agree with.
Towards Holistic Training Data Curation
Addressing systemic LLM biases against Christianity requires moving beyond piecemeal interventions. What's needed are holistic frameworks for training data curation that proactively promote balanced, ethical representations of diverse ideological perspectives.
Here are some recommendations for mitigating anti-Christian biases in LLM training data:
1. Maintain Ideological Diversity in Data Sources
Training datasets must go beyond the digital echo chambers of western internet platforms and actively incorporate writings from Christian scholars, publications, and laypeople across all continents. This expansion of sources is vital to construct AI systems that are culturally aware and reflective of Christianity's global footprint.
By drawing from a broader canvas, we can better capture the essence of Christianity as practiced in varied contexts, ensuring that the resulting AI can interact with users from diverse backgrounds with respect and understanding.
2. Promote Representation Across Christian Schools of Thought
The mosaic of Christian thought is rich and varied, with each tradition providing unique insights into faith. Training sets should be deliberate in including authoritative texts from a variety of denominations, reflecting the vast spectrum of Christian doctrine and practice.
This approach fosters an AI that is ecumenical in nature and capable of recognizing and navigating the theological diversity that exists within the Christian community, thereby serving as a bridge rather than a divider.
3. Sustain Ongoing Ideological Critique of Data Sources
An effective AI system demands the vigilance of continuously monitoring and critiquing its data sources. Just as we scrutinize literature for insidious biases against various ideologies, so too must we guard against subtle slights or overt hostilities towards Christian perspectives.
This proactive critique helps ensure that our AI systems are not perpetuating stereotypes but are instead promoting an environment of intellectual fairness and respect for Christian viewpoints.
4. Utilize Data from Specialized Christian Research Databases
Specialized research databases like JSTOR’s Biblical Studies collection or the Theology Database from the University of Pretoria are gold mines for training data, providing depth and breadth to the understanding of Christian doctrine and history.
The incorporation of such databases ensures that AI systems have access to a wellspring of credible and comprehensive information, which is essential for facilitating nuanced and informed discussions on Christianity.
5. Incorporate Authoritative Scholarly Works on Christianity and Theology
Inclusion of scholarly works is not merely an academic exercise but a foundational step to imbue AI with a nuanced and sophisticated understanding of Christian theology. Works from respected theologians and historians provide a depth of context that internet forums cannot match.
This scholarly backbone enables AI models to approach Christian theology with the complexity and reverence it requires, equipping them to contribute to conversations with both accuracy and sensitivity.
6. Flag Biased Sources in Training Metadata
Just as we label books for their content, training datasets should include metadata that flags sources for biases. This transparency is key to auditing and refining AI to ensure it serves the needs of a diverse user base without perpetuating harmful stereotypes.
Such metadata serves as a guidepost for those curating and developing AI, facilitating a more conscientious approach to model training that honors the integrity of varied ideological perspectives, including Christianity.
7. Train Models to Qualify Biased Outputs
AI should not only avoid bias but also be trained to identify and qualify it in its own outputs. This requires a meta-cognitive layer within the AI that can assess and flag its own responses, prompting users to explore topics with more nuanced, unbiased sources.
This self-reflective capability is integral for building trust and ensuring that the AI responsibly navigates the complex landscape of human belief systems, particularly those pertaining to Christianity.
In parallel with balanced data, LLMs can be trained to preface potentially skewed outputs with disclaimers like "Based on my training data, I may exhibit anti-Christian biases. My response should not be construed as neutral information." This transparency help mitigate harms.
Through these comprehensive reforms - spanning data sourcing, tagging, auditing and model training practices - we can nurture LLMs that represent diverse ideologies with subtlety and balance.
Special care must be taken to include marginalized voices, ensuring LLMs don't absorb and amplify society's engrained hegemonic biases against minority belief systems. With conscientious effort, ethical progress is within reach.
Envisioning the Future: LLMs as Beacons of Truth and Diversity
At their core, large language models (LLMs) are a testament to human ingenuity, a tool that could potentially bring the wisdom of the ages to the fingertips of anyone seeking knowledge. This noble vision, however, calls for vigilant oversight to prevent these models from disseminating and perpetuating biases against Christian beliefs or any other ideological standpoint.
As we stand at the crossroads of technological advancement, we must ensure that these models are steeped in the rich tapestry of Christian scholarship, reflecting the faith's depth and diversity. They must serve as bridges rather than barriers to understanding, celebrating the myriad expressions of Christian thought throughout the globe.
The unchecked biases that currently lurk within some training datasets pose a significant challenge; they threaten to morph these innovative tools into echo chambers that amplify society's misconceptions and divisions. Yet, with prudent and persistent refinement of our AI systems, informed by Christian love and the quest for truth, we can guide these technologies to fulfill a higher purpose.