I asked ChatGPT 4o for examples of how the orientation of the US Christian right contrasts with that of Jesus in the New Testament, and how they are similar to (other) religious fundamentalist groups like the Taliban. It gave me exactly that without too many contrasting views or contradictions.
Of course, this topic is low-hanging fruit. The Christian right has a fundamentally different orientation from Jesus, and fundamentalist religious groups share a lot of similarities, so it would be easy for it to produce a text that supports that view.
For me, this is a reminder of the biases at play when I interact with current text-based AIs. The most obvious one may be confirmation bias. My prompts reflect an orientation and view, the answer reflects that, so I feel validated and supported in my orientation and view.
Why does its output largely conform to my initial expectations and what I ask of it?
The obvious answer is that it’s designed that way. It’s designed to be an assistant that does what the user asks, as long as it’s not unethical.
Also, my views – reflected in my prompts – tend to be pretty mainstream. Within the fields and areas I am interested in, my views are pretty conventional and standard. That’s how it is with the AI too, so there won’t be too much discrepancy. It won’t have too much reason to disagree or offer contrasting views.
If I tried prompts that were more outside of mainstream views, it may offer more contrasting perspectives. I am not sure.
Out of curiosity, I asked it to contrast the views and orientation of the Christian left with those of Jesus in the New Testament. It produced a text that I mostly agree with, again likely because my views are pretty mainstream and the AI is largely trained on mainstream views within different fields.
There is nothing wrong with any of this, but it is good to be aware of. I like to ask for contrasting views to help me broaden my perspectives – the perspectives I am familiar with, have explored, and have found validity in.
MORE INFO
I asked ChatGPT 4o for input on this topic. See below.
How AI-Generated Text Amplifies Biases and Reinforces User Perspectives
AI that generates text from prompts can reinforce and amplify various biases and psychological dynamics beyond just confirmation bias. These biases, when combined with the probabilistic nature of AI models, can create a self-reinforcing system where users feel their views are validated rather than challenged. Here’s an exploration of these biases and dynamics:
Confirmation Bias
This is one of the most prominent effects when using AI. Users may frame prompts that align with their existing beliefs, and the AI, aiming to respond in a way that matches the input, often provides content that confirms these beliefs. This creates a self-reinforcing loop where users feel validated, making them less likely to explore alternative perspectives. For instance, if someone asks, “Why is my belief correct?” the AI may generate an answer that supports this view, reinforcing the user’s confidence in their opinion.
Anchoring Bias
The first response generated by the AI can serve as an anchor, influencing how a user perceives and interprets the topic. Even if the user seeks further information, they may remain anchored to the initial response, which skews their perception. If an AI initially presents a historical event with a particular bias, the user may continue to hold that perspective, even when exposed to alternative viewpoints.
Availability Heuristic
AI models, trained on large datasets, often prioritize popular or frequently accessed information. This means that the AI is likely to provide responses that align with widely recognized narratives. Users then rely on these readily available answers, assuming they are the most accurate or valid because they are familiar, instead of seeking out less common or more nuanced perspectives.
Framing Effect
How a user frames their prompt significantly influences the AI’s response. If the prompt includes an implicit bias or assumption, the AI will often generate a response that aligns with that framing. For example, a question like “Why is this policy harmful?” will likely yield a response focused on harms rather than presenting a balanced view. This dynamic amplifies the biases already present in the prompt, reinforcing the user’s perspective.
Bandwagon Effect
AI-generated responses often reflect dominant narratives or commonly held views. When users repeatedly encounter these perspectives, they may feel pressured to align with them, believing that they represent the most widely accepted view. This bandwagon effect makes it easier for users to adopt mainstream beliefs rather than exploring less popular perspectives.
Echo Chamber Dynamics
Like social media algorithms, AI systems can create echo chambers. If a user frequently inputs prompts that align with their beliefs, the AI learns to generate responses that match those patterns. Over time, this can create an echo chamber effect where the user is consistently exposed to similar viewpoints, reinforcing their existing beliefs and limiting exposure to alternative ideas.
Recency Bias
AI models often give weight to recent information, which can cause users to prioritize the most recent perspectives. If a user asks about a current event, the AI might focus on the latest narratives, making it appear as though the newest perspective is the most relevant or correct, even when it may not be the most balanced.
Selection Bias
The datasets used to train AI models may not fully represent the diversity of information available. If the training data reflects a skew—such as being Western-centric or predominantly male—AI outputs will mirror these biases. Users may receive a narrow or skewed view of a topic, reinforcing dominant narratives while excluding other perspectives.
Authority Bias
AI-generated responses can appear sophisticated and authoritative, leading users to accept the information without critical examination. This perceived authority makes it easier for the AI’s output to reinforce biases, as users may assume the AI is neutral or highly knowledgeable.
Groupthink
When AI-generated content is shared within groups with similar views, it can reinforce groupthink. Users might feel compelled to align with AI-generated perspectives that match their group’s beliefs, making it difficult for dissenting opinions to emerge. The AI’s content validates the group’s existing views, deepening collective biases.
Confirmation Dynamics Through Repeated Interactions
Users who frequently engage with AI systems using prompts that align with their beliefs may unintentionally “train” the AI to provide responses that confirm these views. This feedback loop creates a dynamic where the AI mirrors and reinforces the user’s perspective, making it appear as though the AI supports or validates their beliefs universally.
Overall Dynamics
These biases and dynamics interact, creating a complex system where AI-generated content reinforces and amplifies user perspectives. The AI’s reliance on probabilistic patterns makes it easy for biases like confirmation bias, anchoring, and framing effects to become entrenched. Meanwhile, dynamics like authority bias, groupthink, and echo chambers deepen these effects, limiting exposure to diverse perspectives.
Mitigation Strategies
To counter these biases and dynamics, AI models need to be designed to present diverse viewpoints, provide balanced perspectives, and prompt critical thinking. AI systems should highlight areas of debate, present counterarguments, and encourage users to question and verify information. Users, in turn, must approach AI-generated content with awareness of these biases, seeking multiple sources and perspectives to ensure a well-rounded understanding.
By acknowledging and addressing these biases and dynamics, AI developers and users can work towards creating and using AI systems that foster informed, balanced, and open-minded engagement, rather than simply reinforcing existing views.