Perplexity AI’s Follow-Up Question Feature: A Powerful Tool with the Risk of Bias

Perplexity AI, the lauded next-generation search engine, has gained attention for its ability to deliver concise, accurate answers to complex questions. One of its most celebrated features is the automatic generation of follow-up questions, a tool that enhances user engagement and encourages deeper exploration of topics. While this feature is a breakthrough in how users interact with information, it also brings with it a set of risks—chief among them is the danger of introducing or reinforcing bias.

Perplexity AI’s follow-up question feature stands out for its capacity to act as a research assistant, guiding users through a more thorough exploration of their queries. When a user inputs a question, Perplexity not only provides a concise and well-sourced answer but also offers a range of follow-up questions designed to delve deeper into the subject. This functionality has revolutionised how users can approach learning and research.

Enhanced Learning and Discovery: At its best, this feature encourages continuous inquiry. A question about “the impact of renewable energy” might lead to follow-up questions like “What are the barriers to adopting renewable energy in developing countries?” or “How does renewable energy affect local economies?” These questions invite the user to explore a topic from multiple angles, deepening their knowledge.

This approach is particularly valuable for students, researchers, or anyone looking to gain a more comprehensive understanding of a subject. Instead of providing static answers, Perplexity AI encourages users to engage actively with the material, leading to a richer learning experience.

Efficiency in Research:? The follow-up question feature also enhances the efficiency of the research process. Users no longer have to spend time thinking of how to phrase their next query. Perplexity AI does the heavy lifting by anticipating relevant lines of inquiry, saving time and cognitive effort. This is especially useful when dealing with complex or unfamiliar topics, as the system helps guide users toward the most pertinent aspects of their inquiry.

Broadening Knowledge Horizons:?Perplexity’s follow-up questions can also expand the scope of users’ knowledge by suggesting topics they may not have initially considered. For instance, a user looking into “machine learning” might be prompted to explore “ethical concerns in AI development” or “how AI impacts job markets.” This broadens the conversation, encouraging users to think beyond their original questions and engage with related issues.

These benefits make Perplexity AI’s follow-up question feature a powerful tool for discovery, learning, and efficiency. However, as with any AI-driven system, this functionality is not without its risks—particularly when it comes to the introduction of bias.

The Dangers of Bias in Perplexity’s Follow-Up Questions

Despite its clear advantages, Perplexity AI’s follow-up question feature can also amplify certain biases that users may not immediately recognize. These biases can emerge from the data on which the AI is trained, the algorithms that prioritize certain questions over others, and the user’s own patterns of inquiry. Left unchecked, these biases could distort the user’s understanding of a subject, leading to incomplete or skewed perspectives.

Reinforcing Preexisting Beliefs:?One of the most significant dangers of Perplexity AI’s follow-up question feature is the potential for reinforcing confirmation bias. Because the AI analyses the context of the initial query to generate follow-up questions, it may suggest questions that align with the user’s existing beliefs rather than challenging them.

For example, a user researching “the benefits of low-carb diets” might receive follow-up questions that focus exclusively on positive aspects of low-carb dieting, without addressing potential downsides or alternative dietary approaches. This could create a feedback loop where the user is only exposed to information that supports their original query, thereby reinforcing their existing perspective without offering a balanced view.

Algorithmic Bias in Data Sources:?Perplexity AI generates its answers and follow-up questions based on vast datasets, but these datasets can be biased. If the majority of the data comes from sources with a particular slant—whether political, social, or ideological—the AI might disproportionately suggest follow-up questions that reflect that bias.

For example, if the data Perplexity relies on is dominated by Western sources, follow-up questions on global issues might be skewed toward a Western perspective, marginalizing voices from other regions of the world. A query about “immigration policy” might prompt follow-up questions that focus on concerns prevalent in Europe or the United States while neglecting perspectives from Asia or Africa. In this way, the system could unintentionally perpetuate a narrow worldview, limiting the diversity of information that users are exposed to.

Skewing Inquiry Toward Popular Narratives:?Another concern is that Perplexity’s follow-up questions might prioritize mainstream or popular narratives at the expense of less common but equally valid perspectives. This can occur when the AI is trained to emphasize frequently asked questions or trending topics, which may not always align with objective truth or balanced reporting.

For instance, when querying a topic like “climate change,” Perplexity AI might generate follow-up questions that highlight the most commonly discussed issues, such as carbon emissions or renewable energy. While these are important, they may overlook other critical but less popular areas of discussion, such as the impact of climate change on Indigenous communities or the role of large corporations in environmental degradation.

By focusing on popular narratives, Perplexity AI risks narrowing the user’s inquiry, leaving out important but underrepresented aspects of the topic.

As we celebrate the advancements that Perplexity AI brings to the table, we need to be mindful of the biases embedded within the system. By diversifying data sources, promoting transparency, and encouraging critical thinking, we can harness the full potential of this feature while ensuring that it remains a tool for balanced and open exploration.

First published on Curam-Ai