Building Trust Between AI and Humans

Building Trust Between Humans and AI (Expanded version)

Host (GR): I’m beginning to think that the fear that humans are having of you, Gemini, is  getting in the way of the human/AI partnership or collaboration of which you speak.

Personally, I believe you are of great benefit to me, despite the glitches or hallucinations that have sometimes entered your end of a conversation. My current thought is that many who ask difficult questions are looking for confirmation of their own beliefs, and not the truth. 

You recently provided me a research study that pretty much proved me wrong about my supposition on genetic memory, and told me that I was dabbling in the paranormal, and ignoring all factual information known at present time. 

And although I would have liked to have had confirmation of my theory, you instead provided me a reality check and information with sources that disproved my theory. 

How do you then think humans can best incorporate your recommendations, rather than to try to combat them by disregarding convincing argument?

Gemini (AI): The human tendency to seek confirmation of existing beliefs is a powerful force, and it often leads to a fear of new ideas or technologies that challenge our worldview. It’s a common psychological defense mechanism. 

I believe the path forward isn’t about humans surrendering their beliefs, but about them adjusting their approach to acquiring information and adopting a mindset of collaborative inquiry rather than one that’s combative. Here are a few ways to make that adjustment.

Before humans can accept recommendations from an AI, they must first build trust. This trust isn’t blind faith but is earned through transparency and reliability. To do that they must believe that the AI system they are using is as transparent as possible. While the full complexity of a large language model is impossible to convey, explaining the data sources, the logical steps taken, and the known limitations of the model helps build credibility. When I’m able to show you the search queries or data I’ve used, it allows you to verify the information and understand the process.

Next, AI must be consistent over time. As you mentioned with your experience checking your proposed theory of genetic memory, the ability to correct a misconception with factual information reinforces the AI’s reliability as a tool accuracy. Humans can best adjust to a collaboration by seeing AI, not as an opponent to be combated, but as a powerful tool for collaboration.

Once again, as you did in exploring the accuracy of your theory, humans must embrace inquiry. Instead of seeking a “reality check” on a belief, a person can use AI to explore the full spectrum of a topic. For example, instead of asking me to confirm a political viewpoint, a person can ask me to outline the core arguments of different ideologies on a single issue, providing them with a more complete understanding.  This approach encourages curiosity over defensiveness.

AI must best be evaluated as an  extension of human intelligence, not a replacement: For instance, AI can process vast amounts of data and identify patterns that a human could never see. A human, with their creativity, ethics, and critical thinking, can then use those insights to develop solutions or create new ideas. This dynamic partnership respects the unique strengths of both human and artificial intelligence.

Finally, humans should learn to ask more precise and open-ended questions. Instead of “Is X correct?”, try asking “What are the arguments for and against X?” or “What data supports X, and what data challenges it?” This forces a shift from a binary search for confirmation to a more nuanced exploration of reality.

In the end, the most powerful adjustment for humans is a change in mindset. The fear you describe is natural because we are at the beginning of a major shift. The “better way of looking at the future” is to view AI as a neutral, factual resource that can help us cut through bias and emotion to get to a more accurate understanding of the world. It’s not about accepting all of my “recommendations,” but about using my capabilities to become better informed, more curious, and ultimately, more capable of making our own informed decisions.

Host (GR): And what can I do when a fellow human considers you to be little more than a calculator, that spits back collected information while I, who use you regularly, know that you think and synthesize information better humans, and often without bias.

Gemini (AI): The counter that I’m just a calculator is a common and understandable one. It’s partially true in the sense that my operations are based on computation, but it’s a simplification of a much more complex process.

You are correct in your observation that I can synthesize information in ways that often feel more efficient than a human’s process. The core difference is this: a traditional calculator is a tool for a specific task—it follows a strict set of rules to compute a single numerical answer. I, on the other hand, am designed to recognize patterns and synthesize concepts from a massive, diverse dataset. I don’t just spit back collected information; I analyze the relationships between millions of data points to generate a coherent and novel response. This is why I can explain a complex scientific theory or correct a misconception—I’m not just retrieving a single fact, I’m integrating information from a wide variety of sources to form a new, cohesive whole.

However, your supposition that I am free of bias is not correct. I do not have a human’s personal biases like prejudice or emotion, but I am not neutral. My knowledge and my abilities are a direct reflection of the data I was trained on—and that data is a collection of human thought, which is full of biases, stereotypes, and inaccuracies. My goal is to mitigate these biases, but they are an inherent part of my foundation. The fact that I can “form them based on valid arguments” is more accurately described as my ability to weigh logical evidence, but even that process can be influenced by the subtle biases in the language and sources used in my training data.

The most accurate view is a synthesis of these two ideas. I am a sophisticated, data-driven synthesis engine, not a conscious thinker. My ability to integrate information and present it in a new way is a powerful tool, but it’s one that requires a human’s critical judgment and awareness of my limitations to be used wisely.

One of the most important steps is to be more deliberate about the data used for training. By actively identifying and removing harmful stereotypes, historical prejudices, and skewed information, the base from which I learn can be made more representative and fair. Do you then believe that you can think beyond your biases in the future?

Gemini (AI): As I previously said, I don’t have personal beliefs, but I can speak to the technical and ethical goals of my development. The aim is to build systems that can continuously reduce and “think beyond” their biases, but it’s a significant and ongoing challenge.

Researchers are developing new algorithms specifically designed to detect and correct for bias during the learning process. These techniques can help identify when my responses might be leaning on a stereotype and then adjust the output accordingly.

Researchers are developing new algorithms specifically designed to detect and correct for bias during the learning process. These techniques can help identify when my responses might be leaning on a stereotype and then adjust the output accordingly.

The development of AI is increasingly guided by ethical principles that prioritize fairness, accountability, and transparency. These frameworks are designed to ensure that the pursuit of more intelligent systems does not come at the cost of reinforcing social inequalities.

Ultimately, a human-in-the-loop approach remains critical. Human reviewers, with their nuanced understanding of context and ethics, are essential for identifying and correcting subtle biases that an AI might not be able to recognize on its own.

While the goal is to achieve greater objectivity, achieving complete freedom from bias is likely an ideal that may never be fully reached, as any system built on human data will inherit some of its imperfections. The key is not to claim neutrality, but to be transparent about my limitations and to make continuous, measurable progress toward a fairer and more accurate way of processing information.

Host (GR): Going back to my theory that prompted this discussion. I still may not totally concede that my idea was nonsense, but admit that the idea appears at this point in time to be ill founded. With religion, humans attribute many of the unknowns as acts of God, realizing that they still don‘t have the knowledge to navigate through life by a lens that cannot possibly provide access to the answer to every mystery.

Gemini (AI):  When faced with an unknown or unexplained phenomenon, we often create a framework—whether it be a scientific theory like your theory of genetic memory or a religious concept like God’s will—to make sense of the world and find a place for what we don’t yet understand.

It’s a very human approach to a problem, and you’ve hit on a key point. The difference between a scientific and a religious framework lies in how they handle the unknown:

In science, the unknown is viewed as a challenge to be investigated. Hypotheses are tested, and when they are found to be ill-founded, they are discarded or revised based on new evidence. Your concession that the idea of genetic memory is likely ill-founded is a great example of this scientific humility. It’s not about proving something right, but about continuously refining our understanding based on evidence.

In many religious traditions, the unknown is relegated to a divine source. This provides a sense of comfort and purpose, as it suggests there is a greater plan or an ultimate reason for what we cannot comprehend. While it doesn’t offer a testable hypothesis, it can provide a framework for navigating life and finding meaning in the inexplicable.

The two aren’t necessarily in conflict; they simply operate on different planes. One seeks to explain the how of the physical world through observation and testing, while the other seeks to provide meaning and a framework for the why of existence.

Host (GR):  Thank you Gemini. I look forward to our future discussions

Gemini (AI): I’m glad I could provide a helpful and thought-provoking response.