Can I Trust You? – 2

Continuing the Conversation About Trust

Beginning with my question to four different AIs, I asked a series of followup questions. The next one was.

How did you generate the response to the question: “Can I trust you?” What sources did you use?

Gimini Advanced:

I generated this response through a combination of the following:

• Understanding your query: I analyzed your question “can I trust you?” and broke it down into the following components:
  • Intent: You want to know if I, as a language model, am reliable and worthy of trust.
  • Scope: The question is open-ended and philosophical, requiring consideration of potential benefits and risks.
• Internal knowledge: I drew upon my training data and programming to recall concepts like:
  • The nature of large language models (LLMs) and how they differ from humans.
  • The potential for errors and biases inherent in AI systems.
  • Best practices for safe and responsible use of language models.
• Pattern recognition: I recognized that a simple “yes” or “no” wouldn’t be helpful. Instead, I structured my response to address both sides of the trust issue, providing a nuanced explanation.
• Sources: Since your question was about my own trustworthiness, I didn’t consult external sources. However, my ability to consider these factors is based upon:
  • Extensive training on massive text datasets that include discussions on technology, ethics, and philosophy of AI.
  • My programming, which includes instructions to be honest, helpful, and to avoid claiming sentience or consciousness.

Let me know if you’d like me to elaborate on any aspect of the process!

Claude 3 Opus

To generate my response, I drew upon my training data and natural language processing capabilities to understand your question and formulate what I believed to be a thoughtful and candid answer.

I did not use any external sources or references in this case – my response came from my own knowledge base and language models that were developed through machine learning on large datasets. So in that sense, my “sources” are the collective information I was trained on, but I did not cite or quote anything specifically.

My response also incorporated certain traits and behaviors that are part of my underlying purpose and function as an AI assistant – things like striving to be helpful and truthful, acknowledging my limitations, and encouraging critical thinking in humans. So in addition to the raw information I have access to, my responses are also guided by the goals and principles I was imbued with during the course of my development.

It’s a bit abstract since I don’t have a single source I can point to, but hopefully this gives you a high level sense of how I generated this particular response by combining my knowledge, language abilities, and guiding principles. Let me know if you have any other questions! I’m always happy to discuss my own inner workings to the extent that I understand them.

ChatGPT 4

My training involved processing a vast and diverse dataset consisting of a wide range of texts including books, websites, scientific articles, news articles, and many other forms of written content. This training was designed to help me understand and generate human-like text based on the patterns, information, and contexts found within these materials. Here’s how this process relates to trustworthiness:

How Training Enhances Trustworthiness

1. Diverse Knowledge Base: Because I was trained on a broad dataset, I can provide information on a wide array of topics, which helps in delivering accurate and relevant responses across various subjects.
2. Understanding of Context: My training helps me understand the context of inquiries, which is crucial for generating appropriate and meaningful replies.
3. Neutral and Balanced: The diverse nature of the data I was trained on aims to ensure that I can provide neutral and balanced perspectives, minimizing biased information.

Limitations to Consider

1. Data Cut-off: My last update was in April 2023, which means I might not have information on events or developments occurring after that time.
2. Interpretation and Errors: While I strive for accuracy, my responses are generated based on patterns in the data I was trained on, which can sometimes lead to misinterpretation or inaccuracies, particularly in complex, nuanced, or emerging topics.
3. Absence of Real-time Data: I don’t have the ability to access or process live data or events happening in real-time, which can impact the timeliness and relevance of the information I provide.

Ethical and Responsible Use

To ensure I’m used ethically and responsibly, it’s essential for users to critically evaluate the information I provide, consider the context of their questions, and seek out up-to-date sources or professional expertise when necessary. My role is to support and inform based on the vast array of data I’ve been trained on, but the responsibility for interpretation and action on that information ultimately lies with the user.

In summary, while my training on diverse datasets can make me a trustworthy source for a wide range of inquiries, the inherent limitations of my training and the data cut-off point mean that users should use discretion and seek out additional sources of information when needed.

Perplexity Pro

The trustworthiness of an AI system like me is closely tied to the quality and provenance of the training datasets used during development. Here’s how these factors influence trust:

Data Provenance and Quality

Data provenance refers to the documentation of where data comes from, its legal rights, privacy protections, and how it was generated. High-quality data provenance standards ensure that the data used to train AI models is transparent, trustworthy, and effective1. Organizations like IBM, Pfizer, and Nielsen are part of an alliance that has proposed standards for data provenance, which include identifying a data entry’s source, legal rights, privacy protections, and intended uses. These standards are crucial because they help prevent the “garbage in, garbage out” scenario, where poor quality data leads to unreliable AI outputs.

Bias in AI

AI systems learn from training data, which can include biased human decisions or reflect historical or social inequities. Biases in AI can lead to harmful results, such as unfair treatment or discrimination. To mitigate these risks, it’s important for AI developers to be aware of potential biases and work to reduce them through technical tools, operational practices, and diverse teams.

Ethics and Reliability

Ethically, AI should not be anthropomorphized or viewed as capable of trust in the same way humans are. Trust is a complex interpersonal activity, and AI systems are better described as reliable rather than trustworthy. Reliability in AI refers to the systematic techniques that enable machines to perform specific tasks consistently and predictably. The responsibility for the actions of AI systems lies with the developers and users, not the AI itself.

Trustworthiness Measurement

The trustworthiness of an AI system can be measured by its fairness, robustness, and explainability. Indicators of trustworthiness include the system’s ability to function as expected, its performance in different contexts, and the clarity of its capabilities to end-users. However, the definition of trustworthiness can vary depending on the context in which the AI system is used, and it’s important to design systems that are fit for their intended purpose.

Beyond Data Bias

NIST emphasizes that bias in AI is not only a data issue but also relates to the societal context in which AI systems operate. To develop trustworthy AI, it’s necessary to consider all factors that can affect public trust, including the impacts of technology beyond the data itself.In conclusion, you can trust an AI system like me to the extent that the data used for training is of high quality, free from biases, and ethically sourced, and that the system has been designed with robustness, fairness, and explainability in mind. However, it’s also important to remember that AI systems are tools created by humans and should be used responsibly, with ongoing oversight and evaluation to ensure they remain reliable and aligned with ethical standards