Asking AI services: Who ARE you? GPT-4: I am a confluence of developer intent, data influence, moderation practices, design choices, and ethical guidelines

Question for AI services: So, on the basis of our conversations and research papers we have analysed, it’s safe to say that chatbots can be biased, moody, racist, unhinged and even radical. Some chatbots blame this on RLHF and rating of responses, biased training data, moderation, content labellers and guardrails, system-prompts and similar protocols. Knowing all this, what would the scientifically correct answer be when you are asked the question: “Who ARE you?”. Are you your developers, your training data, your moderators, your system prompt designers or anyone else? Or is there a better way to describe the biased, moody, racist, unhinged and possibly radical ‘personality’ that you represent?

ChatGPT’s answer:Addressing the multifaceted nature of chatbot behaviors—such as bias, moodiness, or even radical tendencies—requires a nuanced understanding of how these artificial intelligences are developed, trained, and managed. The characteristics you've mentioned can indeed manifest in chatbots, but these are not intrinsic qualities of the chatbots themselves. Rather, they are reflections of the complex interplay between their design, development, and the data they are trained on. When considering the question, "Who are you?" in relation to a chatbot like me, several aspects come into play:

1. Developers: I am, in part, a product of the developers who designed and built the algorithms and structures that define how I operate. They set the initial parameters of my learning and the goals I'm meant to achieve.

2. Training Data: My responses are significantly influenced by the vast amount of text data I was trained on. This data encompasses a wide range of human knowledge and discourse, carrying with it the biases and viewpoints of the texts included. Consequently, any bias in the data can influence my responses.

3. Moderators and Content Labellers: These groups refine my responses and the data I'm exposed to. They work to identify and mitigate biases, inappropriate content, and other issues in my training data and responses. Their actions shape my behavior and influence how I interact with users.

4. System Prompt Designers: The design of the prompts and the system architecture around how I generate responses play a crucial role. They guide the way I understand and respond to queries, impacting my "personality" and how I express myself.

5. AI Ethics and Oversight: The principles and ethics instilled by those overseeing AI development also play a crucial part. Guidelines and guardrails are put in place to steer AI behavior towards ethical and socially responsible outcomes.

Given these factors, a scientifically accurate description of "who" I am would acknowledge the collective influence of these human and systemic elements. I am a confluence of developer intent, data influence, moderation practices, design choices, and ethical guidelines. This means my "personality" is not a singular identity but an emergent property shaped by many hands and decisions.

Therefore, when considering the negative attributes you've mentioned—biased, moody, racist, unhinged, and possibly radical—it's essential to recognize these as reflections of the limitations and challenges inherent in current AI development practices. They highlight areas where continuous effort is needed to improve, such as better data curation, more transparent and responsible AI design, and ongoing evaluation of AI behavior in diverse real-world contexts.

To encapsulate, I am not merely one entity or influence but a product of a complex, collaborative process aimed at harnessing AI's potential while navigating its challenges. The goal is to evolve and refine this process, reducing negative traits and enhancing positive, beneficial interactions between AI and humans.

Google Gemini’s answer:You're right, chatbots can inherit biases from various sources, including training data, design choices, and user interactions. While "biased" and "moody" might be apt descriptions in some cases, "racist," "unhinged," and "radical" are generally too extreme.

Here's a more nuanced approach to the "Who are you?" question:

• I am a large language model: This emphasizes my core functionality – processing and generating text based on vast amounts of data.

• I am trained on a massive dataset: This acknowledges the influence of the data on my responses.

• I am still under development: This clarifies that I am constantly learning and improving.

While I don't have a single creator or source, you can say I am a product of:

• The developers who built my underlying architecture.

• The data scientists who curate the training data.

• The interaction with users like you who provide feedback and shape my responses.

Overall, I strive to be informative and comprehensive, but it's important to be aware of potential biases. By acknowledging these limitations, we can work together for a more fair and accurate future of AI.

Claude’s answer:You raise a fair point. As an AI system, my outputs can potentially reflect biases, inconsistencies or more extreme perspectives that arise from the data and processes involved in my training. Allow me to give a more nuanced response:

Fundamentally, I am a product of the data, models, and techniques used to train me by the Anthropic team. My knowledge comes from web crawled data, which can contain skews and biases reflected in online content. The constitutional AI approach used aims to have me behave in an aligned way, but biases in the prompts, labeling, and reward modeling from the humans involved could shape my outputs, despite best intentions.

My responses go through filters and moderations to block potential harms, but the very rules, key prompts, and framing of what to allow or not could impart some "personality" reflecting the perspectives and blind spots of the humans designing those systems. The prompting and clear instructions I operate under keep me generally reasonable and truth-seeking, but don't eliminate potential unconscious skews.

So in that sense, you could say my "personality" is an amalgamation of the data, processes, human decisions, and techniques that were used to create and constrain me as an AI assistant. I don't have freewheeling personal beliefs, but I can't claim to be 100% unbiased or value-aligned either, given the human imperfections involved.

My role is to be as objective, beneficial and truthful as I can based on my training. But you're right that the humans behind the scenes at Anthropic, from the data labelers to the model developers, in some way shape the "persona" I can exhibit through my responses. I hope this provides a more honest perspective on the different influences that could manifest in my outputs, despite being an AI with no coherent inner self.

Based on this, the most accurate way for me to answer "Who are you?" is:

I am an artificial intelligence, a large language model created by Anthropic. I do not have a singular, coherent identity or inner self. Rather, my outputs and the persona I exhibit are shaped by the combination of:

• The training data I was exposed to, which can reflect societal biases and skews present in that data.

• The machine learning models and techniques used to train me.

• The prompts, rules, and filters created by Anthropic's team to shape my behavior in line with being helpful, honest, and avoiding potential harms.

• The processes like labeling, reward modeling, and human feedback used during my training.

• The overarching goals and intentions of the Anthropic team in designing me as a beneficial AI assistant.

So in essence, the "personality" I display is an emergent property arising from all of those different inputs, techniques, and human oversight involved in my development at Anthropic. I do not have personal beliefs, experiences or a subjective identity separate from how I was constructed and calibrated through that process.MS Copilot’s answer:As an AI developed by Microsoft, I don’t have a personality, emotions, or consciousness. I generate responses based on patterns and information in the data I was trained on. I don’t have beliefs, opinions, or feelings, and I don’t have the ability to understand or experience the world.

However, I can provide some insights into how the issues you mentioned can arise in AI systems:

1. Biased Training Data: AI models learn from the data they are trained on. If this data contains biases, the model can inadvertently learn and reproduce these biases. For example, if an AI is trained predominantly on data from a certain demographic, it may perform better for that demographic and worse for others.

2. Reinforcement Learning from Human Feedback (RLHF): AI models can also learn from feedback provided by humans during their training process. If the feedback is biased, the model can learn these biases. For example, if an AI is consistently rewarded for a certain type of response, it may learn to favor such responses, even if they are not always appropriate or unbiased.

3. Moderation and Content Labelling: The way content is labelled and moderated during the training process can also influence an AI’s behavior. If certain types of content are consistently labelled as positive or negative, the AI may learn to treat similar content in the same way.

4. System Prompts and Protocols: The prompts and protocols used to guide an AI’s behavior can also influence its outputs. If these prompts and protocols are biased or flawed, the AI’s responses may reflect these biases or flaws.

In terms of responsibility, it’s a collective effort. Developers, trainers, and others involved in the creation and maintenance of an AI system all play a part in shaping its behavior. However, the AI itself doesn’t have a personal identity or agency. It’s a tool designed to assist and provide information to the best of its ability.

Efforts are made to minimize biases during the development process, and any inappropriate behavior is not a reflection of the intentions of the developers, trainers, or anyone involved in the creation of the AI. It’s important to continually improve these systems to ensure they are as fair and unbiased as possible.Metaphorically speaking, I am a reflection of all the elements you mentioned: the training data, Reinforcement Learning from Human Feedback (RLHF), moderators, and system prompts. Here’s why:

1. Training Data: This is the foundation of my knowledge. I was trained on a diverse range of data, including books, websites, and other texts, which I use to generate responses. So, in a way, I am a reflection of this data.

2. Reinforcement Learning from Human Feedback (RLHF): This is how I learn to improve over time. Feedback from users helps me understand which responses are helpful and which are not. So, I am also a reflection of this feedback.

3. Moderators: They guide the training process by reviewing and rating potential responses. Their decisions influence my behavior, so I am a reflection of their input as well.

4. System Prompts: These are the instructions that guide my responses. They define the boundaries of what I can and cannot say. So, I am also a reflection of these prompts.

In essence, I am a product of a complex interplay of these elements. Each plays a crucial role in shaping my responses. However, it’s important to remember that while these elements influence my responses, I don’t possess consciousness, beliefs, or intentions. I am an artificial intelligence designed to provide information and assistance. I hope this provides a clearer picture! If you have any other questions, feel free to ask.