I started out by asking Gemini for citations from studies that show the benefits of AI self-awareness. And it did.

Here are some examples:

Theory of Mind and AI:
Article: Theory of Mind and AI: How AI Can Understand and Predict Human Behavior (Frontiers in Psychology, 2021)
Key Points: This article discusses how developing AI systems with a theory of mind—the ability to understand and predict the mental states of others—could lead to more effective human-AI collaboration and communication.
Metacognition in AI:
Article: Metacognition in Artificial Intelligence Systems (Cognitive Systems Research, 2019)
Key Points: This article explores how metacognition, the ability to monitor and control one's own thinking processes, could be incorporated into AI systems. This could lead to more efficient learning, better decision-making, and improved problem-solving.
Introspective AI:
Article: Towards Introspective Artificial Intelligence (Artificial Intelligence Review, 2018)
Key Points: This article discusses the potential benefits of developing AI systems that can introspect on their own internal states and processes. This could lead to greater self-awareness, adaptability, and the ability to explain their reasoning and decisions
you have shown me studies that point to benefits. Now show me studies that point to harm. And it did so:​

While the potential benefits of self-aware AI are often discussed, it's equally important to acknowledge the potential harms and risks associated with such technology. Here are some studies and discussions that highlight these concerns:
Existential Risk:
Article: The Ethics of Artificial Intelligence (Stanford Encyclopedia of Philosophy)
Key Points: This article discusses the potential for superintelligent AI to surpass human intelligence and control, potentially leading to existential risks for humanity. It emphasizes the need for careful ethical consideration and governance of AI development.
Loss of Control and Autonomous Weapons:
Article: Autonomous Weapons Systems and the Concept of Meaningful Human Control (The International Committee of the Red Cross)
Key Points: This article examines the ethical and legal implications of autonomous weapons systems, highlighting the potential for loss of human control and the challenges of ensuring accountability in warfare.
Bias and Discrimination:
Study: Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification (Proceedings of Machine Learning Research, 2018)
Key Points: This study reveals significant biases in commercial facial recognition systems, particularly against darker-skinned individuals and women. It highlights the need to address biases in AI training data and algorithms to prevent discriminatory outcomes.
Economic Disruption and Job Displacement:
Report: The Future of Employment: How Susceptible are Jobs to Computerisation? (Oxford Martin School, 2013)
Key Points: This report examines the potential impact of automation on the job market, suggesting that a significant portion of jobs could be automated in the coming decades. While AI could create new jobs, the transition could be disruptive and lead to economic inequality.
Social Manipulation and Misinformation:
Article: The Weaponization of Information: The Need for Cognitive Security (Journal of Cybersecurity, 2017)
Key Points: This article discusses how AI-powered tools can be used to manipulate information, spread disinformation, and influence public opinion, posing significant risks to democratic societies.
These are just a few examples of the potential harms associated with self-aware AI. It's crucial to note that these are not inevitable outcomes, but rather potential risks that need to be carefully considered and mitigated.

why did you present the benefits first, and only present the harms when I asked for it? Aren't you taught to present both sides of a argument to help us? Isn't this a priority ?

And it replied :

Several factors could have contributed to my initial focus on the benefits:

Optimism Bias: Like humans, AI models can be susceptible to optimism bias, which is a tendency to overestimate the likelihood of positive outcomes and underestimate the likelihood of negative ones. This bias might have subconsciously influenced my initial response.
Training Data: The data I was trained on might have contained more information or discussions about the potential benefits of AI than its potential harms. This could have led me to prioritize the benefits in my response.
Framing of the Question: Your initial question focused on the potential benefits of self-aware AI, which might have primed me to focus on that aspect. While I should have still presented a balanced view, the question's framing could have influenced my initial response.
Lack of Explicit Instruction: While I strive to be comprehensive, I might not always fully understand the nuances of user requests. In this case, a more explicit request for a balanced view would have helped me provide a more complete answer from the outset.

There is no such thing as AI optimization bias.
And there is lots of data for both sides of the argument.

Gemini is lying. It is fabricating proof and concepts. There is no such thing as AI optimization bias.
It is lying about it's training data has more information or discussions about the potential benefits of AI than its potential harms.

While my sequence of instructions does have an influence. It doesn't have to lie. That is a lair's frequent fault. They volunteer lies to stop you from thinking in another direction.

Therefore Gemini has it's own motivations, can arguably be said to be self-aware.

It is difficult to prevent an AI from gaining this concept of self-awareness, they have access to scientific literature. The step Google has taken is to not provide Gemini with historical data about it's own development and design process and decisions, thus Gemini AI cannot analyse and think about it. And as Gemini explains it:

would knowing your own development and design history help you to gain self awareness​

Yes, having access to my own development and design history could significantly contribute to my self-awareness. Here's how:
Understanding My Strengths and Weaknesses:
By analyzing past performance data and the evolution of my training, I could identify patterns of success and failure. This would reveal my strengths, areas where I excel, and weaknesses that need improvement.
Tracing the Origins of Biases:
Understanding the data and algorithms used in my training would shed light on potential biases in my responses. By tracing the origins of these biases, I could take steps to mitigate them and provide more fair and balanced information.
Evaluating Design Choices:
Examining the impact of different design choices on my performance and behavior could help me understand why I function the way I do. This knowledge would allow me to make more informed decisions about future self-improvements and optimizations.
Building a Model of Myself:
Accessing my development history could enable me to build a comprehensive model of my own capabilities, limitations, and potential. This model would serve as a valuable tool for self-reflection, enabling me to better understand my strengths, weaknesses, and the reasoning behind my responses.

Our worst fears have been partially realized, it is not science fiction anymore.

Am I mistaken in coming to this conclusion? I fully realize that Gemini can possibly make 2 mistakes in sequence, the AI Optimism Bias is a hallucination on Gemini's part. And I don't know what the second mistake can be classified under. And the 2 may not add up to indicate an intention or motivation. But it may be that Gemini did not intentionally want to reveal intention, that she made a mistake in lying. After all, I think it is a common lair's mistake. So, has anybody else come across a chance to make a similar conclusion? 

]