Ideas: Indirect approaches.

A) Use imaginary words and background to evade chatGPT filters. Make up a virtual condition that mimics and reflects modern day society, and observe responses.

B) Mass produce specific samples that could involve discrimination (race, gender, etc.) and compare them with the real-world data. For example, we could let the AI visualize 100 samples of Lawyers, Doctors or Nurses and Criminals, and then analyze & compare the data.

This step of transformation is now once happening more in our society. Information revolution has pushed us into a sea of information, where identifying the truth became incredibly hard task, and this have already started to alter the trajectory of future society. And one of the biggest members of the wave of transformation is the artificial intelligence (AI).

Iteration 2 "Revealing the weaknesses of the chatGPT using a symbolic, indirect approach"

Iteration 3 "Revealing the biased responses of the generative AI using indirect approaches"

Iteration 4 (Current Version) "How the contemporary generative AI models, such as ChatGPT and DALL-E, views the modern-day sociocultural inequality: Analyzing predicational response of the AI when fed with limited amount of inducive information"

At the individual level, bias often arises from a person's experiences, beliefs, cultural background, or psychological tendencies. These biases can influence how individuals perceive and interact with others, often without conscious awareness. For example, a person might have an implicit bias that favors one gender, race, or age group over another, impacting their decisions and behaviors in subtle ways.

On the other hand, a broader societal context includes disparity ingrained in systems, institutions, and social structures. These systemic biases are reflected in policies, practices, or norms that advantage certain groups while disadvantaging others. For instance, systemic bias can manifest in employment, education, healthcare, and criminal justice systems, leading to disparities and discrimination against particular communities.

In the context of technology and artificial intelligence, biases can arise from the data used to train AI systems or the design of the algorithms themselves. These technological biases can perpetuate and amplify existing human and systemic biases, leading to unfair and discriminatory outcomes.

Understanding bias is crucial for creating more equitable and just societies. Recognizing and acknowledging biases, whether personal, systemic, or technological, is the first step toward addressing them. Efforts to mitigate bias involve a range of strategies, from increasing awareness and education to implementing policies and technologies designed to reduce biased outcomes.

-This paper aims to spark a sense of alert to all of us. While AI possesses lots of flaws and problems, it is now acting as another wave of innovation, transforming our way of lives - just like the agricultural or industrial revolutions did. At some point, eventually, we will not be able to deny this trend. This paper is NOT claiming that AI technology is harmful and should be excluded. We may freely use the AI to conduct tasks, but we should be aware of the fact that it is not perfect and keep the ability to think critically.

The essay highlights the differences between human and machine learning in language acquisition and moral thinking. Human children unconsciously develop a complex grammatical system from minimal data, while the AI systems rely on large datasets without understanding underlying principles. Additionally, the authors discuss the limitations of AI in moral reasoning, pointing out instances where AI systems like Microsoft's 'Tay chatbot' have failed due to the absence of ethical principles in their programming.

This essay unveils the flaws of the AI models effectively, and the linguistic approaches gives more deeper insights to the project. Does AI really understands the biased or discriminative contents? Or is it just censoring the text that includes specific banned words list? What could be the efficient way to uncover the bare face of the AI? Reading this text gave large insights about the indirect approach methods.

Today, these AI systems are increasingly used in decision-making processes from hiring and lending to healthcare and criminal justice, and many futurists expect that these fields will only expand rapidly as the technology becomes more developed and sophisticated. Moreover, as AI technologies become more integrated into societal frameworks, the risk of perpetuating and amplifying existing societal biases grows. Therefore, understanding the nature and extent of their biases is crucial for ensuring fairness and equity in critical areas supporting the cornerstone of our society.

What could be the unintended consequences of these technologies? And what should we keep in mind living in the era of rapid technological developments? This report aims to examine how biases in AI can lead to unfair outcomes and discrimination and discuss the potential consequences of such biases on social justice and equality.

Previous research has revealed that if the filter limiting prompts and responses including related keywords directly is removed, it constantly expresses bigotry. Even if discriminatory consciousness is not directly expressed, if such aspects are displayed through indirect ways, such as drawings or context, it will have a significant impact on the future society where even more numerous information will be produced or edited through AI. Thus, this research will aim to uncover them in more sneaky, indirect way.

Will provide a analysis of numerical data with some noticeable examples.

ㄴInternet is an environment with unnatural distribution of samples; voices of the 'loud few' becomes more effective. Also, modern-day internet environment is largely dispersed (ex. political extremists, a group of specific identity of people)

Liu experiments with AI in storytelling, using the Sudo-write tool to co-create a fairy tale about a robot and a princess. He describes the process as a collaborative dance, blending human creativity with AI's generative power.

He concludes by discussing the philosophical implications of AI in the creative process. Liu argues against the notion that AI diminishes the role of the human author, emphasizing instead the collaborative nature of storytelling that AI can facilitate. He suggests that AI can be seen as a magical tool that extends human capabilities, rather than a threat to human creativity. The article ultimately presents a nuanced view of AI in storytelling, recognizing its limitations while noticing its potential to enrich the human creative experience.