Artificial Intelligence & Human Values - Lecture 3

Ways people understand and interpret events or situations. Frames help us assign meaning and make sense of things.

Using AI tools to create text or content for assignments. This is plagiarism and unethical, and will result in a failing grade.

A way of framing AI that focuses on who controls the information, how it's used for influence, and potential manipulation.

A unit exploring the complex relationship between AI and how knowledge is created and disseminated.

An influential work examining how technology influences ethical decision-making and shapes our understanding of the world.

A seminal work in feminist science studies, emphasizing the importance of diverse perspectives and recognizing the limitations of any single worldview.

Challenges faced in the emerging relationship between AI and the creation and dissemination of knowledge.

Examining how AI technologies impact the process of scientific research, discovery, and communication of knowledge.

This question addresses the need to adapt to the changing demands and possibilities of a world influenced by computing and AI. It suggests that education must equip individuals with the right skills to thrive in this new reality.

This question focuses on the need for education to be flexible and responsive to the rapid evolution of computing and AI. It acknowledges that learning methods and educational systems may need to change to meet these challenges.

This question highlights the importance of safeguarding human values and ethical principles in the age of AI. It emphasizes the need for education to instill critical thinking and a sense of responsibility in the face of technology.

This question explores the integration of computing and AI into education. It asks how we can best leverage their potential while considering the potential implications for learning and teaching.

This phrase describes a crucial shift in learning and knowledge-making. It suggests that educational practices are evolving beyond traditional methods and embracing the use of computational tools to facilitate learning and problem-solving.

This refers to the process of forming or developing ideas and concepts. In the context of computing and AI, it suggests that these tools can assist in brainstorming, exploring possibilities, and generating innovative solutions.

Licklider envisioned computers to be more than just tools for solving pre-defined problems. He believed that they could support the very process of thinking and idea formation.

This refers to established methods and frameworks through which knowledge is acquired and validated within a society. Computing and AI challenge traditional knowledge systems and necessitate a reassessment of how we understand and create knowledge.

AI tailored for a particular field, like healthcare or finance. It uses specialized data and algorithms to solve problems within that specific domain.

The ability to repeat an experiment or study and get similar results. It's crucial for verifying scientific findings.

Openness about how AI works, including the data used, algorithms, and decision-making process. It helps build trust and accountability.

Different methods or perspectives used to gain knowledge. These can include scientific methods, personal experience, or cultural traditions.

AI relies heavily on large datasets for training and learning. These datasets shape the knowledge and decisions AI makes.

The idea that data isn't inherently neutral but reflects existing biases and power structures. Its interpretation impacts knowledge and decisions.

A concept of knowledge that is believed to be true and applicable to everyone, regardless of culture or context. This can be problematic as it often overlooks diverse perspectives.

The idea that AI challenges our existing ways of thinking, learning, and knowing. It prompts us to critically examine our assumptions and practices.

This refers to the difference between what an AI tool tells you and what you expect or believe to be true. It's crucial to understand the AI's limitations and potential biases.

AI learning tools change the dynamic between students and teachers. The role of the teacher shifts from delivering information to facilitating learning and guiding students to use AI effectively.

This is the idea that with enough computational power and data, algorithms can replace traditional scientific theory. The belief is that data patterns reveal truths without the need for human-built models.

This refers to a shift in scientific methodology driven by AI and Big Data. It focuses on finding patterns in vast datasets, prioritizing prediction and correlation, and leaving explanation to a lesser role.

This critiques the notion that data is objective and self-explanatory. It stresses that data is always collected with a purpose and interpreted based on existing knowledge, making it theory-laden.

This refers to Big Data as a reality shaped by technology and social forces. It's not just about volume, variety, and velocity, but also the impact on society and how data is governed.

This questions whether AI can be truly objective and suggests that AI is always embedded within a specific context, influenced by the data it's trained on and the goals of its creators.