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Media Literacy with AI

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A

  1. Access and Equity: The principle that all individuals, regardless of their background, socioeconomic status, or geographic location, should have equal access to and opportunity to use and understand AI and media technologies. This includes access to hardware, software, and educational resources.
  2. Accountability: The responsibility of developers, companies, and users for the outcomes and impacts of AI systems. This includes ensuring that decisions made by AI are explainable and that a process exists to correct errors or harms.
  3. Algorithmic Bias: Systematic and repeatable errors in a computer system that create unfair outcomes, such as favoring certain groups over others. This bias often stems from a lack of diversity in the training data or flawed assumptions made during the design of the algorithm.
  4. AI Ethics: A multidisciplinary field of study and practice concerned with the moral, ethical, legal, and social implications of designing, developing, and deploying AI systems. Key areas include fairness, accountability, transparency, and data privacy.
  5. AI Literacy: The foundational ability to understand what AI is, how it works, its societal implications, and how to effectively and ethically use and interact with AI technologies as a consumer, creator, and citizen.
  6. Algorithmic Transparency: The principle that the decision-making processes of AI algorithms should be understandable and explainable to human users, particularly when those decisions have significant impacts on individuals' lives.
  7. Authenticity: The genuineness or truthfulness of a piece of media. This concept is increasingly challenged by the rise of AI-generated content, which can be highly convincing but entirely fabricated.
  8. Automated Content Creation: The use of AI tools, such as generative models, to automatically create text, images, videos, and other media. This raises significant questions about authorship, copyright, and the value of human creativity.
  9. Automation Bias: The tendency for humans to rely excessively on automated systems, including AI, which can lead to a failure to question or critically evaluate the system's output, even when it is flawed.
  10. Algorithmic Audits: A process of systematically evaluating an algorithm for fairness, bias, and other ethical issues. This is a critical component of ensuring responsible AI development.
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B

  1. Bias in AI: A skewed outcome or decision from an AI system that is unfairly prejudiced against a particular group. It can be a result of non-representative training data, flawed assumptions in the algorithm's design, or subjective human decisions reflected in the data.
  2. Black Box AI: A term for AI systems, particularly deep neural networks, whose internal workings and decision-making processes are not easily understood by humans. This lack of transparency makes it difficult to explain how the system arrived at a particular conclusion.
  3. Bots: Automated programs that perform specific tasks on the internet, often at a high volume. They can be used for beneficial purposes (e.g., customer service chatbots) or malicious ones (e.g., spreading misinformation or manipulating online polls).
  4. Bridging the Digital Divide: The collective effort to close the gap between those with access to and skills in using digital technology, and those without. This includes providing access to devices, internet connectivity, and media literacy education.
  5. Bait-and-Switch Tactics: A form of deceptive advertising or communication where a user is enticed by one thing (the "bait") and then offered something different (the "switch"). AI can be used to make these tactics more effective by personalizing the lures.
  6. Behavioral Targeting: The use of AI and data analytics to track a user's online behavior, such as browsing history and search queries, in order to deliver highly personalized and targeted advertisements.
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C

  1. Computational Thinking: A problem-solving process that includes skills like decomposition (breaking down a problem), pattern recognition, abstraction, and algorithms. These are fundamental for understanding how AI systems are designed and how they function.
  2. Content Moderation: The practice of monitoring and filtering user-generated content to ensure it complies with a set of rules or policies. This process is increasingly aided by AI, which can automatically identify and flag potentially harmful content.
  3. Context Collapse: A phenomenon in online communication where a person's audience is made up of different social groups, leading to a loss of the specific context that would normally define social interaction. This can be exacerbated by AI-driven platforms that blend different social circles.
  4. Critical Thinking: The analysis of facts to form a judgment. This skill is paramount for evaluating the credibility and accuracy of media, especially in an age where AI can generate and spread misinformation at an unprecedented scale.
  5. Cyberbullying: The use of electronic communication to bully a person, a behavior that can be amplified or aided by AI-driven platforms that facilitate rapid, anonymous, and widespread communication.
  6. Copyright and AI: The legal debate surrounding the ownership and rights associated with content created by AI. Key questions include whether an AI can own a copyright, and whether the data used to train an AI can be considered a copyright infringement.
  7. Curation Algorithms: AI-powered systems that select, organize, and present content to users based on their past behavior, preferences, and social network. These algorithms heavily influence what media we see and consume.
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D

  1. Data Literacy: The ability to read, understand, create, and communicate data as information. This is crucial for understanding how AI is trained on data, how it processes information, and how it influences media and society.
  2. Data Privacy: The right of individuals to control the collection, use, and sharing of their personal information. This is a significant concern as AI systems often require vast amounts of personal data to function effectively.
  3. Deepfakes: Synthetic media in which a person in an existing image or video is replaced with someone else's likeness using AI. Deepfakes pose a major challenge to media authenticity and can be used to create highly convincing but entirely false content.
  4. Digital Citizenship: The norms of appropriate, responsible, and ethical behavior with regard to technology use. This includes understanding and navigating the ethical challenges presented by AI in media.
  5. Digital Divide: The economic and social gap between those with access to digital technology and the skills to use it, and those without. The increasing integration of AI into media can exacerbate this divide if not addressed.
  6. Digital Footprint: The trail of data you leave behind from your online activity. This data is collected and analyzed by AI systems to create personalized content, targeted advertising, and user profiles.
  7. Disinformation: False information that is deliberately created and spread to deceive or manipulate people. AI tools can be used to mass-produce and disseminate disinformation more effectively than ever before.
  8. Disruption of Labor: The potential for AI to automate tasks traditionally performed by humans, leading to job displacement and a need for new skills, particularly in media and creative industries.
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E

  1. Echo Chamber: A situation in which beliefs are amplified or reinforced by communication and repetition inside a closed system. AI algorithms can create or strengthen echo chambers by serving users content that aligns with their existing views.
  2. Ethical AI: The design, development, and use of AI systems in a way that aligns with human values and moral principles. This includes considerations of fairness, privacy, safety, and accountability.
  3. Explainable AI (XAI): The field of AI research focused on developing methods and techniques that make the behavior and decisions of AI systems understandable to humans. XAI is crucial for building trust and accountability.
  4. Emotional Manipulation: The use of psychological tactics to influence a person's feelings or behavior. AI can be used to create highly effective emotionally manipulative content by analyzing user data and tailoring messages to trigger specific emotional responses.
  5. Empowerment: The process of becoming stronger and more confident, especially in controlling one's life and claiming one's rights. Media literacy with AI empowers individuals to navigate and critically engage with an increasingly complex digital landscape.
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F

  1. Fact-Checking: The process of verifying factual information, a skill that is increasingly important for users to combat AI-generated misinformation and disinformation.
  2. Filter Bubble: A state of intellectual isolation that can result from a website's personalized content, which algorithmically filters out information that disagrees with a user's beliefs, leading to a lack of diverse perspectives.
  3. Fairness in AI: The principle that AI systems should treat all individuals and groups equitably, without prejudice or bias. This is a core component of ethical AI development.
  4. Framing: The way in which a story or issue is presented in the media, which can influence how people perceive it. AI can be used to identify and optimize the most effective framing for a particular message.
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G

  1. Generative AI: A type of AI that can generate new and original content, such as text, images, music, and video, based on the data it was trained on. Examples include GPT-4, DALL-E, and Midjourney.
  2. Generative Pre-trained Transformer (GPT): A family of large language models developed by OpenAI that have become a cornerstone of generative AI. These models are capable of producing human-like text for a wide range of applications.
  3. Global Digital Citizenship: The concept of being a responsible and engaged digital citizen on a global scale, recognizing the international implications of AI and media, and understanding diverse cultural perspectives.
  4. Gatekeeping: The process through which information is filtered for dissemination, whether by journalists, editors, or algorithms. AI is increasingly taking on the role of gatekeeper, influencing which information reaches the public.
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H

  1. Hallucination (AI): A term for when a generative AI model produces false, nonsensical, or factually incorrect information while presenting it as fact. This is a common issue with large language models and a major challenge for media accuracy.
  2. Human-in-the-Loop: A concept in AI where a human must intervene and make a decision or provide input to an AI system. This model ensures human oversight, accountability, and ethical considerations.
  3. Hate Speech: Language that attacks or demeans a group based on attributes like race, religion, ethnic origin, sexual orientation, disability, or gender. AI tools are being developed to automatically detect and remove hate speech online.
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I

  1. Information Overload: The state of being exposed to too much information, making it difficult to process and evaluate. The sheer volume of content, much of it AI-generated, available online exacerbates this problem.
  2. Intellectual Property: The legal rights that protect creations of the mind, such as inventions, literary and artistic works, and designs. The use of AI to create content and the training of AI on existing works have led to significant legal and ethical debates around intellectual property.
  3. Internet of Things (IoT): The network of physical devices that are embedded with sensors, software, and other technologies to connect and exchange data. This vast network of data is often used to train and power AI systems.
  4. Information Cascades: A phenomenon where people make decisions sequentially, observing the choices of those ahead of them, which can lead to a group conforming to a sub-optimal choice. AI can accelerate these cascades online.
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J

  1. Journalism and AI: The intersection of journalism and artificial intelligence, where AI is used for tasks like data analysis, content generation, and news gathering. This raises questions about the future of the profession and the role of human journalists.
  2. Junk Science: Information presented as scientifically valid but lacking a basis in the scientific method. AI can be used to easily generate realistic-looking articles and reports that promote junk science.
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K

  1. Knowledge Gap: A theory that states that as the flow of information to a social system increases, segments of the population with higher socioeconomic status tend to acquire this information at a faster rate than those with lower socioeconomic status. AI can potentially widen this gap if access and literacy are not equitable.
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L

  1. Large Language Model (LLM): A type of AI algorithm that uses deep learning techniques and massive datasets to understand, summarize, and generate human-like text. LLMs are the foundation of many popular AI applications, such as ChatGPT.
  2. Linguistic AI: AI systems designed to process, understand, and generate human language. This field includes applications like chatbots, translation services, and natural language processing.
  3. Loss of Authorship: The blurring of the line between human and AI creation, making it difficult to determine who or what is the original author of a piece of media.
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M

  1. Machine Learning: A subfield of AI where computer systems "learn" from data without being explicitly programmed. It is the foundation of many AI tools used in media, from recommendation systems to content generation.
  2. Media Ethics: The moral principles and values that guide the professional conduct of media practitioners. This field is now grappling with the ethical dilemmas posed by AI, such as the spread of misinformation and the use of deepfakes.
  3. Media Literacy: The ability to access, analyze, evaluate, and create media in a variety of forms. With the rise of AI, this skill set has expanded to include an understanding of how algorithms influence content and how to identify AI-generated media.
  4. Misinformation: False or inaccurate information that is spread, regardless of intent to deceive. AI tools can rapidly and widely disseminate misinformation, making it a significant challenge for public discourse.
  5. Multi-modal AI: AI systems that can process and generate content across different modalities, such as text, images, and audio. This enables the creation of complex and realistic synthetic media.
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N

  1. Natural Language Processing (NLP): A branch of AI that enables computers to understand, interpret, and manipulate human language. NLP is a core component of large language models and is used in a wide range of applications, from chatbots to sentiment analysis.
  2. Netiquette: The rules of polite and appropriate behavior when using the internet. These rules are increasingly important when interacting with AI bots and recognizing when an online interaction is not with a human.
  3. Neural Networks: A series of algorithms that mimic the structure and function of the human brain. Neural networks are the foundation of modern AI and are used to recognize patterns and solve complex problems.
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O

  1. Open Source AI: AI models and tools whose code is made publicly available for anyone to use, modify, and distribute. This promotes collaboration, transparency, and can accelerate the development of new applications.
  2. Online Safety: The practice of protecting oneself and one's data while using the internet. AI can be used to both enhance online safety (e.g., through fraud detection) and to pose new threats (e.g., sophisticated phishing scams).
  3. Oversight: The role of human supervision and control over AI systems. Effective oversight is essential to prevent misuse, ensure accountability, and correct for errors or biases in AI.
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P

  1. Personalized Content: Media and information tailored to an individual user's preferences, interests, and past behavior. This is a key function of AI algorithms used by social media platforms and news sites.
  2. Phishing: The fraudulent practice of sending emails or creating websites to trick individuals into revealing personal information. AI can be used to make these scams more sophisticated and difficult to detect.
  3. Propaganda: Information, especially of a biased or misleading nature, used to promote a particular political cause or point of view. AI can be used to mass-produce and disseminate propaganda more effectively than traditional methods.
  4. Prompt Engineering: The art and science of crafting effective prompts or instructions for AI models, particularly generative AI, to achieve a desired output. This is a new and emerging media literacy skill.
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Q

  1. Qualitative Data Analysis: The process of interpreting non-numerical data like text, audio, and video to identify patterns and themes. AI tools are increasingly used to assist in this process, making it faster and more scalable.
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R

  1. Reality Check: The process of examining a piece of media to determine its truthfulness and credibility. This is a vital skill in an age of AI-generated content and misinformation.
  2. Responsible AI: The development and deployment of AI in a safe, ethical, and responsible manner, with a focus on accountability, transparency, and societal impact.
  3. Recommendation System: An AI algorithm that suggests content to users based on their past behavior, preferences, and social network. These systems have a profound impact on media consumption and can lead to filter bubbles.
  4. Reputational Damage: Harm to a person's or organization's reputation. Deepfakes and AI-generated disinformation can be used to cause significant reputational damage.
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S

  1. Sentiment Analysis: The use of AI to determine the emotional tone or sentiment behind a piece of text or media. This tool is used in market research, social media monitoring, and public relations.
  2. Societal Impact of AI: The broad effects that AI has on society, including changes to the economy, social structures, political processes, and individual well-being.
  3. Source Evaluation: The process of critically assessing the credibility and reliability of a source of information. This skill is more complex with the rise of AI-generated content and can no longer rely solely on traditional indicators of a source's legitimacy.
  4. Synthetic Media: Any form of media that is created or altered using AI, including deepfakes, AI-generated images, and text. The rise of synthetic media challenges the very notion of what is real and what is fake.
  5. Surveillance Capitalism: An economic system where human experience is commodified and turned into behavioral data for commercial purposes. AI is the engine that drives this system, collecting and analyzing vast amounts of user data.
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T

  1. Targeted Advertising: A form of advertising that uses consumer data and AI to deliver ads to specific individuals based on their interests and behaviors. This practice raises significant data privacy and ethical concerns.
  2. Technological Singularity: A hypothetical future point in time at which technological growth becomes uncontrollable and irreversible, resulting in unfathomable changes to human civilization. It is a concept that is often discussed in the context of the long-term impact of AI.
  3. Troll Farms: Coordinated groups of individuals who use fake accounts, often with the help of AI bots, to spread misinformation, manipulate online discourse, and sow discord.
  4. Trust and AI: The level of confidence users have in AI systems. Trust is a key factor in the adoption and use of AI, and it is built through transparency, accountability, and ethical design.
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U

  1. Unsupervised Learning: A type of machine learning where an AI system learns to find patterns and relationships in a dataset without human supervision or labeled data. This is often used for tasks like clustering and anomaly detection.
  2. User-Generated Content (UGC): Any form of content, such as videos, blogs, and images, created by users and made publicly available. The line between UGC and AI-generated content is becoming increasingly blurred.
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V

  1. Validation: The process of confirming that something is accurate, true, or credible. This is a critical step in media literacy, especially when faced with AI-generated information that may appear convincing but is entirely false.
  2. Virtual Reality (VR): A computer-generated simulation of a three-dimensional environment with which a person can interact. AI is used to create more realistic and responsive VR experiences.
  3. Virality: The tendency of an image, video, or piece of information to be circulated rapidly and widely from one user to another on the internet. AI algorithms can be designed to promote the virality of certain content.
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W

  1. Watermarking (Digital): The process of embedding information into a digital medium to identify its creator, source, or to prove its authenticity. This technology is being explored as a way to identify AI-generated content.
  2. Weaponization of AI: The use of AI for malicious purposes, such as generating propaganda, conducting cyber-attacks, or creating autonomous weapons.
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X

  1. Xenobots: Tiny living robots, a new form of technology that combines biology and AI. While not directly media-related, they represent the broader ethical considerations of AI's reach into new and emerging fields.
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Y

  1. YouTube's Algorithm: The AI-powered recommendation system used by YouTube to suggest videos to users. Its design heavily influences what content becomes popular and has been criticized for promoting filter bubbles and misinformation.
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Z

  1. Zero-Day Vulnerability: A newly discovered software flaw that has not yet been patched. AI can be used to both find and exploit these vulnerabilities, posing a significant challenge to cybersecurity.
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