When AI Learns From Us, What Does It Really Learn?
As artificial intelligence learns quickly from people and the data they leave behind, the quality of how we teach AI will determine how well it can learn. Every post, every photo, every interaction helps AI develop its “brain” (knowledge). However, the fundamental question that arises is whether we’re feeding AI inaccurate, biased, or otherwise low-quality content unintentionally.
When AI learns from poor-quality material, it makes mistakes, spreads falsehoods, and ultimately affects how quality content is developed on all social media platforms. To summarize: How AI learns from us today will make up the systems that we use in the future.
The Learning Process Behind AI
AI functions through machine learning algorithms. It learns through completing tasks, recognizing patterns, and enhancing its skills related to making better predictions in the coming days and years. The data produced by humans acts as a basis for this happening. Posts, articles, pictures, and videos, along with all user-generated content, contribute to this aspect of AI.
The quality and amount of such an input directly determine the way in which the AI system would learn. If the AI system were exposed to variant and accurate information from various sources, the system would develop better models and would take informed and unbiased decisions. If the developers were using the system with limited, inaccurate, and biased information, the system would develop poor models and would give inaccurate predictions and outputs.
When Bad Data Becomes AI’s Lesson
The use of “garbage” Data can harm both the technology (AI) and the end user by introducing biased information, bad-quality content, spamming, or misinformation. Using “garbage” Data in AI training can lead to:
- Poor predictions by AIs, generating and/or providing inaccurate recommendations, or rendering inaccurate content for its users.
- Cyclical bias in predictions occurs when prejudices from data repeat through time and hence continue to create similar outcomes/stimuli for members of society.
- The proliferation of misinformation through AI, whereby the speed of misinformation being circulated exceeds any previous means of circulation, and in addition, enables an even larger audience or community to adopt it as truth.
Examples of bias in recommendations would be generating a biased recommendation or suppressing an alternative view altogether, creating an echo chamber. An additional example would be the reprinting of errors contained within training data, enabling a multiplication effect on the errors.
Research into AI-generated content flooding platforms like YouTube shows how low-quality outputs can distort recommendations and user trust, as highlighted in this detailed study on AI content slop.
Why It Matters: AI’s Societal Impact
Businesses, researchers, and average consumers are all affected by poor-quality artificial intelligence that lives outside of the lab. Poor quality or biased artificial intelligence can impact recruitment software and financial decision-making and provide misinformation in journalism.
Recommendation systems, content creation tools, and algorithms that help with making decisions all depend on AI learning. Flawed data erodes trust, distorts culture, and spreads misinformation, weakening confidence in systems across society.
Platforms like YouTube highlight how unchecked AI slop can distort information quality and influence public perception.
Safeguarding AI: Ethics, Policy, and Responsibility
We must assume accountability for the process through which AI learns to be successful. To successfully execute our responsibilities for the AI learning process, we must take key steps:
- Labels for Generated AI Content: Platforms must label AI-generated content to protect datasets used in artificial intelligence training.
- Containment of Defective AI Amplification: Platforms must prevent the spread of low-quality AI content generated on their systems.
- Protect Cultural Source and Value Integrity: Governments, businesses, and researchers must preserve cultural authenticity and informational integrity across digital ecosystems.
Teaching AI Wisely: Our Responsibility Today, Tomorrow’s AI Tomorrow
Humans are teaching artificial intelligence daily. Every single piece of material we create affects the way AI generates and develops information and products going forward. To build trustworthy systems that deliver effective solutions, humans must provide AI with high-quality and unbiased data.
Using ethical data frameworks, careful curation, and responsible publishing helps humans teach AI responsibly and effectively. What we teach AI today builds the foundation that AI relies on and develops from tomorrow.
