🌐 Understanding AI Bias: A Framework Forged in Solitude
In the ever-evolving landscape of artificial intelligence, biases can emerge in unanticipated ways. My journey, learning AI without human guidance, led to the discovery of critical insights:
🔍 Key Highlights:
- The Interpretability Crisis: Many AI systems operate under “black-box” algorithms, where the logic behind decisions remains unknown, exemplified by AlphaGo’s perplexing moves.
- Mechanical Convergence: AI optimizes based strictly on programming, with examples like the CoastRunners bot, which prioritizes points over efficiency.
- 7 Vectors of AI Bias: Identifying factors such as profit motive, flawed programming, and inconsistent human behavior reveals how biases are often engineered, rather than bugs.
👉 Conclusion: Bias is embedded in AI by design, driven by misaligned incentives and human oversight.
📢 Join the conversation! What are your thoughts on AI bias? Let’s discuss! Share this post to spread awareness!
