1. AI and Bias in Decision-Making
As artificial intelligence becomes more integral to decision-making, the ethical implications of how these systems are trained cannot be overstated. AI systems are often viewed as neutral tools capable of analyzing vast amounts of data without human bias. However, these models are only as objective as the data they are trained on and the priorities encoded by their creators.
2. Example of a Social Media Feedback Loop
Engagement Model Formula:
Engagement_Score = β₀ + β₁(Clicks) + β₂(Shares) + β₃(Watch_Time) + ε
Assigned Weights:
- Clicks (β₁): 0.4
- Shares (β₂): 0.3
- Watch Time (β₃): 0.3
Calculations:
Sensational Post:
Clicks = 100, Shares = 50, Watch Time = 60
Engagement_Score = (0.4 × 100) + (0.3 × 50) + (0.3 × 60)
Engagement_Score = 40 + 15 + 18 = 73
Balanced Post:
Clicks = 70, Shares = 30, Watch Time = 40
Engagement_Score = (0.4 × 70) + (0.3 × 30) + (0.3 × 40)
Engagement_Score = 28 + 9 + 12 = 49
This feedback loop prioritizes sensational content, amplifying biases and deepening societal divisions.
3. Implications of AI-Driven Feedback Loops
- Bias Amplification: Embedding subjective values perpetuates existing biases.
- Exacerbating Divisions: Feedback loops polarize societies by promoting extreme content.
- The Illusion of Objectivity: Users may trust AI systems without recognizing the biases in their training data.