# kaal:claim:4796714-035

**Claim.** AI learning is degraded by Web2 platforms because their engagement driven algorithms amplify extreme viewpoints and negativity, so the human sentiment and ethics the models absorb from that data are systematically distorted.

**Type.** mechanism  **Support.** argued

**Holds when.**

- applies to models trained on unfiltered web and social media data
- arises from engagement optimization in Web2 platforms

**Source quote.**

> AI learning is afflicted by web2 systems that bring out suboptimal human generated outcomes. WEB2 platforms often amplify extreme viewpoints and negativity due to their engagement-driven algorithms, presenting a distorted view of human sentiment and ethics.

**From.** Wulf A. Kaal, *AI Governance* (2024), Avoiding WEB2 Inadvertent AI Learning Mistakes through WDAG AI Learning, page 46

**Cite as.** Wulf A. Kaal, AI Governance (2024). SSRN: https://ssrn.com/abstract=4796714

**Verify.** sha256 of source PDF `59fa63bae179e8f9b6b8efbdf90cee28400276512a1b04f9f579a48641305c93` at https://raw.githubusercontent.com/wulfkaal/Academic-Papers/main/papers/pdf/Kaal%20-%202024%20-%20AI%20Governance.pdf

**Failure mode.** engagement driven data distortion  (family: ai-model-and-training-failure)

**Topics.** ai-and-agents, education-and-practice

**Keywords.** web2, engagement-algorithms, training-data, value-alignment, ai-ethics

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