We often talk about the “online environment.” This metaphorical language makes it seem like the online space looks similar to our offline world. For example, the term “information pollution,” coined by Claire Wardle, is increasingly being used to discuss disinformation online.
A New Blueprint for Platform Governance | Centre for International Governance Innovation - 0 views
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It is even harder to prove direct connections between online platforms and offline harms. This is partly because platforms are not transparent.
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Finally, this analogy reminds us that both problems are dispiritingly hard to solve. Two scholars, Whitney Phillips and Ryan Milner, have suggested that our online information problems are ecosystemic, similar to the climate crisis.
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Amazon counterfeit problem stems from prioritizing selection - The Washington Post - 0 views
Amazon's supplement responsibility - 0 views
Is the Era of "Permissionless Innovation" and Avoidance of Regulation on the Internet F... - 0 views
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avoidance of regulation that the Silicon Valley platforms
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It hasn’t been a great couple of weeks for the “Don’t Be Evil” company.
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The Supreme Court had upheld a lower court ruling requiring Google to delist from its global search results references to a rogue Canadian company that is the subject of an injunction in British Columbia (B.C) f
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The Next Wave of Platform Governance - Centre for International Governance Innovation - 0 views
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he shift from product- and service-based to platform-based business creates a new set of platform governance implications — especially when these businesses rely upon shared infrastructure from a small, powerful group of technology providers (Figure 1).
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The industries in which AI is deployed, and the primary use cases it serves, will naturally determine the types and degrees of risk, from health and physical safety to discrimination and human-rights violations. Just as disinformation and hate speech are known risks of social media platforms, fatal accidents are a known risk of automobiles and heavy machinery, whether they are operated by people or by machines. Bias and discrimination are potential risks of any automated system, but they are amplified and pronounced in technologies that learn, whether autonomously or by training, from existing data.
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Business Model-Specific Implications
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