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Google and Microsoft’s chatbots are already citing one another in a misinformation shitshow
Microsoft’s AI chatbot Bing incorrectly reported the demise of Google’s AI chatbot Bard. It’s an early warning sign that this technology is fueling a massive game of misinformation telephone.James Vincent (The Verge)
![Screencap: "When we rely on ever larger datasets we risk incurring documentation debt, 18 i.e. putting ourselves in a situation where the datasets are both undocumented and too large to document post hoc. While documentation allows for potential accountability [13, 52, 86], undocumented training data perpetuates harm without recourse. Without documentation, one cannot try to understand training data characteristics in order to mitigate some of these attested issues or even unknown ones. The solution, we propose, is to budget for" and footnote 18 "On the notion of documentation debt as applied to code, rather than data, see [154]."](https://cdn.masto.host/daircommunitysocial/media_attachments/files/110/067/706/634/894/053/original/6c6969c1be15c411.png "Screencap: \"When we rely on ever larger datasets we risk incurring documentation debt, 18 i.e. putting ourselves in a situation where the datasets are both undocumented and too large to document post hoc. While documentation allows for potential accountability [13, 52, 86], undocumented training data perpetuates harm without recourse. Without documentation, one cannot try to understand training data characteristics in order to mitigate some of these attested issues or even unknown ones. The solution, we propose, is to budget for\" and footnote 18 \"On the notion of documentation debt as applied to code, rather than data, see [154].\"")
![Screencap "As a part of careful data collection practices, researchers must adopt frameworks such as [13, 52, 86] to describe the uses for which their models are suited and benchmark evaluations for a variety of conditions. This involves providing thorough documentation on the data used in model building, including the motivations underlying data selection and collection processes. This documentation should reflect and indicate researchers’ goals, values, and motivations in assembling data and creating a given model. It should also make note of potential users and stakeholders, particularly those that stand to be negatively impacted by model errors or misuse. We note that just because a model might have many different applications doesn’t mean that its developers don’t need to consider stakeholders. An exploration of stakeholders for likely use cases can still be informative around potential risks, even when there is no way to guarantee that all use cases can be explored." 2nd-4th sentences highlighted in blue.](https://cdn.masto.host/daircommunitysocial/media_attachments/files/110/067/719/287/396/201/original/8014264a9bfb0028.png "Screencap \"As a part of careful data collection practices, researchers must adopt frameworks such as [13, 52, 86] to describe the uses for which their models are suited and benchmark evaluations for a variety of conditions. This involves providing thorough documentation on the data used in model building, including the motivations underlying data selection and collection processes. This documentation should reflect and indicate researchers’ goals, values, and motivations in assembling data and creating a given model. It should also make note of potential users and stakeholders, particularly those that stand to be negatively impacted by model errors or misuse. We note that just because a model might have many different applications doesn’t mean that its developers don’t need to consider stakeholders. An exploration of stakeholders for likely use cases can still be informative around potential risks, even when there is no way to guarantee that all use cases can be explored.\" 2nd-4th sentences highlighted in blue.")
