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This sounds pretty biased itself, first of all: we would need to compare the reported news to "neutral" facts, if the Hamas actually did initiated let's say 20%
by Zealotux 5y ago
This sounds pretty biased itself, first of all: we would need to compare the reported news to "neutral" facts, if the Hamas actually did initiated let's say 20% more violence (illustrative figure) than Israel then it would make sense that roughly 20% reports were made to report such events. ML in this case is just automated bias, you can't look at real world in absolute, a better thing (maybe) would be to compare the NYT's reports to other new outlets.
- md_ 5y agoObviously this depends a lot on how you define the metric. It seems to me (superficially) that "like-for-like" metrics such as "use of passive voice" should be fairly independent of reality; for a similar example, see the American tendency to refer to "officer-involved shootings" (but, in a clear case of bias, to never say "dog-involved bitings"). On the other hand, as you note, "use of violent terms" could be a reflection of real-world differences in violent actions, so it might be a more fraught metric. I'm really intrigued by the idea that we can bring this kind of quantitative analysis to bear to reveal hidden bias, though; it sounds like it can be a powerful approach. But you're certainly right about potential pitfalls.
- desertedisland 5y agoIt's genuinely surprising to me that a researcher at MIT lacks the awareness to attempt at least the pretence of impartiality in a paper about bias.