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>I'm not comfortable with any given group of people doing such sort of approval Historically, that's exactly what newspaper editors did. The algorithmic appro

>I'm not comfortable with any given group of people doing such sort of approval Historically, that's exactly what newspaper editors did. The algorithmic approach has the potential to offer greater transparency, but not if the algorithm isn't published. Furthermore, now that Facebook is in a near-monopolistic position when it comes to picking winners and losers in what we read, acknowledging that fact publicly would be the bare-minimum first step they could take towards accountability. Then we can have a more open discussion about how news stories ought to be judged and propagated (or not).

yummyfajitas 10y ago
The algorithmic approach has the potential to offer greater transparency, but not if the algorithm isn't published. This isn't really true. There's actually a mathematical discipline devoted to treating statistical algorithms as black boxes, and assessing their accuracy/generalizability based solely on test data/broad intrinsic properties of the algorithm (e.g. continuity parameters). It's called "machine learning". From what I can tell, the fundamental distinction between machine learning and statistics is that machine learning provides mathematical guarantees based solely on black box testing of algorithms and very broad properties of the distribution/algo. In contrast, statistics cares about the actual underlying process.
- superbatfish 10y ago
  Great points. It's true that whatever algorithm they end up with will definitely use ML. And it's also true that "interpretability" is one of the great challenges for ML in the coming years. But interpretability of a model isn't a binary attribute. Some techniques offer at least some intuition about why they chose their results. But a human editor, on the other hand, is much less transparent -- basically, the best justification they can offer is "trust me, I know what I'm doing".

yummyfajitas 10y ago

The algorithmic approach has the potential to offer greater transparency, but not if the algorithm isn't published. This isn't really true. There's actually a mathematical discipline devoted to treating statistical algorithms as black boxes, and assessing their accuracy/generalizability based solely on test data/broad intrinsic properties of the algorithm (e.g. continuity parameters). It's called "machine learning". From what I can tell, the fundamental distinction between machine learning and statistics is that machine learning provides mathematical guarantees based solely on black box testing of algorithms and very broad properties of the distribution/algo. In contrast, statistics cares about the actual underlying process.

superbatfish 10y ago
Great points. It's true that whatever algorithm they end up with will definitely use ML. And it's also true that "interpretability" is one of the great challenges for ML in the coming years. But interpretability of a model isn't a binary attribute. Some techniques offer at least some intuition about why they chose their results. But a human editor, on the other hand, is much less transparent -- basically, the best justification they can offer is "trust me, I know what I'm doing".