I'm sure Vox would like if Facebook were obligated to promote approved opinions, but I'm not comfortable with any given group of people doing such sort of approval, so right now I consider an algorithmic approach natively superior.
That is a point the article makes, though. The algorithmic approach is effectively equivalent to particular group of people doing the approval (or rejection). What group that is manifests itself via the way the algorithms are coded and the data that are fed into it.
Edit: This is similar to the conclusion people are coming to regarding algorithms for court sentencing[0] and other areas where algorithms are becoming increasingly used. It's dangerous to conclude that an algorithm is better just by virtue of it being code. That code (and/or its training data) may be reinforcing existing stereotypes and biases.
[0] https://www.engadget.com/2016/06/26/wisconsin-sentencing-alg...
The conclusion there is simply a theorem: it's mathematically impossible to be both well calibrated (a black and white person receiving the same risk score have the same probability of committing crime) and also racially balanced (similar levels of false positives), except in trivial and unrealistic cases.
https://arxiv.org/pdf/1609.05807v1.pdf
See also this WaPo article explaining in simpler terms: https://www.washingtonpost.com/news/monkey-cage/wp/2016/10/1...
But the thing is, this theorem applies to any decision process - human or algorithm. You can't escape mathematical impossibility results just by using humans (e.g. NP Complete problems are still hard even if done by humans).
Human processes just add additional bias, e.g. the editorial slant that Vox wants Facebook to add, or the (alleged) bias that Facebook's human editors added before Facebook went all algorithmic.
Okay, that may have not been the best example to reference.
> Human processes just add additional bias, e.g. the editorial slant that Vox wants Facebook to add, or the (alleged) bias that Facebook's human editors added before Facebook went all algorithmic.
But how you can craft an algorithm that takes human processes (clickbait, investigative journalism, breaking news, photos of cats) and provides an output free of biases? Given the fluid nature of the input biases, is it even possible to craft an algorithm that accounts for those biases without adding in (or substituting) other biases? What is the "objective standard" by which you measure and correct the input biases? Just because there's an algorithm doesn't mean the output is bias-free.
Bias with respect to what? As you say, there is already bias baked into the data collection and the algorithmic choices.
The bias that human editors introduce is different, but not necessarily larger, however you even measure it. There are also myriad human choices behind the choice and deployment details of the algorithm.
An important plus for human editors is greater interpretability and greater transparency regarding the biases the system ends up showing.
Machines simply don't do this.
As you say, there is already bias baked into the data collection and the algorithmic choices.
That's not what I said.
What I said is that you can't have collective equality (e.g. same rate of false positives, lack of disparate impact) and also accuracy (getting the right answer) except in trivial/unrealistic cases.
Human editors are fundamentally less interpretable and transparent than machines. You can easily interrogate machines and test for bias; how do you do that to humans?
Or, to take a historical example, why did colleges switch from algorithms to humans when the supreme court said that transparent racial bias is forbidden?
It's similar to a hiring process: which is better, having a known procedure and rubric for choosing which candidates to hire, or just allow a manager to go with their gut and choose their nephew? There's a sense in which they are the same--at some level, designing a system must still take into account human values and biases. But that's still a huge step forward from having people, even smart people, make gut decisions just based on what's in their head, even if that's still a decision driven by human values and biases.
I don't see how that follows. The biases of an algorithm or beurocracy aren't inherently superior, they are just inherently repeatable.
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).
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.
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".