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Actually the guardian headline is perfectly accurate. It completely is accurate to say the algorithm has a bias, even if that bias isn't "if (right-wing) then amplify()"

> If you search for the word “bias” in Twitter’s post you’ll not find support for The Guardian’s editorialization

But if you search for "favour" you'll find "[our findings revealed that] algorithmic amplification favours right-leaning news source". In the abstract. Which means the exact same thing.

> Here’s an important excerpt, which notes that more analysis is needed to understand whether the amplification is unnatural relative to user interactions

The guardian article includes your quote so I don't think you can accuse it of omitting important context.

> Twitter’s internal research is saying that certain tweets are organically more popular and engaging and more likely to be shown

I'm not sure where you're reading this from. It is pretty explicit that further research is required in to the cause of the discrepancy.

> which notes that more analysis is needed to understand whether the amplification is unnatural relative to user interactions

Whether it’s natural or not is irrelevant IMHO.

If a post fanning the flames of a religious war or ethnic cleansing is “organically popular” should the algorithm amplified it?

It should also be noted that just because something is popular doesn’t mean it’s a factually correct piece of information or a sensible course of action.

Sometimes lies and bad ideas are more (emotionally) appealing and eye catching but that doesn’t mean one should propagate them.

Reddit and HN are guilty of this too given how their vote based system works.

“Sometimes lies and bad ideas are more (emotionally) appealing and eye catching but that doesn’t mean one should propagate them.”

How does an algorithm determine what is a lie or bad idea? Please share.

> How does an algorithm determine what is a lie or bad idea? Please share.

I don't know. Perhaps there will never be an algorithm that can do it even "approximately" never mind accurately - OK maybe "never" is too strong a word.

It doesn't change the fact that the current way we are doing things are causing problems.

In the past we rely on fallible editors to filter the news. It wasn't perfect. However I think they did a better job than the algorithms of Twitter and Facebook - most editors had some common sense with regards to the effects of what they allow to go to print; the more responsible ones will do due diligence and fact check stuff before printing it (as imperfect as their ability to do so is, it's better than the no fact checking at all done by Twitter/Facebook before they amplify it to millions of people).

>Twitter’s internal research is saying that certain tweets are organically more popular and engaging and more likely to be shown, not that the algorithm has an ideological bias

No one is claiming that there's a piece of code that detects a Twitter user being a right-wing politician and then showing it to more users.

Whether the bias is deliberate or not, it's still a bias. Right wing gets their message amplified more than the left.

That's not the definition of bias, in either the colloquial or statistics sense.

A platform promoting one side of the political spectrum over another is not bias? There's no deliberation needed to meet the definition of bias. Every person have unconscious biases, and any structural system, including algorithms that promote content on social media can have unconscious biases as well. Simply stating that it's not bias does not cut it for me.

OK, I admit it's not unequivocally not bias. It could be bias. I would just say there could be other explanations, such as a different ratio between non-politician activists to politicians on either side of the isle and how each slice of the pie tends to monopolize limited attention bandwidth.

If three political tweets by Lebron James are algorithmically boosted, and three political tweets by Ted Cruz are boosted, I wouldn't call that bias necessarily even though the metric being studied did.

Perhaps I didn’t explain my opinion clearly, but I think we might have different definitions for what constitutes bias. You’re suggesting that a difference in outcomes means something is wrong or unfair with the algorithm. But what if it is that right wing users simply engage more, or that the right wing news outlets put out better content, or other such factor? I wouldn’t view the increased algorithmic amplification as problematic if there were such root causes behind it. Put another way, I am taking an equality lens (equality of opportunity) rather than an equity lens (equality of outcomes). If we take an equity lens, then you can start questioning why any number of things don’t get the same amplification - it doesn’t have to stop at a binary of “left versus right”.

How would you enforce, e.g. hate speech, moderation if there are both left and right wing definitions for it?

Or Twitter's content policies could follow the law and reflect the political leaning of the governement, but then how do you enforce moderation on a free/liberal social network which by definition should be free from governement censorship?