Amazing cartoon credit: andertoons.com

Trending Topics are supposed to reflect audience attention in social networks. The inner workings of this process is where the fundamental controversy of the Facebook story lies. At this time, what’s super important is that both sides involved in surfacing news for millions should talk to each other: journalists /editors— who decide what to report and engineers/scientists — who build trending algorithms.

But to begin a conversation, each side must acknowledge the priorities and constraints of the other. I blended this into critical 8 points.

Digital Media has evolved into a ❤ ❤ ❤ system

Lets begin with …

[1] Where do Trends come from ?

Facebook algorithms uses social data to calculate what’s trending. This list of organic trends is collected from Facebook chatter and RSS feeds. The organic trending set is completely powered by algorithms and is only visible in an internal FB editorial panel . Editors use this panel/tool to curate and review the trending topics.

These organic trends is a superset of what you users see on the website (lets call the latter VISIBLE trending topics, VTT). I’ve studied Facebook’s organic trending topics before, and its very clear that VTT is a much smaller set of “trending topics” with its own unique properties.

One crucial point to note here is that contrary to many media reports — editors cannot randomly inject any topic they want. Editors can only “inject” topics which have already been detected in the organic data and therefore, already appears as an organic trend either in the demo or the review tool.

→ which brings us to…

[2] Random Data Sources are unbiased but boring!

Unless a data generating source is controlled by a random process, the data you capture is biased. Random sources can be unbiased, but such sources are no fun to study because there won’t be any patterns. Hence, we are stuck with using non-random data streams. Most of our social interaction points: friends, family and society in general isn’t a random data source. And neither is our social actions in the digital or physical world.

→ which brings us to …

[3] User interests are biased. And Algorithms feed of it.

Crowd interests shared on Facebook aren’t random. What people share is determined by their interests (even if aspirational). Hence, the algorithm that calculates trending topics based on shares will have a biased output because the input is biased. Why are there 8 trending topics about cats and celeb break-ups vs. just 1 trending election story. Thats why! The algorithm ends up collecting an uneven distribution of trends. And when news surfaces unevenly, it can have significant impact on public attention.

→ which brings us to …

[4] Algorithms can’t detect Bias.

Part 1— Many among us realize the need for algorithms because humans can’t parse and analyze the entire feed at the insane incoming rate. But many are still fuzzy about why algorithms can’t select trends fairly?

There are 2 main reasons algorithms can’t filter out bias (at least not yet). First, context is something humans learn over more than 20 years of training and from being domain-literate. (The classic corollary is that if you aren’t media literate, you’d have no idea outlets can be biased). No algorithm has had that training in knowledge transfer. Tech is still figuring out episodic context in consecutive chat bot messages, so we are quite far from automated ethical context analysis at present.

Part 2 — Second, the reason Facebook’s algorithm doesn’t want to show an balanced distribution of trends is because Facebook needs to engage its users — in other words, show users stuff they will click on. (Read Ben Thompson’s excellent post on this). Not to forget, this also means traffic for (certain) media outlets. In other words, the engagement incentive looks like: don’t show political stories to users who click, like and share only entertainment and sports links. This means in order to personalize trends that drive engagement, an uneven distribution of topics has to be served…

→ which brings us to …

[5] Does Engagement require Bias?

Yes. Engagement demands personalization which by definition encodes bias. Engagement incentives for for-profits discourages unbiased delivery of news. Now you could say, Facebook needs to be more ethical and care about engagement less (it seems to be falling behind Twitter in engagement), in the face of fairness and partisanship.

But that might come across as slightly pretentious to some, because media outlets play this exact tune to explain coverage. During the Paris and Beirut attacks, one of the biggest reasons media offered for covering Paris news more than Beirut is that readers engage with Paris news much more than Beirut. I analyzed the media coverage for Paris vs. Beirut as well. They were right. Social media attention does drive coverage. People read the news in a biased fashion- and the news production just mirrored it.

In the current scenario, Facebook is playing exactly the same tune — people won’t engage if you give the evenly distributed non-personalized news. Their claim: a billion users is too diverse a set. The justification does not sound crazy: if media is allowed to favor news topics that readers will read, why shouldn’t Facebook’s trending algorithm select topics that most people on the platform engage with?

Amazing cartoon credit: www.andertoons.com

Big dilemma here: readers want personalized news they can engage with, yet who must shoulder the responsibility of informing people in an unbiased fashion…

→ which brings us to …

[6] Enter Editors as Trained Bias Killers.

The popular consensus is that the human touch — the final beating heart in this loop is indispensable because it makes results fair and presentable. One of the reasons for having editorial supervision is to control bias. And that’s exactly what Facebook effectuated, after considerable criticism that the ice-bucket challenge appeared on more Facebook news feeds than the Ferguson protests.

But as the Gizmodo article which started this entire controversy claims, editors can also introduce bias. So what should the consumer believe? Journalists have training in ethics and exposing biases we overlook. Engineers have training in code and realize that personalized news is the only tractable way to parse the stream and offer to a user.

which brings us to …

[7] What Media Wants.

Media demands for algorithmic systems fall into two categories: (1) Transparency and (2) Accountability. Unfortunately, neither is trivial to scale.

(1) TRANSPARENCY at the qualitative level is obviously manageable. Moreover, some claim publishers already know enough about Facebook’s priorities. However, demanding transparency at the code level from enormously complex algorithms like ranked feeds is like asking to scoop into the mind of every reporter that works in an organization along with their hierarchical structures using some magical device. Even if someone manages to cast that spell, an actionable solution is probably intractable and the accuracy impossible to judge due to several dynamic factors in such systems.

Perhaps more importantly, many algorithms are designed to maneuver in accordance to global and user data. So the output of the algorithm, its behavior at one point of time when the transparency test was performed will never be similar to its behavior at another time in the future. The only way to coerce similar output is by making the ill assumption that platforms generate uniform data distributions at different points of time — which is unrealistic.

(2) Next, lets talk ACCOUNTABILITY. Here, I think Facebook could do some real work. And they are trying. But here too, things are not as easy as it sounds. Appeals that Facebook should be accountable makes complete sense, but I think it also misses a critical aspect. Accountability desires that discretionary power be exercised responsibly. It stipulates that companies be held liable for comparative negligence. But liability of ranked feed results (like Google search or news feeds) has already been dismissed in the US court of law!

What! — Why? Since the algorithm is written by engineers, who are human — it counts as freedom of speech. Also, since the data source it uses to produce the feed is generated by humans — again, freedom of speech. Somehow many are completely oblivious to this ruling.

So what are journalists, consumers and coders supposed to do? Whom should they look to to solve the problem.

I‘d rather have them all.

which FINALLY brings us to …

[8] The Objective of the Octopus

The interplay between the three entities — Editors, Crowds, and Algorithms has produced an unique phase of evolution in the information society. However, all three must come to terms with the some stark realities. First, scientists who develop algorithms must realize that finding the best-fit model for the input data might not always serve the greater good. If the input data is biased, so is the model and the output. Can we develop computational methods to detect bias automatically?

Next, Editors and the Media must realize that many outlets produce easily consumable soft news that is hard to resists for the average Facebook user— which makes the algorithm sway and serve the wishes of the masses. Human expertise, even as a group, isn’t without bias. Finally, Crowds must realize that to ensure they don’t miss out on critical news — they must care to click, read and share such news. So as far as bias is concerned,

The objective of the Octopus was never meant to be objective. It was only meant to pledge a sensor for all voices coming from all corners of the information society.

I don’t know why octopuses evolved to require three hearts. Maybe that’s how evolution works — it was necessary. What happens if one heart stops working? Does it cause the ecosystem to collapse? Natural evolution must have chosen it because there was a clear biological advantage.

The current phase of digital media’s evolution in our information society perhaps necessitates all three — Editors, Crowds and Algorithms. There must be some societal or information advantage to this evolutionary phase; the full story could be yet to be revealed. Lets try to unravel those advantages instead of discounting it.