2020: A Goldilocks Solution for False Political Ads on Social Media is Emerging

Zuckerberg has rationalized that Facebook should do nothing about lies, and Dorsey has Twitter copping to the other extreme of an indiscriminate ad ban. But a readily actionable Goldilocks solution has emerged in response – and there are reports that Facebook is considering it.*

[This post focuses on stopgap solutions for controversial and urgent concerns leading in to the 2020 election. My prior post, Free Speech, Not Free Targeting! (Using Our Own Data to Manipulate Us), addresses the deeper abuses related to microtargeting and how everything in our feeds is filtered.]

[Update 5/29/20:]
Two bills to limit microtargeting of political ads have been introduced in Congress, one by Rep. Eshoo, and one by Rep. Cicilline.  Both are along the lines of the proposals described here. (More at end.)

The real problem

While dishonest political ads are a problem, that in itself is nothing new that we cannot deal with.  What is new is microtargeting of dishonest ads, and that has created a crisis that puts the fairness of our elections in serous doubt.  Numerous sophisticated observers – including the chair of the Federal Election Commission and the former head of security at Facebook -- have identified a far better stopgap solution than an outright ban on all political ads (or doing nothing).

Since the real problem is microtargeting, the “just right” quick solution is to limit microtargeting (at least until we have better ways to control it).  Microtargeting provides the new and insidious capability for a political campaign to precisely tailor its messaging to microsegments of voters who are vulnerable to being manipulated in one way, and while sending many different, conflicting messages to other microsegments who can be manipulated in other ways – by precision targeting down to designated sets of individual voters (such as with multifacet categories or with Facebook Custom Audiences). The social media feedback cycle can further enlist those manipulated users to be used as conduits ("useful idiots") to amplify that harm throughout their social graphs (much like the familiar screech of audio feedback that is not properly damped). This new kind of message amplification has been weaponized to incite extreme radicalization and even violent action.

We must be clear that there is a right of speech, but only limited rights to amplification or targeting. We have always had political ads that lie. America was founded on the principle that the best counter to lies is not censorship, but truth. Policing lies is a very slippery slope, but when a lie is out in the open, it can be exposed, debunked, and shamed. Sunlight has proven the best disinfectant. With microtargeting there is no exposure to sunlight and shame.

• This new microtargeted filtering service can direct user posts or paid advertising to those most vulnerable to being manipulated, without their informed permission or awareness.
• The social media feedback cycle can further enlist those manipulated users to be used as conduits ("useful idiots") to amplify that harm throughout their social graphs (much like the familiar screech of audio feedback that is not properly damped). 
• These abuses are hidden from others and generally not auditable. That is compounds the harm of lies, since they can be targeted to manipulate factions surreptitiously. 

Consensus for a stopgap solution

In the past week or so, limits on microtargeting have been suggested to take a range of forms, all of which seem workable and feasible:

• Ellen Weintraub, chair of the Federal Election Commission (in the Washington Post), Don’t abolish political ads on social media. Stop microtargeting, suggests “A good rule of thumb could be for Internet advertisers to allow targeting no more specific than one political level below the election at which the ad is directed.
• Alex Stamos, former Facebook security chief, in an interview with Columbia Journalism Review, suggests “There are a lot of ways you can try to regulate this, but I think the simplest is a requirement that the "segment" somebody can hit has a floor. Maybe 10,000 people for a presidential election, 1,000 for a Congressional.”
• Siva Vaidhyanathan, in the NT Times, suggesting "here’s something Congress could do: restrict the targeting of political ads in any medium to the level of the electoral district of the race."
• In my prior post, I suggested “allow ads to only be run...in a way that is no more targeted than traditional media…such as to users within broad geographic areas, or to a single affinity category that is not more precise or personalized than traditional print or TV slotting options.

This seems to be an emerging consensus that this is the best we can expect to achieve in the short run, in time to protect the 2020 election. This is something that Zuckerberg, Dorsey, and others (such as Google) could just decide to do -- or might be pressured to do. NBC News reported yesterday that Facebook is considering such an action.

We should all focus on avoiding foolish debate over naive framing of this problem as a dichotomy of "free speech" versus "censorship." The real problem is not the right of free speech, but the more nuanced issues of limited rights to be heard versus the right not to be targeted in ways that use our personal data against our interests.

The longer term

In the longer term, dishonest political ads are only a part of this new problem of abuse of microtargeting, which applies to speech of all kinds -- paid or not, political or commercial, or not. Especially notable is the fact that much of what Cambridge Analytica did was to get ordinary people to spread lies created by bots posing as ordinary people. To solve these problems, we need to change how the platforms not only how identity is known, but also how content is filtered into our feeds. Filtering content into our feeds is a user service that should be designed to provide the value that users, not advertisers seek. 

There are huge opportunities for innovation here. My prior post explains that, shows how much we are missing because the platforms are now driven by advertiser needs for amplification of their voice, not user needs for filtering of all voices, and it points to how we might change that.


See my prior post for more, plus links to related posts.

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*[Update 11/7:] WSJ reports Google is considering political ad targeting limits as well.
[Update 11/20:] Google has announced it will impose political ad targeting limits -- Zuck, your move.
[Update 11/22:] WSJ reports Facebook is considering similar political ad targeting limits.

The downside of targeting limits. Meanwhile, there are reports, notably in NYTimes, that highlight the downside of limiting targeting precision in this way. That is why it is prudent to view blanket limits not as a final cure, but a stopgap:

• Political campaigns rightly point out how these limits harm legitimate campaign goals: “This change won’t curb disinformation...but it will hinder campaigns and (others) who are already working against the tide against bad actors to reach voters with facts.” “Broad targeting kills fund-raising efficiency”
• That argues that the real solution is to recognize that platforms do have the right and obligation to police ads of all kinds, including paid political ads, in order to enable an appropriate mix of targeting privileges to legitimate campaigns -- when placing non-abusive ads -- to those who choose to receive them.
• But since we are nowhere near a meaningful implementation of such a solution in time for major upcoming elections, we need a stopgap compromise now. That is why I originally advocated this targeting limit, while noting that it was only a stopgap.

[Update 5/29/20:]
Related to the update at the top, about the bills introduced in Congress, a nice statement quoted in the 5/26/20 Eshoo press release explains the problem in a nutshell:

It used to be true that a politician could tell different things to different voters, but journalists would check whether the politician in question was saying different things to different people and write about it if they found conflicting political promises. That is impossible now because the different messages are shown privately on social media, and a given journalist only has his or her own profile. In other words, it's impossible to have oversight. The status quo, in other words, is bad for democracy. This new bill would address this urgent problem. --Cathy O’Neil, author of Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy and CEO of ORCAA, an algorithmic auditing firm.

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[Supplement 11/8:] These 11/5 updates from my prior post seem worth repeating here as added background: 

(Alex Stamos from CJR)

In a 10/28 CJR interview by Mathew Ingram, Talking with former Facebook security chief Alex Stamos, Stamos offers this useful diagram to clarify key elements of Facebook and other social media that are often blurred together. He clarifies the hierarchy of amplification by advertising and recommendation engines (filtering of feeds) at the top, and free expression in various forms of private messaging at the bottom. This shows how the risks of abuse that need control are primarily related to paid targeting and to filtering. Stamos points out that "the type of abuse a lot of people are talking about, political disinformation, is absolutely tied to amplification" and that at the rights of unfettered free expression get stronger at the bottom, "the right of individuals to be exposed to information they have explicitly sought out."

Stamos argues that "Tech platforms should absolutely not fact-check candidates organic (unpaid) speech," but, in support of the kind of targeting limit suggested here, he says "I recommended, along with my partners here at Stanford, for there to be a legal floor on the advertising segment size for ads of a political nature."

Ben Thompson, in Tech and Liberty, supports Stamos' arguments and distinguishes rights of speech from "the right to be heard." He notes that "Targeting... both grants a right to be heard that is something distinct from a right to speech, as well as limits our shared understanding of what there is to debate."

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See the Selected Items tab for more on this theme.

Free Speech, Not Free Targeting! (Using Our Own Data to Manipulate Us)

(Image adapted from The Great Hack movie)

Zuckerberg's recent arguments that Facebook should restrict free expression only in the face of imminent, clear, and egregious harm have generated a storm of discussion -- and a very insightful counter from Dorsey (at Twitter).

But most discussion of these issues misses how social media can be managed without sacrificing our constitutionally protected freedom of expression. It oversimplifies how speech works in social media and misdiagnoses the causes of harm and abuse. 

[Update: A newer 11/6 post focuses on stopgap solutions for controversial and urgent concerns leading in to the 2020 election: 2020: A Goldilocks Solution for False Political Ads on Social Media is Emerging. This post focuses on the broader and deeper abuses of microtargeting, and how everything in our feeds is filtered.]

Much of this debate seems like blind men arguing over how to control an elephant when they don't yet understand what an elephant is. That is compounded by an elephant driver who exploits that confusion to do what he likes. (Is he, too, blind? ...or just motivated not to see the harm his elephant does?)

I suggest some simple principles can lead to more productive solution. Effective regulation -- whether self-regulation by the platforms, or by government -- requires understanding that we are really dealing with a new and powerfully expanded kind of hybrid media -- which is provided by a new and powerfully expanded kind of hybrid platform. That understanding suggests how to find a proper balance that protects free expression without doing great harm.

(This is a preliminary outline that I hope to expand on and refine. In the  meantime, some valuable references are suggested.) 

The essence of the problem

I suggest these three simple principles as overarching:

1. Clearly, we need to protect "free speech," and a "free press," the First Amendment rights that are essential to our democracy and to our "marketplace of ideas." Zuckerberg is right that we need to be vigilant against overreaching cures -- in the form of censorship -- that may be worse than the disease.
2. But he and his opponents both seem to misunderstand the nature of these new platforms. The real problem arises from the new services these platforms enable: precision targeted delivery services are neither protected speech, nor the protected press. They are a new kind of add-on service, separate from speech or the press. 
3. Enabling precision targeted delivery against our interests, based on data extracted from us without informed consent is an abuse of power -- by the platforms -- and by the advertisers who pay them for that microtargeted delivery service. This is not a question of whether our data is private (or even wholly ours) -- it is a question of the legitimate use of data that we have rights in versus uses of that data that we have rights to disallow (both individually and as a society). It is also a question of when manipulative use of targeted ads constitutes deceptive advertising, which is not protected speech, and what constraints should be placed on paid targeting of messages to users. 

By controlling precision targeted delivery of speech, we can limit harmful behavior in the dissemination of speech -- without censorship of that speech.

While finalizing this post, I realized that Renee DiResta made some similar points under the title Free Speech Is Not the Same As Free Reach, her 2018 Wired article that explains this problem using that slightly different but equally pointed turn of phrase. With some helpful background, DiResta observed that:

...in this moment, the conversation we should be having—how can we fix the algorithms?—is instead being co-opted and twisted by politicians and pundits howling about censorship and miscasting content moderation as the demise of free speech online. It would be good to remind them that free speech does not mean free reach. There is no right to algorithmic amplification. In fact, that’s the very problem that needs fixing.

...So what can we do about it? The solution isn’t to outlaw algorithmic ranking or make noise about legislating what results Google can return... 

...there is a trust problem, and a lack of understanding of how rankings and feeds work, and that allows bad-faith politicking to gain traction. The best solution to that is to increase transparency and internet literacy, enabling users to have a better understanding of why they see what they see—and to build these powerful curatorial systems with a sense of responsibility for what they return.

In the following sections, I outline novel suggestions for how to go farther to manage this problem of free reach/free targeting -- in a way that drives the platforms to make their algorithms more controllable by their users, for their users. Notice the semantics: targeting and reach are both done to users -- filtering is done for users.

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Sidebar: The Elements of Social Media

Before continuing -- since even Zuckerberg seems to be confused about the nature of his elephant -- let's review the essential elements of Facebook and other social media.

Posting: This is the simple part. We start with what Facebook calls the Publisher Box that allows you to "write something" to post a Status Update that you wish to be available to others. By itself, that is little more than an easy-to-update personal Web site (a "microblogging" site), that makes short content items available to anyone who seeks them out. Other users can do that by going to that your Timeline/Wall (for Friends or the Public, depending on settings that you can control). For abuse and regulatory purposes, this aspect of Facebook is essentially a user-friendly Web hosting provider -- with no new First Amendment harms or issues.

Individually Filtered News Feeds: This is where things get new and very different. Your News Feed is an individually filtered view of what your friends are saying or commenting on (including what you "Liked" as a kind of comment). Facebook's filtering algorithms filter all of that content, based on some secret algorithm, to show you the items Facebook thinks will most likely engage you. This serves as a new kind of automated moderation. Some items are upranked so they will appear in your feed, others are downranked so they will not be shown in your feed. That ranking is weighted based on the social graph that connects you to your friends, and their friends, and so on -- how much positive interest each item draws from those the fewest degrees apart from you in your social graph. That ranking is also adjusted based on all the other information Facebook has about you and our friends (from observing activity anywhere in the vast Facebook ecosystem, and from external sources). It is this new individually filtered dissemination function of social media platforms that creates this new kind of conflict between free expression and newly enabled harms. (A further important  new layer is the enablement of self-forming Groups of like-minded users who can post items to the group -- and so have them filtered into the feeds of other group members, much like a special type of Friend.)

Targeted Ads: Layered on top of the first two elements, ads are special kind of posting in which advertisers pay Facebook to have their postings selectively filtered into the news feeds of individual users. Importantly, what is new in social media is that an ad are no longer just crudely targeted to some page in a publication or some time-slot in a video channel that goes to all viewers of that page or channel. Instead, it is precision targeted (microtargeted) to a set of users who fit some narrowly defined combination of criteria (or to a Custom Audience based on specific email addresses). Thus individualized messages can be targeted to just those users predicted to be especially receptive or easily manipulated -- and to remain unseen by others. This creates an entirely new category of harm that is both powerful and secretive. (How insidious this can be has already been demonstrated in Cambridge Analytica's abuse of Facebook.)  In this respect it is much like subliminal advertising (which is banned and not afforded First Amendment protection). The questions about about the harm of paid political advertising are especially urgent and compelling, as expressed by none other than Jack Dorsey of Twitter, who has just taken an opposite stand from Zuckerberg, saying “This isn’t about free expression. This is about paying for reach. And paying to increase the reach of political speech has significant ramifications that today’s democratic infrastructure may not be prepared to handle. It’s worth stepping back in order to address.” (See more in the "Coda: The urgent issue of paid political advertising.")
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Why these principles?

For an enlightening and well-researched explanation of the legal background behind my three principles, I recommend The Case for the Digital Platform Act: Market Structure and Regulation of Digital Platforms, by Harold Feld of Public Knowledge. (My apologies if I mis-characterize any of his points here.)

Feld's Chapter V parses these issues nicely, with a detailed primer on First Amendment issues, as evolved in communications and media law and regulation history. It also provides an analysis of how these platforms are a new kind of hybrid of direct one-to-one and one-to-many communications -- and how they add a new level of self-organizing many-to-many communities (fed by the new filtering algorithms). He explains why we should preserve strong freedom of speech for the one-to-one, but judiciously regulate the one-to-many. He also notes how facilitating creation of self-organizing communities introduces a new set of dangerous issues (including the empowerment of terrorist and hate groups who were previously isolated).

I have previously expressed similar ideas, focusing on better ways to do the filtering and precision targeting of content to an individual level that powers the one-to-many communication on these platforms and drives their self-organizing communities. That filtering and targeting is quantum leaps beyond anything ever before enabled at scale. Unfortunately, it is currently optimized for advertiser value, rather than user value.

The insidious new harm in false speech and other disinformation on these platforms is not in the speech, itself -- and not in simple distribution of the speech -- but in the abuse of this new platform service of precision targeting (microtargeting). Further, the essential harm of the platforms is not that they have our personal information, but in what they do with it. As described in the sidebar above, filtering -- based on our social graphs and other personal data -- is the core service of social media, and that can be a very valuable service. This filtering acts as a new, automated, form of moderation -- one that emerges from the platform's algorithms as they both drive and are driven by the ongoing activity of its users in a powerful new kind of feedback loop. The problem we now face with social media arises when that filtering/moderation service is misguided and abused:

• This new microtargeted filtering service can direct user posts or paid advertising to those most vulnerable to being manipulated, without their informed permission or awareness.
• The social media feedback cycle can further enlist those manipulated users to be used as conduits ("useful idiots") to amplify that harm throughout their social graphs (much like the familiar screech of audio feedback that is not properly damped). 

So that is where some combination of self-regulation and government regulation is most needed. Feld points to many relevant precedents for content moderation that have been held to be consistent with First Amendment rights, and he suggests that this is a fruitful area for regulatory guidance. My perspective on this is:

• Regulation and platform self-regulation can be applied to limit social media harms, without impermissible limitation of rights of speech or the press. 
• Free expression always entails some risk of harm that we accept as free society.
• The harm we can best protect against is not the posting of harmful content, but the delivering of that harmful content to those who have not specifically sought it out. 
• That is where Zuckerberg completely misses the point (whether by greed, malice, or simple naivete -- “It is difficult to get a man to understand something, when his job depends on his not understanding it”).
• And that is where many of Zuckerberg's opponents waste their energy fighting the wrong battle -- one they cannot and should not win. 

Freedom of speech (posting), not freedom of intrusion on others who have not invited it.

That new kind of intrusion is the essential issue that most discussion seems to be missing.

• I suggest that users should retain the right to post information with few restriction (the narrow exceptions that have traditionally been allowed by the courts as appropriate limits to First Amendment rights). 
• That can be allowed without undue harm, as long as objectionable content is automatically downranked enough in a filtering (moderation) process, to largely avoid sending it to users who do not want such content. 
• This is consistent with the safe-harbor provisions of Section 230 of the Communications Decency Act of 1996. That was created with thought to the limited and largely unmoderated posting functions of early Web aggregators (notably CompuServe and Prodigy, as litigated at the time). That also accepted the the freedom of the myriad independent Web sites that one had to actively seek out. 
• Given the variation in community standards that complicate the handling of First Amendment rights by global platforms, filtering can also be applied to selectively restrict distribution of postings that are objectionable in specific communities or jurisdictions, without restricting posters or other allowable recipients.

As an important complement to this understanding of the problem, I also argue that users should be granted significant ability to customize the filtering process that serves them. That could better limit the exposure of users (and communities of users) to undesired content, without censoring material they do want.

• Filtering should be a service for users, and thus should be selectable and adjustable by users to meet their individual desires. That customization should be dynamically modifiable, as a user's desires vary from time to time and task to task. (Some similar selectability has been offered to a limited extent for search -- and should apply even more fully to feeds, recognizing that search and feeds are complementary services. 
• Issues here relate not only to one-to-one versus one-to-many, but to distinguish the user-active "pull" of requested information (such as a Web site access) versus the user-passive "push" of unsolicited information in a platform-driven feed. Getting much smarter about that would have huge value to users, as well as limiting abuses. 

Recipient-controlled "censorship:" content filtering, user choice, and competitive innovation - 

I suggest new levels of censorship of social media postings are generally not needed, because filtering enables a new kind of recipient-controlled "censorship" of delivery.

Social media work because they offer a new kind of filtering service for users -- most particularly, filtering a feed based on one's social graph. That has succeeded in spite of the fact that the platforms currently give their users little say over how that filtering is done (beyond specifying the social graph), and largely use it to manipulate their users rather than serve them. I put that forth as a central argument for regulation and antitrust action against the platforms.

Filtering algorithms should give users the kind of content they value, when they value it:

• to include or exclude what the user considers to be objectionable or of undesired quality generally
• to be dynamically selectable  (or able to sense the user's mood, task, and flow state) 
• to filter for challenge, enlightenment, enjoyment, humor, emotion, support, comraderie, or relaxation at any given time. 

I explain in detail how smart use of "augmented intelligence" that draws on human inputs can enable that in The Augmented Wisdom of Crowds: Rate the Raters and Weight the Ratings and in A Cognitive Immune System for Social Media -- Developing Systemic Resistance to Fake News. This kind of hybrid man+machine intelligence can be far more powerful (and dynamically responsive) than either human or machine intelligence alone in determining the relevance, value, and legitimacy of social media postings (and ads). With this kind of smart real-time filtering of our feeds to protect us, censorship of postings can be limited to clearly improper material. Such methods have gotten little attention because Facebook is secretive about its filtering methods, and has had little incentive to develop them to serve users in this way. (But Google's PageRank algorithm has demonstrated the power of such multilevel rate the raters techniques to determine the relevance, value, and legitimacy of content.)

A monolithic platform like Facebook would be hard-pressed to deliver that level of flexibility and innovation for a full range of user desires and skill levels even if it wanted to. Key strategies to meet this complex need are:

• to enable users to select from an open market in filtering services, each filtering service provider tuning its algorithms to provide value that competes in the marketplace to appeal to specific segments of users 
• to combine multiple filtering services and algorithms to produce a desired overall effect
• to allow filtering algorithm parameters to be changed by their users to vary the mix of algorithms and the operation of individual algorithms at will
• to also factor in whatever "expert" content rating services they want.

(For an example of how such an open market might be shaped, consider the long-successful model of the open market for analytics that are used to filter financial market data to rank investment options. Think of social media as having user interface agents, repositories of posts, repositories of social graphs, and filtering/presentation tools, where the latter correspond to the financial analytics. Each of those elements might be separable and interoperable in an open competitive market.) 

These proposals have huge implications for speech and democracy, and well as for competitive innovation in augmenting the development of human wisdom (or de-augmenting it, as is happening now). That is how Facebook and other platforms could be much better at "bringing people closer together" without being so devilishly effective at driving them apart.

The need for a New Digital Platform Agency 

While adding bureaucracy is always a concern -- especially relating to the dynamic competitive environment of emerging digital technology -- there are strong arguments for that in this context.

The world is coming to realize that the Chicago School of antitrust that dominated the recent era of narrow antitrust enforcement is not enough. Raising "costs" to consumers is not a sufficient measure of harm when direct monetary costs to consumers are "zero." The real costs are not zero. Understanding what social media could do for us provides a reference point that shows how much we are really paying for the low-value platform services we now have. We cannot afford these supposedly "free" services!

Competition for users could change the value proposition, but this space is too complex, dynamic, and dependent on industry and technical expertise to be left to self-regulation, the courts, or legislation.

We need a new, specialized agency. The Feld report (cited above) offers in-depth support for such an agency, as do the three references recommended in the announcement of a conference on The Debate Over a New Digital Platform Agency: Developing Digital Authority and Expertise. (I recently attended that conference, and plan to post more about in the near future).

Just touching on this theme, we need a specialist agency that can regulate the platforms with expertise (much as the FCC has regulated communications and mass media) to find the right balance between the First Amendment and the harmful speech that it does not protect -- and to support open, competitive innovation as this continues to evolve. Many are unaware of the important and productive history here. (I observed from within the Bell System how the FCC and the courts regulated and eventually broke it up, and how this empowered the dynamic competition that led to the open Web and the Internet of Things that we now enjoy.) Inspired by those lessons, I offer specific new suggestions for regulation in Architecting Our Platforms to Better Serve Us -- Augmenting and Modularizing the Algorithm. Creating such an agency will take time, and be challenging -- but the alternative is to put not only the First Amendment, but our democracy and our freedom at risk.

These problems are hard, both for user speech, and for the special problem of paid advertising, which gives the platforms an incentive to serve advertisers, not users. As Dorsey of Twitter put it:

These challenges will affect ALL internet communication, not just political ads. Best to focus our efforts on the root problems, without the additional burden and complexity taking money brings. Trying to fix both means fixing neither well, and harms our credibility. ...For instance, it‘s not credible for us to say: “We’re working hard to stop people from gaming our systems to spread misleading info, buuut if someone pays us to target and force people to see their political ad…well...they can say whatever they want! 😉”

I have outlined a promising path toward solutions that preserve our freedom of speech while managing proper targeting of that speech, the underlying issue that few seem to recognize. But it will be a long and winding road, one that almost certainly requires a specialized agency to set guidelines, monitor, and adjust, as we find our way in this evolving new world.

Coda: The urgent issue of paid political advertising

The current firestorm regarding paid political advertising highlights one area where my proposals for smarter filtering and expert regulation are especially urgent, and where the case for reasonable controls on speech is especially well founded. My arguments for user control of filtering would have advertising targeting options be clearly subordinate to user filtering preferences. That seems to be sound in terms of First Amendment law, and common sense. Amplifying that are the arguments I have made elsewhere (Reverse the Biz Model! -- Undo the Faustian Bargain for Ads and Data) that advertising can be done in ways that better serve both users and well-intended advertisers. All parties win when ads are relevant, useful, and non-intrusive to their recipients.

But given the urgency here, for temporary relief until such selective controls can be put into effect, Dorsey's total ban on Twitter seems well worth considering for Facebook as well. Zuckerberg's defensive waving of the flag of free expression seems naive and self-serving.

[See my newer post (11/6) on stopgap solutions for controversial and urgent concerns leading in to the 2020 election: 2020: A Goldilocks Solution for False Political Ads on Social Media is Emerging. It reorganizes and expands on updates that are retained below.]

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See the Selected Items tab for more on this theme.

Two key summaries:

• A Cognitive Immune System for Social Media -- Developing Systemic Resistance to Fake News – why we need a systemic solution to limit the spread of cognitive pathogens.
• The Augmented Wisdom of Crowds:  Rate the Raters and Weight the Ratings – details a broad architecture for where we should be going in social media (and digital democracy more broadly), more comprehensive and powerful than any I have seen.

Further discussion is in these posts:

• The Tao of Fake News – the essential need for fuzziness in our logic: the inherent limits of experts, moderators, and rating agencies – and the need for augmenting the wisdom of the crowd (as essential to maintaining the intellectual openness of our democratic/enlightenment values).
• In the War on Fake News, All of Us are Soldiers, Already! – a simpler view of the massive problem of separating the real from the fake -- and why we must exploit the mass of information already available from the crowd.
• Filtering for Serendipity -- Extremism, 'Filter Bubbles' and 'Surprising Validators' – shows how this architecture addresses these specific concerns.
• Architecting Our Platforms to Better Serve Us -- Augmenting and Modularizing the Algorithm – broad solutions to technical issues of openness, transparency, regulation, antitrust, and market forces in platform architecture.
• An Open Letter to Influencers Concerned About Facebook and Other Platforms (and other posts that it refers to) – suggestions on business model incentives and regulation.

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Updates on stopgaps have since been consolidated into an 11/6 post: 2020: A Goldilocks Solution for False Political Ads on Social Media is Emerging...
That is more complete, but this section is retained as a history of updates.

[Update 11/2/19:]

An excellent analysis of the special case of political speech related to candidates is in Sam Lessin's 2016 Free Speech and Democracy in the Age of Micro-Targeting, which makes a well-reasoned argument that:

The growth of micro-targeting and disappearing messaging on the internet means that politicians can say different things to large numbers of people individually, in a way that can’t be monitored. Requirements to put this discourse on the public record are required to maintain democracy.

Lessin has a point that the secret (and often disappearing) nature of these communications, even when invited, is a threat to democracy. I agree that his remedy of disclosure is powerful, and it is a potentially important complement to my broad remedy of user-controlled targeting filters.

2020 Stopgaps?  

As to the urgent issue of the 2020 election, acting quickly will be hard. My proposal for user-controlled targeting filters is unlikely to be feasible as soon as 2020. So what can we do now?

Perhaps most feasible for 2020, is a simplistic stop-gap solution that might be easy to apply quickly:  just enact a temporary ban -- not on placing political ads, but on the individualized targeting of political ads. Do this as a simple and safe compromise between the Zuckerberg and Dorsey policies until we have a regulatory regime to manage micro-targeting properly:

• Avoid a total ban on political ads on social media, but allow ads to only be run just as they are in traditional media, in a way that is no more targeted than traditional media. 
• Disallow precision targeting to individuals: allow as many or as few ads as advertisers wish to purchase, but target them to all users, or to whatever random subset of all users fill the paid allotment.
• A slight extension of this might permit a "traditional" level of targeting, such as to users within broad geographic areas, or to a single affinity category that is not more precise or personalized than traditional print or TV slotting options.

This is consistent with my point that the harm is not the speech, but the precision targeting of the speech, and would buy time to develop a more nuanced approach. It is something that Zuckerberg, Dorsey, and others could just decide to do on their own (...or be pressured to do).

[Update 11/3/19:] Siva Vaidhyanathan made a very similar proposal to my stop-gap suggestion: "here’s something Congress could do: restrict the targeting of political ads in any medium to the level of the electoral district of the race." That seems a good compromise that could stand until we have a better solution (or become a part of a more complete solution). (I am not sure if Vaidhyanathan meant to allow targeting to the level of individual districts in a multi-district election, but it seems to me that would be sufficient to enable reasonable visibility and not much harder to do quickly than the broader bans I had suggested.)

[Update 11/5/19:] Three other experts have argued for much the same kind of limits on targeting as the effective middle-ground solution.

(Alex Stamos from CJR)

One is Alex Stamos, in a 10/28 CJR interview by Mathew Ingram, Talking with former Facebook security chief Alex Stamos. Stamos offers a useful diagram to clarify key elements of Facebook and other social media that are often blurred together. He clarifies the hierarchy of amplification by advertising and recommendation engines (filtering of feeds) at the top, and free expression in various forms of private messaging at the bottom. This shows how the risks of abuse that need control are primarily related to paid targeting and to filtering. Stamos points out that "the type of abuse a lot of people are talking about, political disinformation, is absolutely tied to amplification" and that at the rights of unfettered free expression get stronger at the bottom, "the right of individuals to be exposed to information they have explicitly sought out."

Stamos argues that "Tech platforms should absolutely not fact-check candidates organic (unpaid) speech," but, in support of the kind of targeting limit suggested here, he says "I recommended, along with my partners here at Stanford, for there to be a legal floor on the advertising segment size for ads of a political nature."

Ben Thompson, in Tech and Liberty, supports Stamos' arguments and distinguishes rights of speech from "the right to be heard." He notes that "Targeting... both grants a right to be heard that is something distinct from a right to speech, as well as limits our shared understanding of what there is to debate."

And -- I just realized there had been another powerful voice on this issue! Ellen Weintraub, chair of the Federal Election Commission (in WaPo), Don’t abolish political ads on social media. Stop microtargeting. She suggests the same kind of limits on targeting of political ads outlined here, in even more specific terms (emphasis added):

A good rule of thumb could be for Internet advertisers to allow targeting no more specific than one political level below the election at which the ad is directed. Want to influence the governor’s race in Kansas? Your Internet ads could run across Kansas, or target individual counties, but that’s it. Running at-large for the Houston City Council? You could target the whole city or individual council districts. Presidential ads could likely be safely targeted down two levels, to the state and then to the county or congressional district level.

Maybe this flurried convergence of informed opinion will actually lead to some effective action.

Until we get more key people (including the press) to have some common understanding of what the problem is, it will be very hard to get a solution. For most of us, that is just a matter of making some effort to think clearly. For some it seems to be a matter of motivated reasoning that makes them not want to understand. (Many -- not always the same people -- have suggested that both Zuckerberg and Dorsey suffer from motivated reasoning.)

...And, as addressed in the first sections of this post, maybe that will help move us toward broader action to regain the promise of social media -- to apply smart filtering to make its users smarter, not dumber!

A Regulatory Framework for the Internet (with Thanks to Ben Thompson)

Summarizing Ben Thompson of Stratechery, plus my own targeted proposals

"A Regulatory Framework for the Internet," Ben Thompson's masterly framework, should be required reading for all regulators, as well as anyone concerned about tech and society. (Stratechery is one of the best tech newsletters, well worth the subscription price, but this article is freely accessible.)

I hope you will read Ben's full article, but here are some points that I find especially important, followed by the suggestions I posted on his forum (which is not publicly accessible).

Part I -- Highlights from Ben's Framework (emphasis added)

Opening with the UK government White Paper calling for increased regulation of tech companies, Ben quotes MIT Tech Review about the alarm it raised among privacy campaigners, who "fear that the way it is implemented could easily lead to censorship for users of social networks rather than curbing the excesses of the networks themselves."

Ben identifies three clear questions that make regulation problematic:

First, what content should be regulated, if any, and by whom?
Second, what is a viable way to monitor the content generated on these platforms?
Third, how can privacy, competition, and free expression be preserved?

Exploring the viral spread of the Christchurch hate crime video, he gets to a key issue:

What is critical to note, though, is that it is not a direct leap from “pre-Internet” to the Internet as we experience it today. The terrorist in Christchurch didn’t set up a server to livestream video from his phone; rather, he used Facebook’s built-in functionality. And, when it came to the video’s spread, the culprit was not email or message boards, but social media generally. To put it another way, to have spread that video on the Internet would be possible but difficult; to spread it on social media was trivial.

The core issue is business models: to set up a live video streaming server is somewhat challenging, particularly if you are not technically inclined, and it costs money. More expensive still are the bandwidth costs of actually reaching a significant number of people. Large social media sites like Facebook or YouTube, though, are happy to bear those costs in service of a larger goal: building their advertising businesses.

Expanding on business models, he describes the ad-based platforms as "Super Aggregators:"

The key differentiator of Super Aggregators is that they have three-sided markets: users, content providers (which may include users!), and advertisers. Both content providers and advertisers want the user’s attention, and the latter are willing to pay for it. This leads to a beautiful business model from the perspective of a Super Aggregator:

Content providers provide content for free, facilitated by the Super Aggregator
Users view that content, and provide their own content, facilitated by the Super Aggregator
Advertisers can reach the exact users they want, paying the Super Aggregator 

...Moreover, this arrangement allows Super Aggregators to be relatively unconcerned with what exactly flows across their network: advertisers simply want eyeballs, and the revenue from serving them pays for the infrastructure to not only accommodate users but also give content suppliers the tools to provide whatever sort of content those users may want.

...while they would surely like to avoid PR black-eyes, what they like even more is the limitless supply of attention and content that comes from making it easier for anyone anywhere to upload and view content of any type.

...Note how much different this is than a traditional customer-supplier relationship, even one mediated by a market-maker... When users pay they have power; when users and those who pay are distinct, as is the case with these advertising-supported Super Aggregators, the power of persuasion — that is, the power of the market — is absent.

He then distinguishes the three types of "free" relevant to the Internet, and how they differ:

“Free as in speech” means the freedom or right to do something
“Free as in beer” means that you get something for free without any additional responsibility
“Free as in puppy” means that you get something for free, but the longterm costs are substantial

...The question that should be asked, though, is if preserving “free as in speech” should also mean preserving “free as in beer.”

Platforms that are paid for by their users are "regulated" by the operation of market forces, but those that are ad-supported are not, and so need external regulation.

Ben concludes that:

...platform providers that primarily monetize through advertising should be in their own category: as I noted above, because these platform providers separate monetization from content supply and consumption, there is no price or payment mechanism to incentivize them to be concerned with problematic content; in fact, the incentives of an advertising business drive them to focus on engagement, i.e. giving users what they want, no matter how noxious.

 This distinct categorization is critical to developing regulation that actually addresses problems without adverse side effects

...from a theoretical perspective, the appropriate place for regulation is where there is market failure; constraining the application to that failure is what is so difficult.

That leads to Ben's figure that brings these ideas together, and delineates critical distinctions:

I agree completely, and build on that with my two proposals for highly targeted regulation...

Part II -- My proposals, as commented on in the Statechery Forum 
(including some minor edits and portions that were abridged to meet character limits):

Elegant model, beautifully explained! Should be required reading for all regulators.

FIRST:  Fix the business model! I suggest taking this model farther, and mandating that the "free beer" ad-based model be ratcheted away once a service reaches some critical level of scale. That would solve the problem -- and address your concerns about competition.

Why don't we regulate to fix the root cause? The root cause of Facebook's abuse of trust is its business model, and until we change that, its motivations will always be opposed to consumer and public trust.

Here is a simple way to force change, without over-engineering the details of the remedy. Requiring a growing percentage of revenue from users is the simplest way to drive a fundamental shift toward better corporate behavior. Others have suggested paying for data, and I suggest this is most readily done in the form of credits against a user service fee. Mandating that a target level of revenue (above a certain level) come from users could drive Facebook to offer such data credits, as a way to meet their user revenue target (even if most users pay nothing beyond that credit). We will not motivate trust until the user becomes the customer, and not the product.

There is a regulatory method that has already proven its success with a similarly challenging problem – forcing automakers to increase the fuel efficiency of the cars they make. The US government has for years mandated staged multi-year increases in Average Fuel Efficiency. This does not mandate how to fix things. It mandates a limit on the systems that have been shown to cause harm. Facebook and YouTube can determine how best to achieve that. Require that X% of the revenue come from users rather than advertisers. Government can monitor progress, with a timetable for ratcheting up the percentage. (This should apply only above some amount of revenues, to facilitate competition.)

With that motivation, Facebook and YouTube can be driven to shift from advertising revenue to customer revenue. That may seem difficult, but only for lack of trying. Credits for attention and data are a just a start. If we move in that direction, we can be less dependent on other, more problematic, kinds of regulation.

This regulatory strategy is outlined in To Regulate Facebook and Google, Turn Users Into Customers (in Techonomy). More on why that is important in Reverse the Biz Model! -- Undo the Faustian Bargain for Ads and Data. (And some suggestions on more effective ways to obtain user revenue:  Information Wants to be Free; Consumers May Want to Pay, (also in Techonomy.)

SECOND: Downrank dissemination, don't censor speech! Your points about limiting user expression, and that the real issue is harmful spreading on social media, are also vitally important.

I say the real issue is:

1.  Not: rules for what can and cannot be said – speech is a protected right
2.  But rather: rules for what statements are seen by who – distribution (how feeds are filtered and presented) is not a protected right.

The value of a social media service should be to disseminate the good, not the bad. (That is why we talk about “filter bubbles” – failures of value-based filtering.)

I suggest Facebook and YouTube should have little role in deciding what can be said (other than to enforce government standards of free speech and clearly prohibited speech to whatever extent practical).  What matters is who that speech is distributed to, and the network has full control of that.  Strong downranking is a sensible and practical alternative to removal -- far more effective and nuanced, and far less problematic.

I have written about new ways to use PageRank-like algorithms to determine what to downrank or uprank – “rate the raters and weight the ratings.”

• Facebook can have a fairly free hand in downranking objectionable speech
• They can apply community standards to what they promote -- to any number of communities, each with varying standards.
• They could also enable open filtering, so users/communities can chose someone else’s algorithm (or set their preferences in any algorithm). 
• With smart filtering, the spread of harmful speech can be throttled before it does much harm.
• The “augmented wisdom of the crowd” can do that very effectively, on Internet scale, in real time.
• No pre-emptive, exclusionary, censorship technique is as effective at scale -- nor as protective of free speech rights or community standards.

That approach is addressed at some length in these posts (where “fake news” is meant to include anything objectionable to some community):

• The Augmented Wisdom of Crowds: Rate the Raters and Weight the Ratings
• A Cognitive Immune System for Social Media -- Developing Systemic Resistance to Fake News

…and some further discussion on that:

• Architecting Our Platforms to Better Serve Us -- Augmenting and Modularizing the Algorithm
• The Tao of Fake News
• In the War on Fake News, All of Us are Soldiers, Already!)

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More of my thinking on these issues is summarized in this Open Letter to Influencers Concerned About Facebook and Other Platforms

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"The Square and the Tower" — Augmenting and Modularizing the Algorithm (a Review and Beyond)

[Note: A newer post updates this one and removes much of the book review portion, to concentrate on the forward-looking platform issues: Architecting Our Platforms to Better Serve Us -- Augmenting and Modularizing the Algorithm.]

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Niall Ferguson's new book, The Square and the Tower: Networks and Power from the Freemasons to Facebook is a sweeping historical review of the perennial power struggle between top-down hierarchies and more open forms of networks. It offers a thought-provoking perspective on a wide range of current global issues, as the beautiful techno-utopian theories of free and open networks increasingly face murder by two brutal gangs of facts: repressive hierarchies and anarchistic swarms.

Ferguson examines the ebb and flow of power, order, and revolution, with important parallels between the Gutenberg revolution (which led to 130 years of conflict) and our digital revolution, as well as much in between. There is valuable perspective on the interplay of social networks (old and new), the hierarchies of governments (liberal and illiberal), anarchists/terrorists, and businesses (disruptive and monopolistic). One can disagree with Ferguson's conservative politics yet find his analysis illuminating.

Drawing on a long career as a systems analyst/engineer/designer, manager, entrepreneur and inventor, I have recently come to share much of Ferguson's fear that we are going off the rails. He cites important examples like the 9/11 attacks, counterattacks, and ISIS, the financial meltdown of 2008, and most concerning to me, the 2016 election as swayed by social media and hacking. However -- discouraging as these are -- he seems to take an excessively binary view of network structure, and to discount the ability of open networks to better reorganize and balance excesses and abuse. He argues that traditional hierarchies should reestablish dominance.

In that regard, I think Ferguson fails to see the potential for better ways to design, manage, use, and govern our networks -- and to better balance the best of hierarchy and openness. To be fair, few technologists are yet focused on the opportunities that I see as reachable, and now urgently needed.

New levels of man-machine augmentation and new levels of decentralizing and modularizing intelligence can make these network smarter and more continuously adaptable to our wishes, while maintaining sensible and flexible levels of control.   We can build on distributed intelligence in our networks to find more nuanced ways to balance openness and stability (without relying on unchecked levels of machine intelligence). Think of it as a new kind of systems architecture for modular engineering of rules that blends top-down stability with bottom-up emergence that apply checks and balances that work much like our representative democracy. This is a still-formative exploration of some ideas that I have written about, and plan to expand on in the future. First some context.

The Square (networks), the Tower (hierarchies) and the Algorithms that make all the difference

Ferguson's title comes from his metaphor of the medieval city of Sienna, with a large public square that serves as a marketplace and meeting place, and a high tower of government (as well as a nearby cathedral) that displayed the power of those hierarchies. But as he elaborates, networks have complex architectures and governance rules that are far richer than the binary categories of either "network" ( a peer to peer network with informal and emergent rules) or "hierarchy" (a constrained network with more formal directional rankings and restrictions on connectivity).

The crucial differences among all kinds of networks are in the rules (algorithms, code, policies) that determine which nodes connect, and with what powers. While his analysis draws out the rich variety of such structures, in many interesting examples, with diagrams, what he seems to miss is any suggestion of a new synthesis. Modern computer-based networks enable our algorithms to be far more nuanced and dynamically variable. They become far more emergent in both structure and policy, while still subject to basic constraints needed for stability and fairness.

Traditional networks have rules that are either relatively open (but somewhat slow to change), or constrained by laws and customs (and thus resistant to change) -- and even our current social and information networks are constrained in important ways. For example,

• The US constitution defines the powers and the structures for the governing hierarchy, and processes for legislation and execution, made resilient by its provisions for self-amendable checks and balances. 
• Real-world social hierarchies have structures based on empowered people that tend to shift more or less slowly.
• Facebook has a social graph that is emergent, but the algorithms for filtering who sees what are strictly controlled by and private to Facebook. (They have just announced a major change --  unilaterally -- hopefully for the better for users and society, if not for content publishers.)
• Google has a page graph that is given dynamic weight by the PageRank algorithm, but the management of that algorithm is strictly controlled by Google. It has been continuously evolving in important respects, but the details are kept secret to make it harder to game.

As Ferguson points out, our vaunted high-tech networks are controlled by corporate hierarchies (he refers to FANG, Facebook, Amazon, Netflix, and Google, and BAT, Baidu, Alibaba, and Tencent) -- but subject to levels of government control that vary in the US, EU, and China. This corporate control is a source of tension and resistance to change -- and a barrier to more emergent adaptation to changing needs and stressors (such as the Russian interference in our elections). These new monopolistic hierarchies extract high rents from the network -- meaning us, the users -- mostly in the form of advertising and sales of personal data.

A fuller summary of Ferguson's message is in his WSJ preview article, "In Praise of Hierarchy." That headlines which side of fence he is on.

Smarter, more open and emergent algorithms -- APIs and a common carrier governance model

My view on this is more positive -- in that the answer to the question of governance is to make our network algorithms not only smarter, but more open to individual and multi-party control. Business monopolies or oligarchies (or governments) may own and control essential infrastructure, but we can place limits on what they control and what is open. In the antitrust efforts of the past century governments found need to regulate rail and telephone networks as common carriers, with limited corporate-owner power to control how they are used, giving marketplace players (competitors and consumers) a large share in that control. 

Initially this was rigid and regulated in great detail by the government (very hierarchical), but the Carterfone decision showed how to open the old AT&T Bell System network to allow connection of devices not tested and approved by AT&T. Many forget how only AT&T phones could be used (except for special cases of alternative devices like early faxes (Xerox "telecopiers") that went through cumbersome and often arbitrary AT&T approval processes). That changed when the FCC's decision opened the network up to any device that met defined electrical interface standards (using the still-familiar RJ11, a "Registered Jack"). Similarly only AT&T long-distance connections could be used, until the antitrust Consent Decree opened up competition among the "Baby Bells" and broke them off from Long Lines to compete on equal terms with carriers like MCI and Sprint.

In software systems, such plug-like interfaces are known as APIs (Application Program Interfaces), and are now widely accepted as the standard way to let systems inter-operate with one another -- just enough, but no more -- much like a hardware jack does. This creates a level of modularity in architecture that lets multiple systems, subsystems, and components  inter-operate as interchangeable parts -- the great advance of the first Industrial Revolution.

What I suggest as the next step in evolution of our networks is a new kind of common carrier model that recognizes networks like Facebook, Google, and Twitter as common utilities once they reach some level of market dominance. Then antitrust protections would mandate open APIs to allow substitution of key components by customers -- to enable them to choose from an open market of alternatives that offer different features and different algorithms. Some specific suggestions are below, but first, a bit more on the motivations.

Modularity, emergence, markets, transparency, and democracy

Systems architects have long recognized that modularity is essential to making complex systems feasible and manageable. Software developers saw from the early days that monolithic systems did not scale -- they were hard to build, maintain, or modify. Web 2.0 extended that modularity to our network services, using network APIs that could be opened to the marketplace. Now we see wonderful examples of rich applications in the cloud composed of elements of logic, data, and analytics from a vast array of companies (such as travel services that seamlessly combine air, car rental, hotel, local attractions, loyalty programs, and tracking services from many companies).

The beauty of this kind of modularity is that systems can be highly emergent, based on the transparency and stability of published APIs, to quickly adapt to meet needs that were not anticipated. Some of this can be at the consumer's discretion, and some is enabled by nimble entrepreneurs. The full dynamics of the market can be applied, yet basic levels of control can be retained by the various players to ensure resilience and minimize abuse or failures.

The challenge that Ferguson makes clear is how to apply hierarchical control in the form of regulation in a way that limits risks, while enabling emergence driven by market forces. What we need is new focus on how to modularize critical common core utility services and how to govern the policies and algorithms that are applied, at multiple levels in the design of these systems (another kind of hierarchy). That can be done through some combination of industry self-regulation (where a few major players have the capability to do that, probably faster and more effectively than government), but by government where necessary (and only to the extent and duration necessary).

That obviously will be difficult and contentious, but it is now essential, if we are not to endure a new age of disorder, revolution, and war much like the age that followed Gutenberg. Silicon Valley and the rest of the tech world need to take responsibility for the genie they have let out of the bottle, and mobilize to deal with it.

Once that progresses and is found to be effective, similar methods can be applied to make government itself more modular, emergent, transparent, and democratic -- moving carefully toward "Democracy 2.0." (The carefully part is important -- Ferguson rightfully notes the dangers we face, and we have done a poor job of teaching our citizens, and our technologists, the principles of history, civics, and governance that are prerequisite to a working democracy.)

Opening the FANG walled gardens (with emphasis on Facebook and Google, plus Twitter)

This section outlines some rough ideas. (Some were posted in comments on an article in The Information by Sam Lessin, titled, "The Tower of Babel: Five Challenges of the Modern Internet" -- another tower.)

The fundamental principle is that entrepreneurs should be free to innovate improvements to these "essential" platforms, to be selected by consumer market forces. Just as we moved beyond the restrictive walled gardens of AOL, and the early closed app stores (limited to apps created by Apple or Motorola or Verizon), we have unleashed a cornucopia of innovative Web services and apps that have made our services far more effective (and far more valuable to the platform owners as well, in spite of their fears). Why should first movers be allowed to block essential innovation? Why should they have sole control of the essential algorithms that are coming to govern major aspects of our lives? Why shouldn't our systems evolve toward fitness functions that we control, with just enough hierarchical structure to prevent excessive instability at any given time?

Filtering rules. Filters are central to the function of Facebook, Google, and Twitter. As Ferguson observes, there are issues of homophily, filter bubbles, echo chambers, and fake news, and spoofing that are core to whether these networks make us smart or stupid, and whether we are easily manipulated to think in certain ways. Why not mandate that platforms be opened to user-selectable filtering algorithms (and/or human curators)? The major platforms can control their core services, but could allow for separate filters that inter-operate. Let users control their filters, whether just by setting key parameters, or by substituting pluggable alternative filters. This would be much like third party analytics in financial market data systems. Greater competition and transparency would allow users to compare alternative filters and decide what kinds of content they do or do not want. It would stimulate innovation to create new kinds of filters that might be far more useful.

For example, I have proposed strategies for filters that can help counter filter bubble effects by being much smarter about how people are exposed to views that may be outside of their bubble, doing it in ways that they welcome and think about. My post, Filtering for Serendipity -- Extremism, "Filter Bubbles" and "Surprising Validators" explains the need, and how that might be done. The key idea is to assign levels of authority to people based on the reputational authority that other people ascribe to them (think of it a RateRank, analogous to Google's PageRank algorithm). This approach also suggests ways to create smart serendipity, something that could be very valuable as well.

The "wisdom of the crowd" may be a misnomer when the crowd is an undifferentiated mob, but, what I propose seeking the wisdom of the smart crowd -- first using the crowd to evaluate who is smart, and then letting the wisdom of the smart sub-crowd emerge, in a cyclic, self-improving process (much as Google's algorithm improves with usage).

Social graphs and user agents: Why do Facebook, Twitter, LinkedIn, and others own separate, private forms of our social graph. Why not let other user agents interoperate with a given platform’s social graph? Does the platform own my social graph or do I? Does the platform control how that affects my filter or do I? Yes, we may have different flavors of social graph, such as personal for Facebook and professional for LinkedIn, but we could still have distinct sub-communities that we select when we use an integrated multi-graph, and those could offer greater nuance and flexibility with more direct user control.

Email systems were modularized long ago, so that we compose and read mail using user agents (Outlook, Apple mail, Gmail, and others) that connect with remote mail transfer agent servers (that we may barely be aware of) which interchange mail with any other mail transfer agent to reach anyone using any kind of user agent, thus enabling universal connectivity. Why not do the same to let any social media user agent inter-operate with any other, using a common social graph? We would then set our user agent to apply filters to let us see whichever communities we want to see at any given time.

Identity: A recurring problem with many social networks is abuse by anonymous users (often people with many aliases, or even just bots). Once again, this need not be a simple binary choice. It would not be hard to have multiple levels of participant, some anonymous and some with one or more levels of authentication as real human individuals (or legitimate organizations). These could then be clearly marked in feeds, and users could filter out anonymous or unverified users if desired.

Value transfers and extractions: Another important problem, and one that Ferguson cites is that the new platform businesses are driven by advertising and data sales, which means the consumer is not the customer but the product. Short of simply ending that practice (to end advertising and make the consumer the customer), those platforms could be driven to allow customer choice about such intrusions and extractions of value. Some users may be willing opt in to such practices, to continue to get "free" service, and some could opt out, by paying compensatory fees -- and thus becoming the customer. If significant numbers of users opted to become the customer, then the platforms would necessarily become far more customer-first -- for consumer customers, not the business customers who now pay the rent. (I have done extensive work on such alternative strategies, as described in my FairPayZone blog and my book.)

Analytics and metrics: we need access to relevant usage data and performance metrics to help test and assess alternatives, especially when independent components interact in our systems. Both developers and users will need guidance on alternatives. The Netflix Prize contests for improved recommender algorithms provided anonymized test data from Netflix to participant teams. Concerns about Facebook's algorithm, and the recent change that some testing suggests may do more harm than good, point to the need for independent review. Open alternatives will increase the need for transparency and validation by third parties. (Sensitive data could be restricted to qualified organizations.) [This paragraph added 1/14.]

If such richness sounds overly complex, remember that complexity can be hidden by well-designed user agents and default rules. Those who are happy with a platform's defaults need not be affected by the options that other users might enable (or swap in) to customize their experience. But we could have the choice, and innovations that are valuable can emerge for use by early adopters, and then spread into the mainstream if success fuels demand. That is the genius of our market economy -- a spontaneous, emergent process for adaptively finding what works and has value -- more effective than any hierarchy (as Ferguson extols, with reference to Smith, Hayek, and Levitt).

Augmentation of humans (and their networks)

Another very powerful aspect of networks and algorithms that Ferguson (and many others) neglect is  the augmentation of human intelligence. This idea dates back some 60 years (and more), when "artificial intelligence" went through its first hype cycle -- Licklider and Engelbart observed that the smarter strategy is not to seek totally artificial intelligence, but to seek hybrid strategies that draw on and augment human intelligence. Licklider called it "man-computer symbiosis, and used ARPA funding to support the work of Engelbart on "augmenting human intellect." In an age of mundane uses of computers, that proved eye-opening ("the mother of all demos") at a 1968 conference, and was one of the key inspirations for modern user interfaces, hypertext, and the Web.

The term augmentation is resurfacing in the artificial intelligence field, as we are once again realizing how limited machine intelligence still is, and that (especially where broad and flexible intelligence is needed) it is often far more effective to seek to apply augmented intelligence that works symbiotically with humans, retaining human visibility and guidance over how machine intelligence is used.

Why not apply this kind of emergent, reconfigurable augmented intelligence to drive a bottom up way to dynamically assign (and re-assign) authority in our networks, much like the way representative democracy assigns (and re-assigns) authority from the citizen up? Think of it as dynamically adaptive policy engineering (and consider that a strong bottom-up component will keep such "engineering" democratic and not authoritarian). Done well, this can keep our systems human-centered.

Not binary:  networks versus hierarchies -- "Everything is deeply intertwingled"

Ted Nelson (who coined the term "hypertext" and was also one of the foundational visionaries of the Web), wrote in 1974 that "everything is deeply intertwingled." Ferguson's exposition illuminates how true that is of history. Unfortunately, his artificially binary dichotomy of hierarchies versus networks tends to mask this, and seems to blind him to how much more intertwingled we can expect our networks to be in the future. As Nelson put it, "Hierarchical and sequential structures, especially popular since Gutenberg, are usually forced and artificial. Intertwingularity is not generally acknowledged—people keep pretending they can make things hierarchical, categorizable and sequential when they can't."

It's a race:  augmented network hierarchies that are emergently smart, balanced, and dynamically adaptable -- or disaster

If we pull together to realize this potential, we can transcend the dichotomies and conflicts of the Square and the Tower that Ferguson reveals as so wickedly complex and dangerous. Just as Malthus failed to account for the emergent genius of civilization, and the non-linear improvements it produces, Ferguson seems to discount how non-linear the effect of smarter networks with more dynamically augmented and balanced structures can be. But he is right to be very fearful, and to raise the alarm -- we are racing along a very dangerous path, and are not being nearly smart or proactive enough about what we need to do to avert disaster. What we need now is not a top-down command and control Manhattan Project, but a multi-faceted, broadly-based movement.

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First published in Reisman on User-Centered Media, 1/13/18.

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