The Pandemic Reminds Us "Everything is Deeply Intertwingled" – We Need Better Logics for That

A bat catches a cold in Wuhan, and weeks later the whole world coughs. America and China battle a trade war, and then there is a shortage of PPE and ventilator parts from China.  Poor neighborhoods suffer high death rates because of poor health, but even celebrities and heads of state go into ICUs.  The economy craters, and we argue over relief to businesses versus workers based on which is more disposed to misuse what they might be given.  Health officials say flatten the curve, financiers say reopen, and corporations say they don’t dare reopen without testing.  The Federal government is too polarized to fix much of anything, and has forgotten their real job of governing by consensus.  

Modern technologies of global connection -- both physical and virtual -- make the pandemic emerge in weeks instead of years, and make all the butterfly effects far more complex.  That is our new curse, but also our new blessing.  We have global travel and supply chains, global communications and media networks -- a global village composed of local villages.  Techies moved fast and broke society, and now discourse seems too polarized to fix it.

All of these effects are driven by market forces -- however regulated.  Marketplaces of goods and services and marketplaces of ideas.  These marketplaces are driven by complex interplays of top-down structure and bottom-up emergence from billions of actors --and systems of actors.  

Technology has made these forces more dynamic and turbulent, but technology can enable smarter and better-regulated marketplaces -- if we re-focus.  We cannot undo this onrushing dynamic -- we need to get smarter about how we use technology to help us go forward.

The pandemic may be the kick in the ass we need to reform society over a wide range of domains and levels.  Seeing the commonalities can help us capture a new synergy.  If rise to that challenge, the future will be bright. If we fail it will be dark.  Many see that, but few focus on the root causes. 

Peter Drucker said “The greatest danger in times of turbulence is not the turbulence, it is to act with yesterday’s logic.” Two new logics can help us correct the failures of our current logics.

Ever-growing intertwingularity

The problem we now face all too urgently is that our lives are all deeply intertwingled, but we fall back to simplistic “fast” thinking with rigid categories and institutions.  Some leaders rise to the challenge and others flail, and we argue over who does which.  The regulation of our marketplace of ideas that “mediates consent” about facts has broken down, as has our social/economic marketplace.  These problems are difficult and complex – but we can get smarter about solving them – the first order and second order effects.  (To get a sense of the range of these issues, see this briefing by Tony O’Driscoll [since adapted for publication] and this McKinsey report. To see how this has reopened old questions, and may provide an opening for new thinking, see this NY Times report on the shifting issues for the 2020 election.)

 

The symbolic circle of the Tao reminds us of that truth is never entirely black or white, but shades of gray that depend on the light we view it in and the perspective we view it from.  Just how much is subject to argument, discovery, and rediscovery, as reality emerges.  This is age-old, but it is more urgent than ever that we come to grips with it.  2020 will mark a turning point in human history.

For decades our world and our markets have been increasingly stressed, even as we seemed to be progressing.  Tensions of nationality, race, ideology, religion, economics, technology, and governance are raging.  “Things fall apart; the centre cannot hold /…The best lack all conviction, while the worst / Are full of passionate intensity.”  It is now urgent that we re-center more wisely on our better convictions.

The Enlightenment has run aground because those who saw the light and had the benefits did not pay enough attention to sharing that.  Liberals turned away from “the deplorables” instead of caring for and raising them up.  Capitalists extracted short-term profits and enriched themselves with stock buybacks -- exploiting workers instead of empowering them.  Factions and political parties fought zero-sum struggles to control the existing pie instead of engaging in win-win cooperation to create and share a larger pie.

The Chinese ideograph for crisis is composed from the characters for danger plus opportunity.  Many retreat in fear of the danger and seek to throw blame and erect walls, but wiser heads look to the opportunity.  Most see opportunity in narrow domains, but some look to the big picture.  We now face an urgent and historic opportunity to refocus on a more enlightened and productive kind of cooperation across the full range of issues.

Those who see and work on these problems in particular domains of concern and expertise can unite in spirit and vision with those in other domains.  We can forge a new Age of Enlightenment – a Reformation of The Enlightenment.  An awakening of interconnection and cooperative spirit is emerging.  Our challenge is to synergize it.  Some elements:

• Economic and health insecurity for some leads to insecurity for all.  A safety net is needed.
• Market systems need slack to respond to black swan events.  “Just-in-time” and “lean” are efficient only when not overstressed.  Global supply chains need resilience and redundancy. Too much slack and safety drain our wealth and will, but too little lead to disaster.
• Moving fast and breaking things can break things that cannot be fixed.  Experience can blind us, but inexperience can kill.
• Power among local, state, national, and global government must be properly balanced and adaptable to stress.  Power and wealth must be shared fairly among people, factions, and nations, or those left wanting will throw rocks at the crystal palace.  The resurgence of nationalism, factionalism, and the crisis of disinformation are symptoms of perceived unfairness.  Government that is too small is just as bad as too big.

Our modern, high-tech world is far too complex for purely top-down or bottom-up management and governance -- we need a smart and adaptive blend. That requires openness, transparency, trust, and fairness, so even when there is disagreement, there is a common sense of reasonableness and good spirit.

New Logics for Intertwingularity

My recent work has focused on two new ways to deal better with this growing complexity.  These new logics that do not just exhort people to be better and wiser, but better align interests so that virtue is rewarded. 

One relates to failures of our marketplace of ideas – especially our social media and other collaborative systems.  Computer-augmented human collaboration first emerged in the 1960s, and was used for disaster preparedness (natural and nuclear).  It progressed slowly until the Web made it far more powerful and accessible to consumers, but we failed to direct those social media systems to serve us well.  Struggling to find a business model, they hit on advertising. We now recognize that to be “the original sin of the Internet” because it misdirects our platforms to serve advertisers and not users.  Algorithms can help augment human intelligence to make us smarter collectively -- instead of making us stupider, as social media now do.  Systems that elucidate nuance, context, and perspective can empower us to draw out and augment the wisdom of crowds (as explained in detail on this blog) to deal more smartly with our deeply intertwingled world.  That could drive a new Age of Enlightenment in which technology augments the marketplace of ideas in the ways that we have always relied on to mediate consent – an emergent mix of top-down guidance and bottom-up emergence that can lead to new, yet natural, forms of digital democracy.

The other relates to failures of our economic marketplace – how we can shift from the short-term. zero-sum logic of extractive mass-market capitalism to more long-term, win-win forms of market cooperation.  That can restore the emergent, distributed, and human logic of traditional markets that Adam Smith saw as socially beneficial -- before modern mass-marketing alienated producers from consumers and lost sight of broader human values.  Our digital economy now enables new ways to shift from fighting over a current pie to cooperating to co-create a larger pie -- and to share it fairly.  That logic can empower a reformation of market capitalism from within that could actually be more profitable, and thus self-motivating.  We can apply the power of computer-mediated marketplaces to let businesses and consumers negotiate at a human level -- about the values they care about, how to co-create that value, and how to share in the benefits.  We have begun to think in terms of customer journeys, but have been trying to fit customers into segments or personas. Instead, we need to design for segments of one that are custom-fit to each customer, to build relationships with each customer on human terms.

These two logics are interrelated: a flawed economic logic for consumer platform services has been built on advertising revenue (“the Internet’s original sin”). That has warped incentives to favor engagement with junk content that sells ads, rather then the value to users of quality content. An improved logic for value will create incentives for our platforms to facilitate a logic for a better marketplace of ideas.

The brief descriptions of these new logics may sound like just more exhortations, but the posts that they link to provide details of operational mechanisms -- and evidence that their elements have proven effective.  These new combinations of elements can quickly become second nature, because they draw on and re-channel natural behaviors that promise to make them highly self-reinforcing.

Many allied visions for better logics of emergence are finding new relevance in this era of crisis.  We have only to join together and rise to the occasion.  We say that “we are all in this together” – we need to open our minds to really think that way, and to work with new logics and “choice architectures” that make that natural.  With better logics, our instinctive behaviors can once again synergize to flow in increasingly enlightened ways.

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For more about the new logic for the marketplace of ideas (and intertwingularity in general), see this list of selected items on the SmartlyIntertwingled.com blog.

For more about the new logic for the economic marketplace, see this list of selected items on the FairPayZone.com blog.

2020 Vision -- The Restoration of the Customer

The Age of the Customer: You Ain't Seen Nothin' Yet

Nearly a decade has passed since Forrester said we were entering The Age of the Customer. That is apparent and has obvious implications. But as the decade of the 2020's dawns, I call out a deeper vision -- The Restoration of the Customer -- that could bring far more fundamental changes in the coming decade.

There are two surprising turns that may be taken in this decade to restore power to customers -- one that can fundamentally change how we conduct business, and one that can fundamentally change how we collaborate.

Those turns might just begin to undo many of the ills of the industrial revolution and of the computer revolution.  Both turns center on a return to enlightened human values:

• The customer is not just a persona with a bundle of attributes that a business can learn how to manipulate, but a unique human being that has been bred since pre-history to thrive on a cooperative effort to create value and share it.
• The user is not just a a source of attention that can be engaged to be sold to advertisers, but a customer to be served what they value -- again, a cooperative effort to create value and share it.

What those paying attention see

Forrester put the basic drivers nicely (emphasis added):

In this era, digitally-savvy customers would change the rules of business, creating extraordinary opportunity for companies that could adapt, and creating existential threat to those that could not. ...It requires leaders to think and act differently – in ways that feel foreign, unfamiliar, and counter-intuitive. And honestly, it is simply hard to do. ...These dynamics will endure as new technologies like artificial intelligence and robotics emerge to challenge core notions of what it means to be a company, what it means to build human capital, and what it means to compete and win.

...And, a deeper vision

Here I point to some little recognized ideas on how re-centering on value can change not only the dynamic of commerce, but also a parallel dynamic of customer value that is equally important.

• First, the commercial dynamic that Forrester describes is just the foundation for reversing how the "progress" of technology cost us the human dimension in commerce -- a dimension that we had when commerce was just the way villagers did business with one another -- with human beings on both sides of an ongoing relationship. 
• Second, we humans, as "customers" of Web services, have lost control of our experience of the world.  Our central experience of human interaction has been hijacked by platforms who "engage" us in order to profit from bombarding us with advertising and paid propaganda.

First: Back to the future of commerce

Consumers are increasingly alienated from the companies they do business with. Instead of neighbors or shopkeepers, we deal with soulless institutions that we distrust and feel abused by. That has been, increasingly, the price of productivity and material riches. But now technology has advanced far enough to restore the dimension of human values -- if we applied to do so. That does not require that we abandon the miracle of capitalism, but only that we bring it back to the marketplace of human value. Technology now makes it possible for even large faceless institutions to build human interfaces that behave with human values. That will drive institutions to interact with human in ways that are more truly human.

FairPay is a framework for centering on why and how to do that. The key is to recenter on relationships and the creation and sharing of value in ways that are tailored to each individual. Specifics on how to do that are in my FairPayZone blog, some articles written with prominent marketing scholars, and my 2016 book. Some of the best places to begin to understand this are:

• The Ghost of Pricing Future -- A Thought Experiment -- a simple vision of human value.
• The Relationship Economy -- It's All About Valuing Customer Experiences -- how the restoration begins.
• The Elements of Next-Gen Relationships and Pricing -- A Unifying Framework -- deconstructing the elements of value.
• "Risk-Free" Subscriptions to The Celestial Jukebox? -- a simpler, 80/20 step toward value based subscriptions.
• FairPay Changes the "Game" of Commerce  -- a game theory method for seeking human value in business relationships.
• ...and for those with a more scholarly bent, A Novel Revenue Architecture to Monetize Digital Goods and Pricing in Consumer Digital Markets:  A Dynamic Framework.

Second: Who does it serve? - a course correction in how we experience the world

Social media and other online content services have changed how we experience the world, including how we interact with other people. Computer-mediation began with great hopes, but now it seems we have built a Frankenstein's monster.  As growing calls for change are beginning to focus on new levels of regulation, it is not enough to regulate against specific harms. Instead we must refocus on what we want to regulate for -- who these "services" serve, and what we want these platforms to facilitate. They were supposed to make us happy and smart -- instead they are making us angry and stupid. But technology can reverse that, if we incentivize that.

We can design new architectures for our interactive media that create value for us.  The key is to recognize that each of us is an individual, and we should be able to individualize our services, mixing and matching offerings to make just the service we want for what we are doing now. The most urgent part of that is to shape our media services to give each of us what we value. The Web stated out seeking to do that, and we can return to that vision. It won't be free, but it can be affordable. And we have seen that "free" is not really affordable (because it is not really free). If we do not change direction, our democracies and our civilization will collapse. Some starting points for seeing how:

• Our Digital Platforms -- What We Want to Regulate For (Not Just Against) -- a preliminary summary of my comments at an excellent event on this theme.
• The Augmented Wisdom of Crowds: Rate the Raters and Weight the Ratings -- a vision for how social media can make us smarter and happier.
• Reverse the Biz Model! -- Undo the Faustian Bargain for Ads and Data -- why the ad model was "the original sin of the Internet."
• The Tao of Fake News -- how truth emerges from what humans value as an informed consensus.
• Architecting Our Platforms to Better Serve Us -- Augmenting and Modularizing the Algorithm -- further suggestions on designing for human values.
• Free Speech, Not Free Targeting! (Using Our Own Data to Manipulate Us) -- how this clarifies the issues in the 2020 election crisis.

(Cross-posted with my other blog, FairPayZone.)

To Regulate Facebook and Google, Turn Users Into Customers

First published in Techonomy, 2/26/19 -- and more timely than ever...

There is a growing consensus that we need to regulate Facebook, Google, and other large internet platforms that harm the public in large part because they are driven by targeted advertising.  The seductive idea that we can enjoy free internet services — if we just view ads and turn over our data — has been recognized to be “the original sin” of the internet.  These companies favor the interests of the advertisers they profit from more than the interests of their billions of users.  They are powerful tools for mass-customized mind-control. Selling their capabilities to the highest bidder threatens not just consumer welfare, but society and democracy.

There is a robust debate emerging about how these companies should be regulated. Many argue for controls on data use and objectionable content on these platforms.  But poorly targeted regulation risks many adverse side-effects – for example abridging legitimate speech, and further entrenching these dominant platforms and impeding innovation by making it too costly for others to compete.

But I believe we need to treat the disease, not just play whack-a-mole with the symptoms. It’s the business model, stupid! It is widely recognized that the root cause of the problem is the extractive, ad-funded, business model that motivates manipulation and surveillance.  The answer is to require these companies to shift to revenue streams that come from their users.  Of course, shifting cold-turkey to a predominantly user-revenue-based model is hard.  But in reality, we have a simple, market-driven, regulatory method that has already proven its success in addressing a similarly challenging problem – forcing automakers to increase the fuel efficiency of the cars they make. Government has for years required staged multi-year increases in Corporate Average Fuel Efficiency. A similar strategy can be applied here.

This market-driven strategy does not mandate how to fix things. It instead mandates a measurable limit on the systems that have been shown to cause harm.  Each service provider can determine on their own how best to achieve that.  Require that X% of the revenue of any consumer data service come from its users rather than advertisers.  Government can monitor their progress, and create a timetable for steadily ratcheting up the percentage.  (This might apply only above some amount of revenues, to limit constraints on small, innovative competitors.)

It is often said of our internet platforms that “if you are not the customer, you are the product.”  This concept may oversimplify, but it is deeply powerful.  With or without detailed regulations on privacy and data use, we need to shift platform incentives by making the user become the customer, increasingly over time.

Realigning incentives for ads and data.  Advertising can provide value to users – if it is targeted and executed in a way that is non-intrusive, relevant, and useful.  The best way to make advertising less extractive of user value is by quantifying a “reverse meter” that gives users credit for their attention and data.  Some services already offer users the option to pay in order to avoid or reduce ads (Spotify is one example).  That makes the user the customer. Both advertisers and the platforms benefit by managing user attention to maximize, rather than optimize for exploitive “engagement.”

What if the mandated user revenue level is not met?  Government could tax away enough ad revenue to meet the target percentage.  That would provide a powerful incentive to address the problem.  In addition, that taxed excess ad revenue could fund mechanisms for oversight and transparency, for developing better solutions, and for remediating disinformation.

Can the platforms really shift to user revenue?  Zuckerberg has been a skeptic, but none of the big platforms has tried seriously.  When the platforms realize they must make this change, they will figure out how, even if it trims their exorbitant margins.

Users increasingly recognize that they must pay for digital services.  A system of reverse metering of ads and data use would be a powerful start.  Existing efforts that hint at the ultimate potential of better models include including crowdfunding, membership models, and cooperatives. Other emerging variations promise to be adaptive to large populations of users with diverse value perceptions and abilities to pay.

A growing focus on customer value would move us back towards leveraging a proven great strength of humanity — the deeply cooperative behavior of traditional markets.

A simple mandate requiring internet platforms to generate a growing percentage of revenue from users will not cure all ills. But it is the simplest way to drive a fundamental shift toward better corporate behavior.

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Coda, 7/24/19:

Since the original publication of this article, this issue has become even more timely, as the FTC and Justice Department begin deep investigation into the Internet giants. 

• There is growing consensus that there is a fundamental problem with the ad- and data-based business model. 
• There is also growing consensus that we must move beyond the narrow theory of antitrust that says there can be no "harm" in a free service that does not raise direct costs to consumers (but does raise indirect costs to them and limits competition). 
• But the targeted strategies for forcing a fundamental shift in business models outlined here are still not widely known or considered. 

For more information see my Open Letter to Influencers Concerned About Facebook and Other Platforms. 

• It primarily focuses on these business model issues and regulatory strategies (including the auto emissions model described here), and how FairPay offers an innovative strategy that has gained recognition for how it can generate user revenue in equitable ways that do not prevent a service like Facebook or Google from being affordable by all, even those with limited ability to pay.
• It also links to a body of work "On the deeper issues of social media and digital democracy." That includes Google-like algorithms for getting smarter about the wisdom of crowds, and structural strategies for regulation based on the specific architecture of the platforms and how power should be modularized (much as smart modularization was applied to regulating the Bell System and enabling the decades of robust innovation we now enjoy.)

Reisman in Techonomy: Business Growth is Actually Good for People. Here’s Why.

My 4th piece in Techonomy was published today:
Business Growth is Actually Good for People. Here’s Why..

Blurb:

We cannot—and should not—stop growing. Sensible people know the real task is to channel growth to serve human ends.

My opening and closing:

Douglas Rushkoff gave a characteristically provocative talk last week at Techonomy NYC – which provoked me to disagree strongly...

...Rushkoff delivers a powerful message on the need to re-center on human values. But his message would be more effective if it acknowledged the power of technology and growth instead of indiscriminately railing against it. We need a reawakening of human capitalism — and a Manhattan Project to get tech back on track. That will make us a better team human.

Zucked -- Roger McNamee's Wake Up Call ...And Beyond

Zucked: Waking Up to the Facebook Catastrophe is an authoritative and frightening call to arms -- but I was disappointed that author Roger McNamee did not address some of the suggestions for remedies that I shared with him last June (posted as An Open Letter to Influencers Concerned About Facebook and Other Platforms).

Here are brief comments on this excellent book, and highlights of what I would add. Many recognize the problem with the advertising-based business model, but few seem to be serious about finding creative ways to solve it. It is not yet proven that my suggestions will work quite as I envision, but the deeper need is to get people thinking about finding and testing more win-win solutions. His book makes a powerful case for why this is urgently needed.

McNamee's urgent call to action

McNamee offers the perspective of a powerful Facebook and industry insider. A prominent tech VC, he was an early investor and mentor to Zuckerberg -- the advisor who suggested that he not sell to Yahoo, and who introduced him to Sandberg. He was alarmed in early-mid 2016 by early evidence of manipulation affecting the UK and US elections, but found that Zuckerberg and Sandberg were unwilling to recognize and act on his concerns. As he became more concerned, he joined with others to raise awareness of this issue and work to bring about needed change.

He provides a rich summary of how we got here, most of the issues we now face, and the many prominent voices for remedial action. He addresses the business issues and the broader questions of governance, democracy, and public policy. He tells us: “A dystopian technology future overran our lives before we were ready.” (As also quoted in the sharply favorable NY Times review.)

It's the business model, stupid!

McNamee adds his authoritative voice to the many observers who have concluded that the business model that serves advertisers to enable consumers to obtain "free" services distorts incentives, causing businesses to optimize for advertisers, not for users:

Without a change in incentives, we should expect the platforms to introduce new technologies that enhance their already-pervasive surveillance capabilities...the financial incentives of advertising business models guarantee that persuasion will always be the default goal of every design."

He goes on to suggest:

The most effective path would be for users to force change. Users have leverage...

The second path is government intervention. Normally I would approach regulation with extreme reluctance, but the ongoing damage to democracy, public health, privacy, and competition justifies extraordinary measures. The first step would be to address the design and bushiness model failures that make internet platforms vulnerable to exploitation. ...Facebook and Google have failed at self-regulation.

My suggestions on the business model, and related regulatory action

This is where I have novel suggestions -- outlined on my FairPayZone blog, and communicated to McNamee last June -- that have not gotten wide attention, and are ignored in Zucked. These are at two levels.

The auto emissions regulatory strategy. This is a simple, proven regulatory approach for forcing Facebook (and similar platforms) to shift from advertising-based revenue to user-based revenue. That would fundamentally shift incentives from user manipulation to user value.

If Facebook or other consumer platforms fail to move to do that voluntarily, this simple regulatory strategy could force that -- in a market-driven way. The government could simply mandate that X% of their revenue must come from their users -- with a timetable for gradually increasing X.  This is how auto emissions mandates work -- don't mandate how to fix things, just mandate a measurable result, and let the business figure out how best to achieve that. Since reverse-metered ads (with a specific credit against user fees) would count as a form of reader revenue, that would provide an immediate incentive for Facebook to provide such compensation -- and to begin developing other forms of user revenue. This strategy is outlined in Privacy AND Innovation ...NOT Oligopoly -- A Market Solution to a Market Problem.

The deeper shift to user revenue models. Creative strategies can enable Facebook (and other businesses) to shift from advertising revenue to become substantially user-funded. Zuckerberg has
thrown up his hands at finding a better way: "I don’t think the ad model is going to go away, because I think fundamentally, it’s important to have a service like this that everyone in the world can use, and the only way to do that is to have it be very cheap or free."

Who Should Pay the Piper for Facebook? (& the rest), explains this new business model architecture -- with a focus on how it can be applied to let Facebook be "cheap or free" for those who get limited value and have limited ability to pay, but still be paid for, at fair levels for those who get more value and who are willing and able to pay for that. This architecture, called FairPay, has gained recognition for operationalizing a solution that businesses can begin to apply now.

• A reverse meter for ads and data. This FairPay architecture still allows for advertising to continue to defray the cost of service, but on a more selective, opt-in basis --  by applying a "reverse meter" that credits the value of user attention and data against each user's service fees -- at agreed upon terms and rates. That shifts the game from the advertiser being the customer of the platform, to to the advertiser being the customer of the user (facilitated by the platform). In that way advertising is carried only if done in a manner that is acceptable to the user. That aligns the incentives of the user, the advertiser, and the platform. Others have proposed similar directions, but I take it farther, in ways that Facebook could act on now.

• A consumer-value-first model for user-revenue. Reverse metering is a good starting place for re-aligning incentives, but Facebook can go much deeper, to transform how its business operates.The simplest introduction to the transformative twist of the FairPay strategy is in my Techonomy article, Information Wants to be Free; Consumers May Want to Pay   (It has also been outlined in in Harvard Business Review, and more recently in the Journal of Revenue and Pricing Management.) The details will depend on context, and will need testing to fully develop and refine over time, but the principles are clear and well supported.
  
  This involves ways to mass-customize pricing of Facebook, to be "cheap or free" where appropriate, and to set customized fair prices for each user who obtain real value and can be enticed to pay for that. That is adaptive to individual usage and value-- and eliminates the risk of having to pay when the value actually obtained did not warrant that. That aligns incentives for transparency, trust, and co-creation of real value for each user. Behavioral economics has shown that people are willing to pay and will do so even voluntarily -- when they see good reason to help sustain the creation of value that they actually want and receive. We just need business models that understand and build on that.

Bottom line. Whatever the details, unless the Facebook shifts direction on its own to aggressively move in the direction of user payments -- which now seems unlikely -- regulatory pressure will be needed to force that (just as with auto emissions). A user revolt might force similar changes as well, but the problem is far too urgent to wait and see.

The broader call -- augmenting the wisdom of crowds

Shifting to a user-revenue-based business model will change incentives and drive significant progress to remedy many of the problems that McNamee and many others have raised. McNamee provides a wide-ranging overview of many of those problems and most of the initiatives that promise to help resolve them, but there, too, I offer suggestions that have not gained attention.

Most fundamental is the power of social media platforms to shape collective intelligence. Many have come to see that, while technology has great power to augment human intelligence, applied badly, it can have the opposite effect of making us more stupid. We need to steer hard for a more positive direction, now that we see how dangerous it is to take good results for granted, and how easily things can go bad. McNamee observes that "We...need to address these problems the old fashioned way, by talking to one another and finding common ground." Effective social media design can help us do that.

Another body of my work relates to how to design social media feeds and filtering algorithms to do just that, as explained in The Augmented Wisdom of Crowds:  Rate the Raters and Weight the Ratings:

• The core issue is one of trust and authority -- it is hard to get consistent agreement in any broad population on who should be trusted or taken as an authority, no matter what their established credentials or reputation. Who decides what is fake news? What I suggested is that this is the same problem that has been made manageable by getting smarter about the wisdom of crowds -- much as Google's PageRank algorithm beat out Yahoo and AltaVista at making search engines effective at finding content that is relevant and useful.
  
  As explained further in that post, the essence of the method is to "rate the raters" -- and to weight those ratings accordingly. Working at Web scale, no rater's authority can be relied on without drawing on the judgement of the crowd. Furthermore, simple equal voting does not fully reflect the wisdom of the crowd -- there is deeper wisdom about those votes to be drawn from the crowd.
  
  Some of the crowd are more equal than others. Deciding who is more equal, and whose vote should be weighted more heavily can be determined by how people rate the raters -- and how those raters are rated -- and so on. Those ratings are not universal, but depend on the context: the domain and the community -- and the current intent or task of the user. Each of us wants to see what is most relevant, useful, appealing, or eye-opening -- for us -- and perhaps with different balances at different times. Computer intelligence can distill those recursive, context-dependent ratings, to augment human wisdom.

• A major complicating issue is that of biased assimilation. The perverse truth seems to be that "balanced information may actually inflame extreme views." This is all too clear in the mirror worlds of pro-Trump and anti-Trump factions and their media favorites like Fox, CNN, and MSNBC. Each side thinks the other is unhinged or even evil, and layers a vicious cycle of distrust around anything they say. It seems one of the few promising counters to this vicious cycle is what Cass Sunstein referred to as surprising validators: people one usually gives credence to, but who suggest one's view on a particular issue might be wrong. An example of a surprising validator was the "Confession of an Anti-GMO Activist." This item is  readily identifiable as a "turncoat" opinion that might be influential for many, but smart algorithms can find similar items that are more subtle, and tied to less prominent people who may be known and respected by a particular user. There is an opportunity for electronic media services to exploit this insight that "what matters most may be not what is said, but who, exactly, is saying it."

If and when Facebook and other platforms really care about delivering value to their users (and our larger society), they will develop this kind of ability to augment the wisdom of the crowd. (Similar large-scale ranking technology is already proven in uses for advertising and Google search.) Our enlightened, democratic civilization will disintegrate or thrive, depending on whether they do that.

The facts of the facts. One important principle which I think McNamee misunderstands (as do many), is his critique that "To Facebook, facts are not absolute; they are a choice to be left initially to users and their friends but then magnified by algorithms to promote engagement." Yes, the problem is that the drive for engagement distorts our drive for the facts -- but the problem is not that "To Facebook, facts are not absolute." As I explain in The Tao of Fake News, facts are not absolute --we cannot rely on expert authorities to define absolute truth -- human knowledge emerges from an adaptive process of collective truth-seeking by successive approximation and the application of collective wisdom. It is always contingent on that, not absolute. That is how scholarship and science and democratic government work, that is what the psychology of cognition and knowledge demonstrates, and that is what effective social media can help all of us do better.

Other monopoly platform excesses - openness and interoperability

McNamee provides a good survey of many of the problems of monopoly (or oligopoly) power in the platforms, and some of the regulatory and antitrust remedies that are needed to restore the transparency, openness, and flexibility and market-driven incentives needed for healthy innovation. These include user ownership of their data and metadata, portability of the users' social graphs to promote competition, and audits and transparency of algorithms.

I have addressed similar issues, and go beyond McNamee's suggestions to emphasize the need for openness and interoperability of competing and complementary services -- see Architecting Our Platforms to Better Serve Us -- Augmenting and Modularizing the Algorithm. This draws on my early career experience watching antitrust regulatory actions relating to AT&T (in the Bell System days), IBM (in the mainframe era), and Microsoft (in the early Internet browser wars).

The wake up call

There are many prominent voices shouting wake up calls. See the partial list at the bottom of An Open Letter to Influencers Concerned About Facebook and Other Platforms, and MacNamee's Bibliographic Essay at the end of Zucked (excellent, except for the omission that I address here).

All are pointing in much the same direction. We all need to do what we can to focus the powers that be -- and the general population -- to understand and address this problem. The time to turn this rudderless ship around is dangerously short, and effective action to set a better direction and steer for it has barely begun. We have already sailed blithely into killer icebergs, and many more are ahead.

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This is cross-posted from both of my blogs, FairPayZone.com and Reisman on User-Centered Media, which delve further into these issues.

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See the Selected Items tab for more on this theme.

In the War on Fake News, All of Us are Soldiers, Already!

This is intended as a supplement to my posts "A Cognitive Immune System for Social Media -- Developing Systemic Resistance to Fake News" and "The Augmented Wisdom of Crowds: Rate the Raters and Weight the Ratings." (But hopefully this stands on its own as well).Maybe this can make a clearer point of why the methods I propose are powerful and badly needed...
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A NY Times article titled "Soldiers in Facebook’s War on Fake News Are Feeling Overrun" provides a simple context for showing how I propose to use information already available from all of us, on what is valid and what is fake.

The Times article describes a fact checking organization that works with Facebook in the Philippines (emphasis added):

On the front lines in the war over misinformation, Rappler is overmatched and outgunned - and that could be a worrying indicator of Facebook’s effort to curb the global problem by tapping fact-checking organizations around the world.

...it goes on to describe what I suggest is the heart of the issue:

When its fact checkers determine that a story is false, Facebook pushes it down on users’ News Feeds in favor of other material. Facebook does not delete the content, because it does not want to be seen as censoring free speech, and says demoting false content sharply reduces abuse. Still, falsehoods can resurface or become popular again.

The problem is that the fire hose of fake news is too fast and furious, and too diverse, for any specialized team of fact-checkers to keep up with it. Plus, the damage is done by the time they do identify the fakes and begin to demote them.

But we are all fact checking to some degree without even realizing it. We are all citizen-soldiers. Some do it better than others.

The trick is to draw out all of the signals we provide, in real time -- and use our knowledge of which users' signals are reliable -- to get smarter about what gets pushed down and what gets favored in our feeds. That can serve as a systemic cognitive immune system -- one based on rating the raters and weighting the ratings.

We are all rating all of our news, all of the time, whether implicitly or explicitly, without making any special effort:

• When we read, "like," comment, or share an item, we provide implicit signals of interest, and perhaps approval.
• When we comment or share an item, we provide explicit comments that may offer supplementary signals of approval or disapproval.
• When we ignore an item, we provide a signal of disinterest (and perhaps disapproval).
• When we return to other activity after viewing an item, the time elapsed signals our level of attention and interest.

Individually, inferences from the more implicit signals may be erratic and low in meaning. But when we have signals from thousands of people, the aggregate becomes meaningful. Trends can be seen quickly. (Facebook already uses such signals to target its ads -- that is how they makes so much money).

But simply adding all these signals can be misleading. 

• Fake news can quickly spread through groups who are biased (including people or bots who have an ulterior interest in promoting an item) or are simply uncritical and easily inflamed -- making such an item appear to be popular.
• But our platforms can learn who has which biases, and who is uncritical and easily inflamed.
• They can learn who is respected within and beyond their narrow factions, and who is not, who is a shill (or a malicious bot) and who is not.
• They can use this "rating" of the raters to weight their ratings higher or lower.

Done at scale, that can quickly provide probabilistically strong signals that an item is fake or misleading or just low quality. Those signals can enable the platform to demote low quality content and promote high quality content. 

To expand just a bit:

• Facebook can use outside fact checkers, and can build AI to automatically signal items that seem questionable as one part of its defense.
• But even without any information at all about the content and meaning of an item, it can make realtime inferences about its quality based on how users react to it.
• If most of the amplification is from users known to be malicious, biased, or unreliable it can downrank items accordingly
• It can test that downranking by monitoring further activity.
• It might even enlist "testers" by promoting a questionable item to users known to be reliable, open, and critical thinkers -- and may even let some generally reliable users to self-select as validators (being careful not to overload them).
• By being open-ended in this way, such downranking is not censorship -- it is merely a self-regulating learning process that works at Internet scale, on Internet time.

That is how we can augment the wisdom of the crowd -- in real time, with increasing reliability as we learn. That is how we build a cognitive immune system (as my other posts explain further).

This strategy is not new or unproven. It is is the core of Google's wildly successful PageRank algorithm for finding useful search results. And (as I have noted before), it was recently reported that Facebook is now beginning to do a similar, but apparently still primitive form of rating the trustworthiness of its users to try to identify fake news -- they track who spreads fake news and who reports abuse truthfully or deceitfully.* 

What I propose is that we take this much farther, and move rapidly to make it central to our filtering strategies for social media -- and more broadly. An all out effort to do that quickly may be our last, best hope for enlightened democracy.

Related posts:

• 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.
• 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 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).
• 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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(*More background from Facebook on their current efforts was cited in the Times article: Hard Questions: What is Facebook Doing to Protect Election Security?

[Update 10/12:] A subsequent Times article by Sheera Frenkel, adds perspective on the scope and pace of the problem -- and the difficulty in definitively identifying items as fakes that can rightly be censored "because of the blurry lines between free speech and disinformation" -- but such questionable items can be down-ranked.

A Cognitive Immune System for Social Media -- Developing Systemic Resistance to Fake News

To counter the spread of fake news, it's more important to manage and filter its spread than to try to interdict its creation -- or to try to inoculate people against its influence. 

A recent NY Times article on their inside look at Facebook's election "war room" highlights the problem, quoting cybersecurity expert Priscilla Moriuchi:

If you look at the way that foreign influence operations have changed these last two years, their focus isn’t really on propagating fake news anymore. “It’s on augmenting stories already out there which speak to hyperpartisan audiences.”

That is why much of the growing effort to respond to the newly recognized crisis of fake news, Russian disinformation, and other forms of disruption in our social media fails to address the core of the problem. We cannot solve the problem by trying to close our systems off from fake news, nor can we expect to radically change people's natural tendency toward cognitive bias. The core problem is that our social media platforms lack an effective "cognitive immune system" that can resist our own tendency to spread the "cognitive pathogens" that are endemic in our social information environment.

Consider how living organisms have evolved to contain infections. We did that not by developing impermeable skins that could be counted on to keep all infections out, nor by making all of our cells so invulnerable that they can resist whatever infectious agents may unpredictably appear.

We have powerfully complemented what we can do in those ways by developing a richly nuanced internal immune system that is deeply embedded throughout our tissues. That immune system uses emergent processes at a system-wide level -- to first learn to identify dangerous agents of disease, and then to learn how to resist their replication and virulence as they try to spread through our system.

The problem is that our social media lack an effective "cognitive immune system" of this kind. 

In fact many of our social media platforms are designed by the businesses that operate them to maximize engagement so they can sell ads. In doing so, they have learned that spreading incendiary disinformation that makes people angry and upset, polarizing them into warring factions, increases their engagement. As a result, these platforms actually learn to spread disease rather than to build immunity. They learn to exploit the fact that people have cognitive biases that make them want to be cocooned in comfortable filter bubbles and feel-good echo-chambers, and to ignore and refute anything that might challenge beliefs that are wrong but comfortable. They work against our human values, not for them.

What are we doing about it? Are we addressing this deep issue of immunity, or are we just putting on band-aids and hoping we can teach people to be smarter? (As a related issue, are we addressing the underlying issue of business model incentives?) Current efforts seem to be focused on measures at the end-points of our social media systems:

• Stopping disinformation at the source. We certainly should apply band-aids to prevent bad-actors from injecting our media with news, posts, and other items that are intentionally false and dishonest. Of course we should seek to block such items and those who inject them. Band-aids are useful when we find an open wound that germs are gaining entry through. But band-aids are still just band-aids.

• Making it easier for individuals to recognize when items they receive may be harmful because they are not what they seem. We certainly should provide "immune markers" in the form of consumer-reports-like ratings of items and of the publishers or people who produce them (as many are seeking to do). Making such markers visible to users can help prime them to be more skeptical, and perhaps apply more critical thinking -- much like applying an antiseptic. But that depends on the willingness of users to pay attention to such markers and apply the antiseptic. There is good reason to doubt that will have more than modest effectiveness, given people's natural laziness and instinct for thinking fast rather than slow. (Many social media users "like" items based only on click-bait headlines that are often inflammatory and misleading, without even reading the item -- and that is often enough to cause those items to spread massively.)

These end-point measures are helpful and should be aggressively pursued, but we need to urgently pursue a more systemic strategy of defense. We need to address the problem of dissemination and amplification itself. We need to be much smarter about what gets spread -- from whom, to whom, and why.

Doing that means getting deep into the guts of how our media are filtered and disseminated, step by step, through the "viral" amplification layers of the media systems that connect us. That means integrating a cognitive immune system into the core of our social media platforms. Getting the platform owners to buy in to that will be challenging, but it is the only effective remedy.

Building a cognitive immune system -- the biological parallel

This perspective comes out of work I have been doing for decades, and have written about on this blog (and in a patent filing since released into the public domain). That work centers on ideas for augmenting human intelligence with computer support. More specifically, it is centers on augmenting the wisdom of crowds. It is based on the idea the our wisdom is not the simple result of a majority vote -- but results from an emergent process that applies smart filters that rate the raters and weight the ratings. That provides a way to learn which votes should be more equal than others (in a way that is democratic and egalitarian, but also merit-based). This approach is explained in the posts listed below. It extends an approach that has been developing for centuries.

Supportive of those perspectives, I recently turned to some work on biological immunity that uses the term "cognitive immune system." That work highlight the rich informational aspects of actual immune systems, as a model for understanding how these systems work at a systems level. As noted in one paper (see longer extract below*), biological immune systems are "cognitive, adaptive, fault-tolerant, and fuzzy conceptually." I have only begun to think about the parallels here, but it is apparent that the system architecture I have proposed in my other posts is at least broadly parallel, being also "cognitive, adaptive, fault-tolerant, and fuzzy conceptually." (Of course being "fuzzy conceptually" makes it not the easiest thing to explain and build, but when that is the inherent nature of the problem, it may also necessarily be the essential nature of the solution -- just as it is for biological immune systems.)

An important aspect of this being "fuzzy conceptually," is what I call The Tao of Truth. We can't definitively declare good-faith "speech" as "fake" or "false" in the abstract. Validity is "fuzzy" because it depends on context and interpretation. ("Fuzzy logic" recognizes that in the real world, it is often the case that facts are not entirely true or false but, rather, have degrees of truth.)  That is why only the clearest cases of disinformation can be safely cut off at the source. But we can develop a robust system for ranking the probable (fuzzy) value and truthfulness of speech, revising those rankings, and using that to decide how to share it with whom. For practical purposes, truth is a filtering process, and we can get much smarter about how we apply our collective intelligence to do our filtering. It seems the concepts of "danger" and "self/not-self" in our immune systems have a similarly fuzzy Tao -- many denizens of our microbiome that are not "self" are beneficial to us, and our immune systems have learned that we live better with them inside of us.

My proposals

Expansion on the architecture I have proposed for a cognitive immune system -- and the need for it -- are here:

• The Augmented Wisdom of Crowds: Rate the Raters and Weight the Ratings – an overall architecture for social media (and for digital democracy more broadly) that includes a cognitive immune system that is "cognitive, adaptive, fault-tolerant, and fuzzy conceptually."

• 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.

• 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).

• Filtering for Serendipity -- Extremism, 'Filter Bubbles' and 'Surprising Validators – specific approaches to embracing the fuzziness/Tao of truth that can break through the cognitive bias of our filter bubbles (and can also help with the related problem of augmenting serendipity).

• 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.

(These works did not explicitly address the parallels with biological cognitive immune systems -- exploring those parallels might well lead to improvements on these strategies.)

To those without a background in the technology of modern information platforms, this brief outline may seem abstract and unclear. But as noted in these more detailed posts, these methods are a generalization of methods used by Google (in its PageRank algorithm) to do highly context-relevant filtering of search results using a similar rate the raters and weight the ratings strategy. (That is also "cognitive, adaptive, fault-tolerant, and fuzzy conceptually.") These methods not simple, but they are little stretch from the current computational methods of search engines, or from the ad targeting methods already well-developed by Facebook and others. They can be readily applied -- if the platforms can be motivated to do so.

Broader issues of support for our cognitive immune system

The issue of motivation to do this is crucial. For the kind of cognitive immune system I propose to be effective, it must be built deeply into the guts of our social media platforms (whether directly, or via APIs). As noted above, getting incumbent platforms to shift their business models to align their internal incentives with that need will be challenging. But I suggest it need not be as difficult as it might seem.

• For some innovative strategies on realigning incentives, please see my Open Letter to Influencers Concerned About Facebook and Other Platforms.

A related non-technical issue that many have noted is the need for education of citizens 1) in critical thinking, and 2) in the civics of our democracy. Both seem to have been badly neglected in recent decades. Aggressively remedying that is important, to help inoculate users from disinformation and sloppy thinking -- but that will have limited effectiveness unless we alter the overwhelmingly fast dynamics of our information flows (with the cognitive immune system suggested here) -- to help make us smarter, not dumber in the face of this deluge of information.

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[Update 10/12:] A subsequent Times article by Sheera Frenkel, adds perspective on the scope and pace of the problem -- and the difficulty in definitively identifying items as fakes that can rightly be censored "because of the blurry lines between free speech and disinformation" -- but such questionable items can be down-ranked.
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*Background on our Immune Systems -- from the introduction to the paper mentioned above, "A Cognitive Computational Model Inspired by the Immune System Response" (emphasis added):

The immune system (IS) is by nature a highly distributed, adaptive, and self-organized system that maintains a memory of past encounters and has the ability to continuously learn about new encounters; the immune system as a whole is being interpreted as an intelligent agent. The immune system, along with the central nervous system, represents the most complex biological system in nature [1]. This paper is an attempt to investigate and analyze the immune system response (ISR) in an effort to build a framework inspired by ISR. This framework maintains the same features as the IS itself; it is cognitive, adaptive, fault-tolerant, and fuzzy conceptually. The paper sets three phases for ISR operating sequentially, namely, “recognition,” “decision making,” and “execution,” in addition to another phase operating in parallel which is “maturation.” This paper approaches these phases in detail as a component based architecture model. Then, we will introduce a proposal for a new hybrid and cognitive architecture inspired by ISR. The framework could be used in interdisciplinary systems as manifested in the ISR simulation. Then we will be moving to a high level architecture for the complex adaptive system. IS, as a first class adaptive system, operates on the body context (antigens, body cells, and immune cells). ISR matured over time and enriched its own knowledge base, while neither the context nor the knowledge base is constant, so the response will not be exactly the same even when the immune system encounters the same antigen. A wide range of disciplines is to be discussed in the paper, including artificial intelligence, computational immunology, artificial immune system, and distributed complex adaptive systems. Immunology is one of the fields in biology where the roles of computational and mathematical modeling and analysis were recognized...

The paper supposes that immune system is a cognitive system; IS has beliefs, knowledge, and view about concrete things in our bodies [created out of an ongoing emergent process], which gives IS the ability to abstract, filter, and classify the information to take the proper decisions.

The Tao of Fake News / The Tao of Truth

We are smarter than this!

Everyone with any sense sees "fake news" disinformation campaigns as an existential threat to "truth, justice, and the American Way," but we keep looking for a Superman to sort out what is true and what is fake. A moment's reflection shows that, no Virginia, there is no SuperArbiter of truth. No matter who you choose to check or rate content, there will always be more or less legitimate claims of improper bias.

• We can't rely on "experts" or "moderators" or any kind of "Consumer Reports" of news. We certainly can't rely on the Likes of the Crowd, a simplistic form of the Wisdom of the Crowd that is too prone to "The Madness of Crowds." 
• But we can Augment the Wisdom of the Crowd.

• We can't definitively declare good-faith "speech" as "fake" or "false." 
• But we can develop a robust system for ranking the probable value and truthfulness of speech, revising those rankings, and using that to decide how to share it with whom.

For practical purposes, truth is a filtering process, and we can get much smarter about how we apply our collective intelligence to do our filtering.

The Tao of Fake News, Truth, and Meaning

Truth is a process. Truth is complex. Truth depends on interpretation and context. Meaning depends on who is saying something, to whom, and why (as Humpty-Dumpty observed). The truth in Rashomon is different for each of the characters. Truth is often very hard for individuals (even "experts") to parse.

Truth is a process, because there is no practical way to ensure that people speak the truth, nor any easy way to determine if they have spoken the truth. Many look to the idea of flagging fake news sources, but who judges, on what basis and what aspects? (A recent NeimanLab assessment of NewsGuard's attempt to do this shows how open to dispute even well-funded, highly professional efforts to do that are.)

Truth is a filtering process: How do we filter true speech from false speech? Over centuries we have come to rely on juries and similar kinds of panels, working in a structured process to draw out and "augment" the collective wisdom of a small crowd. In the sciences, we have a more richly structured process for augmenting the collective wisdom of a large crowd of scientists (and their experiments), informally weighing the authority of each member of the crowd -- and avoiding over-reliance on a few "experts." Our truths are not black and white, absolute, and eternal -- they are contingent, nuanced, and tentative -- but this Tao of truth has served us well.

It is now urgent that our methods for augmenting and filtering our collective wisdom be enhanced. We need to apply computer-mediated collaboration to apply a similar augmented wisdom of the crowd at Internet scale and speed. We can make quick initial assessments, then adapt, grow, and refine our assessments of what is true, in what way, and with regard to what.

Filtering truth -- networks, context, and community

If our goal is to exclude all false and harmful material, we will fail. The nature of truth, and of human values, is too complex. We can exclude the most obviously pernicious frauds -- but for good-faith speech from humans in a free society, we must rely on a more nuanced kind of wisdom.

Our media filter what we see. Now the filters in our dominant social media are controlled by a few corporations motivated to maximize ad revenue by maximizing engagement. They work to serve the advertisers that are their customers, not we users (who now are really their product). We need to get them to change how the filters operate, to maximize value to their users.

We need filters to be tuned to the real value of speech as communication from one person to other people.  Most people want the "firehose" of items on the Internet to be filtered in some way, but just how may vary. Our filters need to be responsive to the desires of the recipients. Partisans may like the comfort of their distorting filter bubbles, but most people will want at least some level of value, quality, and reality, at least some of the time. We can reinforce that by doing well at it.

There is also the fact that people live in communities. Standards for what is truthful and valuable vary from community to community -- and communities and people change over time. This is clearer than ever, now that our social networks are global.

Freedom of speech requires that objectionable speech be speak-able, with very narrow exceptions. The issue is who hears that speech, and what control do they have over what they hear. A related issues is when do third parties have a right to influence those listener choices, and how to keep that intrusive hand as light as possible. Some may think we should never see a swastika or a heresy, but who has the right to draw such lines for everyone in every context?

We cannot shut off objectionable speech, but we can get smarter about managing how it spreads. 

To see this more clearly, consider our human social network as a system of collective intelligence, one that informs an operational definition of truth. Whether at the level of a single social network like Facebook, or all of our information networks, we have three kinds of elements:

• Sources of information items (publishers, ordinary people, organizations, and even bots) 
• Recipients of information items  
• Distribution systems that connect the sources and recipients using filters and presentation service that determine what we see and how we see it (including optional indicators of likely truthfulness, bias, and quality).

Controlling truth at the source may, at first, seem the simple solution, but requires a level of control of speech that is inconsistent with a free society. Letting misinformation and harmful content enter our networks may seem unacceptable, but (with narrow exceptions) censorship is just not a good solution.

Some question whether it is enough to "downrank" items in our feeds (not deleted, but less likely to be presented to us), but what better option do we have than to do that wisely? The best we can reasonably do is manage the spread of low quality and harmful information in a way that is respectful of the rights of both sources and recipients, to limit harm and maximize value.*

How can we do that, and who should control it? We, the people, should control it ourselves (with some limited oversight and support).  Here is how.

Getting smarter -- The Augmented Wisdom of Crowds

Neither automation nor human intelligence alone is up to the scale and dynamics of the problem.  We need a computer-augmented approach to managing the wisdom of the crowd -- as embodied in our filters, and controlled by us. That will pull in all of the human intelligence we can access, and apply algorithms and machine learning (with human oversight) to refine and apply it. The good news is that we have the technology to do that. It is just a matter of the will to develop and apply it.

My previous post outlines a practical strategy for doing that -- "The Augmented Wisdom of Crowds: Rate the Raters and Weight the Ratings." Google has already shown how powerful a parallel form of this strategy can be to filter which search results should be presented to whom-- on Internet scale. My proposal is to broaden these methods to filter what our our social media present to us.

The method is one of considering all available "signals" in the network and learning how to use them to inform our filtering process. The core of the information filtering process -- that can be used for all kinds of media, including our social media -- is to use all the data signals that our media systems have about our activity. We can consider activity patterns across these three dimensions:

• Information items (content of any kind, including news items, personal updates, comments/replies, likes, and shares/retweets).
• Participants (and communities and sub-communities of participants), who can serve as both sources and recipients of items (and of items about other items)
• Subject and task domains (and sub-domains) that give important context to information items and participants.

We can apply this data with the understanding that any item or participant can be rated, and any item can contain one or more ratings (implicit or explicit) of other items and/or participants. The trick is to tease out and make sense of all of these interrelated ratings and relationships. To be smart about that, we must recognize that not all ratings are equal, so we "rate the raters, and weight the ratings" (using any data that signals a rating). We take that to multiple levels -- my reputational authority depends not only on the reputational authority of those who rate me, but on those who rate them (and so on).

This may seem very complicated (and at scale, it is), but Google proved the power of such algorithms to determine which search results are relevant to a user's query (at mind-boggling scale and speed). Their PageRank algorithm considers what pages link to a given page to assess the imputed reputational authority of that page -- with weightings based on the imputed authority of the pages that link to it (again to multiple levels). Facebook uses similarly sophisticated algorithms to determine what ads should be targeted to whom -- tracking and matching user interests, similarities, and communities and matching that with information on their response to similar ads.

In some encouraging news, it was recently reported that Facebook is now also doing a very primitive form of rating the trustworthiness of its users to try to identify fake news -- they track who spreads fake news and who reports abuse truthfully or deceitfully. What I propose is that we take this much farther, and make it central to our filtering strategies for social media and more broadly.

With this strategy, we can improve our media filters to better meet our needs, as follows:

• Track explicit and implicit signals to determine authority and truthfulness -- both of the speakers (participants) and of the things they say (items) -- drawing on the wisdom of those who hear and repeat it (or otherwise signal how they value it).
• Do similar tracking to understand the desires and critical thinking skills of each of the recipients
• Rate the raters (all of us!) -- and weight the votes to favor those with better ratings. Do that n-levels deep (much as Google does).
• Let the users signal what levels and types of filtering they want. Provide defaults and options to accommodate users desiring different balances of ease or of fine control and reporting. Let users change that as they desire, depending on their wish to relax, to do focused critical thinking, or to open up to serendipity.
• Provide transparency and auditability -- to each user (and to independent auditors) -- as to what is filtered for them and how.**
• Open the filtering mechanisms to independent providers, to spur innovation in a competitive marketplace in filtering algorithms for users to choose from.

That is the best broad solution that we can apply. As we get good at it we will be amazed at how effective it can be. But given the catastrophic folly of where have have let this get to...

First, do no harm!

Most urgently, we need to change the incentives of our filters to do good, not harm. At present, our filters are pouring gasoline on the fires (even as their corporate owners claim to be trying to put them out). As explained in a recent HBR article, "current digital advertising business models incentivize the spread of false news." That article explains the insidious problem of the ad model for paying for services (others have called it "the original sin of the Web") and offers some sensible remedies.  

I have proposed more innovative approaches to better-aligning business models -- and to using a light-handed, market-driven, regulatory approach to mandate doing that -- in "An Open Letter to Influencers Concerned About Facebook and Other Platforms."

We have learned that the Internet has all the messiness of humanity and its truths. We are facing a Pearl Harbor of a thousand pin-pricks that is rapidly escalating. We must mobilize onto a war footing now, to halt that before it is too late.

• First we need to understand the nature and urgency of this threat to democracy, 
• Then we must move on both short and longer time horizons to slow and then reverse the threat. 

The Tao of fake news contains its opposite, the Tao of Augmented Wisdom. If we seek that, the result will be not only to manage fake news, but to be smarter in our collective wisdom than we can now imagine.

Related posts:

• 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.
• 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.
• 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.
• 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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*Of course some information items will be clearly malicious, coming from fraudulent human accounts or bots -- and shutting some of that off at the source is feasible and desirable. But much of the spread of "fake news" (malicious or not) is from real people acting in good faith, in accord with their understanding and beliefs. We cannot escape that non-binary nature of human reality, and must come to terms with our world in nuanced shades of gray. But we can get very sophisticated at distinguishing when news is spread by participants who are usually reliable from when it is spread by those who have established a reputation for being credulous, biased, or malicious.

**The usual concern with transparency is that if the algorithms are known, then bad-actors will game them. That is a valid concern, and some have suggested that even if the how of the filtering algorithm is secret, we should be able to see and audit the why for a given result.  But to the extent that there is an open market in filtering methods (and in countermeasures to disinformation), and our filters vary from user to user and time to time, there will be so much variability in the algorithms that it will be hard to game them effectively.

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[Update 8/30/18:]  Giuliani and The Tao of Truth 

To indulge in some timely musing, the Tao of Truth gives a perspective on the widely noted recent public statement that "truth isn't truth." At the level of the Tao, we can say that "truth is/isn't truth," or more precisely, "truth is/isn't Truth" (with one capital T). That is the level at which we understand truth to be a process in which the question "what is truth?" depends on what we mean, at what level, in what context, with what assurance -- and how far we are in that process. We as a society have developed a broadly shared expectation of how that process should work. But as the process does its never-ending work, there are no absolutes -- only more or less strong evidence, reasoning, and consensus about what we believe the relevant truth to be. (That, of course is an Enlightenment social perspective, and some disagree with this very process, and instead favor a more absolute and authoritarian variation. Perhaps most fundamentally, we are now in a reactionary time in which our prevailing process for truth is being prominently questioned. The hope here is that continuing development of a free, open, and wise process prevails over return to a closed, authoritarian one -- and prevails over the loss of any consensus at all.

[Update 10/12/18:] A Times article by Sheera Frenkel, adds perspective on the scope and pace of the problem -- and the difficulty in definitively identifying items as fakes that can be censored "because of the blurry lines between free speech and disinformation" -- but such questionable items can be down-ranked.

[Update 11/2/20:] A nice article on the importance of understanding the social nature of truth ("epistemic dependence" -- our reliance on others' knowledge -- "knowing vicariously"), and the interplay of evidence, trust, and authority, is in MIT Tech Review. It refers to a much-cited fundamental paper on epistemic dependence from 1985.

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