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.
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
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:
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.
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:
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.
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.
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!
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.
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.
*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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