Showing posts with label Google. Show all posts
Showing posts with label Google. Show all posts

Wednesday, August 12, 2020

Reverse the “Nuance Destruction Machine!”

Techonomy has just published my latest article on our social media disaster, Don’t Swim Against the Tide of “Nuance Destruction”
Social media is “a nuance destruction machine,” as Jeff Bezos concisely put it in his recent widely reported Congressional testimony.
...As long as social media’s financial incentives favor engagement (or “enragement”) over quality, its filtering algorithms will be designed to be favorable to messages of hate and fear. As long as that happens at Internet speed, bolted-on efforts to add back nuance and limit conflict will be futile. We will waste time and resources with little result, and democracy may drown in the undertow.
The article explains a two-part solution:
  1. Change the incentives.
  2. That will motivate platforms to redesign algorithms to filter for nuance — and against incivility and hate speech.
---
To go beyond the brief outline in the article, see this list of Selected Items.

Friday, January 10, 2020

The Dis-information Choke Point: Dis-tribution (Not Supply or Demand) [Stub]

Demand for Deceit: How the Way We Think Drives Disinformation, is an excellent report from the National Endowment for Democracy (by Samuel Woolley and Katie Joseff, 1/8/20). It highlights the dual importance of both supply and demand side factors in the problem of disinformation (fake news). That crystallizes in my mind an essential gap in this field -- smarter control of distribution. The importance of this third element that mediates between supply and demand was implicit in my comments on algorithms (in section #2 of the prior post).

[This is a stub for a fuller post yet to come. (It is an adaptation of a brief update to my prior post on Regulating the Platforms, but deserves separate treatment.)]

There is little fundamentally new about the supply or the demand for disinformation.  What is fundamentally new is how disinformation is distributed.  That is what we most urgently need to fix. If disinformation falls in a forest… but appears in no one’s feed, does it disinform?

In social media a new form of distribution mediates between supply and demand.  The media platform does filtering that upranks or downranks content, and so governs what users see.  If disinformation is downranked, we will not see it -- even if it is posted and potentially accessible to billions of people.  Filtered distribution is what makes social media not just more information, faster, but an entirely new kind of medium.  Filtering is a new, automated form of moderation and amplification.  That has implications for both the design and the regulation of social media.

[Update: see comments below on Facebook's 2/17/20 White Paper on Regulation.] 

Controlling the choke point

By changing social media filtering algorithms we can dramatically reduce the distribution of disinformation.  It is widely recognized that there is a problem of distribution: current social media promote content that angers and polarizes because that increases engagement and thus ad revenues.  Instead the services could filter for quality and value to users, but they have little incentive to do so.  What little effort they ever have made to do that has been lost in their quest for ad revenue.

Social media marketers speak of "amplification." It is easy to see the supply and demand for disinformation, but marketing professionals know that it is amplification in distribution that makes all the difference. Distribution is the critical choke point for controlling this newly amplified spread of disinformation. (And as Feld points out, the First Amendment does not protect inappropriate uses of loudspeakers.)

While this is a complex area that warrants much study, as the report observes, the arguments cited against the importance of filter bubbles in the box on page 10 are less relevant to social media, where the filters are largely based on the user’s social graph (who promotes items to be fed to them, in the form of posts, likes, comments, and shares), not just active search behavior (what they search for). 

Changing the behavior of demand is clearly desirable, but a very long and costly effort. It is recognized that we cannot stop the supply. But we can control distribution -- changing filtering algorithms could have significant impact rapidly, and would apply across the board, at Internet scale and speed -- if the social media platforms could be motivated to design better algorithms.

How can we do that? A quick summary of key points from my prior posts...

We seem to forget what Google’s original PageRank algorithm had taught us.  Content quality can be inferred algorithmically based on human user behaviors, without intrinsic understanding of the meaning of the content.  Algorithms can be enhanced to be far more nuanced.  The current upranking is based on likes from all of one’s social graph -- all treated as equally valid.  Instead, we can design algorithms that learn to recognize the user behaviors on page 8, to learn which users share responsibly (reading more than headlines and showing discernment for quality) and which are promiscuous (sharing reflexively, with minimal dwell time) or malicious (repeatedly sharing content determined to be disinformation).  Why should those users have more than minimal influence on what other users see?

The spread of disinformation could be dramatically reduced by upranking “votes” on what to share from users with good reputations, and downranking votes from those with poor reputations.  I explain further in A Cognitive Immune System for Social Media -- Developing Systemic Resistance to Fake News and In the War on Fake News, All of Us are Soldiers, Already!  More specifics on designing such algorithms is in The Augmented Wisdom of Crowds: Rate the Raters and Weight the Ratings.  Social media are now reflecting the wisdom of the mob -- instead we need to seek the wisdom of the smart crowd.  That is what society has sought to do for centuries.

Beyond that, better algorithms could combat the social media filter bubble effects by applying measures that apply judo to the active drivers noted on page 8.  Cass Sunstein suggested “surprising validators” in 2012 one way this might be done, and I built on that to explain how that could be applied in social media algorithms:  Filtering for Serendipity -- Extremism, 'Filter Bubbles' and 'Surprising Validators’.

If platforms and regulators focused more on what such distribution algorithms could do, they might take action to make that happen (as addressed in Regulating our Platforms -- A Deeper Vision).

Yes, "the way we think drives disinformation," and social media distribution algorithms drive how we think -- we can drive them for good, not bad!

---
Background noteNiemanLab today pointed to a PNAS paper showing evidence that "... ratings given by our [lay] participants were very strongly correlated with ratings provided by professional fact-checkers. Thus, incorporating the trust ratings of laypeople into social media ranking algorithms may effectively identify low-quality news outlets and could well reduce the amount of misinformation circulating online." The study was based on explicit quality judgments, but using implicit data on quality judgments as I suggest should be similarly correlated, and could apply the imputed judgments of every social media user who interacted with an item with no added user effort.

[Update:] 
Comments on Facebook's 2/17/20 White Paper, Charting a Way Forward on Online Content Regulation

This is an interesting document, with some good discussion, but it seems to provide evidence that leads to the point I make here, but totally misses seeing it. Again this seems to be a case in which "It is difficult to get a man to understand something when his job depends on not understanding it."

The report makes the important point that:
Companies may be able to predict the harmfulness of posts by assessing the likely reach of content (through distribution trends and likely virality), assessing the likelihood that a reported post violates (through review with artificial intelligence), or assessing the likely severity of reported content
So Facebook understands that they can predict "the likely reach of content" -- why not influence it??? It is their distribution process and filtering algorithms that control "the likely reach of content." Why not throttle distribution to reduce the reach in accord with the predicted severity of the violation? Why not gather realtime feedback from the distribution process (including the responses of users) to refine those predictions, so they can course correct the initial predictions and rapidly refine the level of the throttle? That is what I have suggested in many posts, notably In the War on Fake News, All of Us are Soldiers, Already!


See the Selected Items tab for more on this theme.

Monday, December 30, 2019

Regulating our Platforms -- A Deeper Vision (Working Draft)


Redirection and regulation of our Internet platforms is badly needed. There are numerous powerful statements on why, and many smart people working to make that happen.

But it is hard to agree on how, and with what objectives. Most people have little clue of where to start, or why it matters -- and many of those who do are divided about what to do, and whether proposed actions are too small or too large. This is a richly complex problem -- and much of what we read is oversimplified.

I recently immersed myself in some of the best analyses from respected think tanks -- and have some innovative perspectives of my own. This post begins with pointers to some of the best thinking, and then explains what I see as missing. That is largely a question of what are we designing for.

Update 4/26/21: An important strategy for a surgical restructuring (published in Tech Policy Press) — to an open market strategy that shifts control over our feeds to the users they serve — complements the discussion here.

==================================================================
The Ideas in Brief

Our Internet platforms have gone seriously wrong, and fixing that is more complex than most observers seem to realize. The good news is that there are well-conceived proposals for creating an expert regulatory agency that can oversee significant corrections. 

At a complementary level, we should be looking ahead to what these platforms should and could be doing to better serve us. That kind of vision should inform both how we regulate and how we design.
  • One critical need is to change how the algorithms work, so they serve users and society -- to make us smarter and happier, instead of dumber and angrier.
  • Another critical need is to shift the business models so that users, not advertisers become the customers, to better align the incentives of Internet service platforms to serve their users (and actually benefit the advertisers as well).
==================================================================

Broad issues, deep thinking, and vision

Many calls for regulatory action focus on just one or a few of the following diverse categories of abuse. Some of these conflict with one another and are advocated by different parties:
  1. privacy and controls on use of personal data 
  2. moderation of disinformation and false political ads and news and other objectionable content versus freedom of speech and the "marketplace of ideas"
  3. economic sustainability of news media and quality journalism
  4. antitrust, competition, and stifling of innovation
  5. failures of artificial intelligence (AI) and machine learning (ML) -- including hidden bias
The works recommended below stand out for their broad consideration of most or all of these issues and for their informed consideration of the legal background and history of related areas of regulation -- including nuanced First Amendment, antitrust, and media technology issues. They make a strong case that, given the complexity of these issues, compounded by the rapid (and reliably surprising) dynamics of business and technology development, that neither the market nor legislators can provide the necessary understanding and foresight. Instead they unanimously see need for an expert agency with an ongoing charter much like the Federal Trade Commission or the Federal Communications Commission but with the new mix of expertise relevant to the Internet platforms.

Particularly edifying are the analyses of issues and regulation related to the safe harbor provisions of Section 230 of the Communications Decency Act that protects "interactive services" from liability for bad content provided by others. Many have called for repealing those safe harbor protections, seeing that as a license to wantonly distribute harmful content, but these deeper analyses suggests a more nuanced interpretation -- the safe harbor should continue to apply to posting of content, but should not apply to filtered distribution in social media feeds. That is one of the themes I build on with my own suggestions below.

Beyond these excellent works, the gap -- and opportunity -- that I see is to refocus our objectives. We should look beyond just limiting the harms of over-concentration of power as we see them today and in hindsight, but look ahead to where we could be going.
  • Where we should be going is a question for public policy, not tech oligarchs who move fast and break things, and are driven by their private interests. 
  • But to understand that question of where we should be going, we need to understand where we could be going (both good and bad).
We need multidisciplinary efforts to set realistic but visionary objectives that serve all stakeholders as our platforms and technology continue to evolve, and we need to explore alternative scenarios to protect against abuse by some stakeholders against others.

"The Debate Over a New Digital Platform Agency" -- some essential resources

On October 17, I was invited to attend The Debate Over a New Digital Platform Agency: Developing Digital Authority and Expertise, at the The Digital Innovation & Democracy Initiative of the German Marshall Fund of the US in Washington, DC. Three reports that resulted from the work of the panelists were suggested as background reading:
The event generated excellent discussion. I made some comments that were well-received on the further opportunities I saw, and had discussions afterwards with several of the speakers. That led to an introduction to the author of another excellent report on this theme (and a discussion with him):
I highly recommend that anyone with a serious interest in these vital challenges read these reports. The Stigler and Feld reports provide thorough treatments from a US regulatory perspective, and complement one another in important areas. The Furman report is a valuable complement from a UK perspective (and Furman reported that the UK will move ahead to establish such a regulatory body and he has been asked to advise on that). The Kornbluth and Goodman report provides a shorter overview of many of the same issues.

More recently, I found another excellent report that is more focused on the technological and business issues and points toward some of the what I have proposed:
Also worth noting, Ben Thompson's Stratechery newsletter provides excellent insights into the business structure issues of our dominant platforms.

[Update: See the updates at the end for additional valuable resources, and my comments on them.]

My own suggestions on where our platforms could and should be going

The following is an updated rework of comments I sent on 10/20/19 to some of the speakers and attendees at the GMF meeting, followed by some added comments from my later discussion with Harold Feld, and other updates: 

Summary and Expansion of Dick Reisman’s comments on attending GMF 10/17/19 event:

I very much support the proposals for a New Digital Platform Authority (as detailed in the excellent background items cited on the event page) and offer some innovative perspectives.  I welcome dialog and opportunities to participate in and support related efforts. 

(My background is complementary to most of the attendees -- diverse roles in media-tech, as a manager, entrepreneur, inventor, and angel investor.  I became interested in hypermedia and collaborative social decision support systems around 1970, and observed the regulation of The Bell System, IBM, Microsoft, the Internet, and cable TV from within the industry.  As a successful inventor with over 50 software patents that have been widely licensed to serve billions of users, I have proven talent for seeing what technology can do for people.  Extreme disappointment about the harmful misdirection of recent developments in platforms and media has spurred me to continue work on this theme on a pro-bono basis.)  

My general comment is that for tech to serve democracy, we not only need to regulate to limit monopolies and other abuses, but also need to regulate with a vision of what tech should do for us -- to better enable regulation to facilitate that, and to recognize the harms of failing to do so.  If we don’t know what we should expect our systems to do, it is hard to know when or how to fix them.  The harm Facebook does becomes far more clear when we understand what it could do – in what ways it could be “bringing people closer together,” not just that it is actually driving them apart.  That takes a continuing process of thinking about the technical architectures we desire, so competitive innovation can realize and evolve that vision in the face of rapid technology and market developments.

More specifically, I see architectural designs for complex systems as being most effective when built on adaptive feedback control loops that are extensible to enable emergent solutions, as contexts, needs, technologies, and market environments change.  That is applicable to all the strategies I am suggesting (and to technology regulation in general).
  • I cited the Bell System regulation as a case in point that introduced well-architected modularity in the Carterfone Decision (open connections via a universal jack, much like modern API’s), followed by the breakup into local and long-distance and manufacturing, and the later introduction of number portability.  This resonated as reflecting not only the wisdom of regulators, but expert vision of the technical architecture needed, specifically what points of modularity (interoperability) would enable innovation.  (Of course the Bell System emerged as a natural monopoly growing out of an earlier era of competing phone systems that did not interoperate.)  
  • The modular architecture of email is another very relevant case in point (one that did not require regulation). 
  • The original Web and Web 2.0 were built on similar modularity and APIs that facilitated openness, interoperability, and extensibility.
But the platforms have increasingly returned us to proprietary walled gardens that lock in users and lock out competitive innovation.

I noted three areas where my work suggests how to add a more visionary dimension to the excellent work in the cited reports.  One is a fundamental problem of structure, and the other two are problems of values that reinforce one another.  (The last one applies not only to the platforms, but to the fundamental challenge of sustaining news services in a digital world.)  All of these are intended not as definitive point solutions, but as ongoing processes that involve continuing adaptation and feedback, so that the solutions are continuously emergent as technology and competitive developments advance.

1.  System and business structure -- Modular architecture for flexibility and extensibility.  The heart of systems architecture is well-designed modularity, the separation of elements that can interoperate yet be changed in at will -- that seems central to regulation as well – especially to identify and manage exclusionary bottlenecks/gateways.  At a high level, the e-mail example is very relevant to how different “user agents” such as Outlook, Apple mail, and Gmail clients can all interoperate to interconnect all users through “message transfer agents” (through the mesh of mail servers on the Internet).  A similar decoupling should be done for social media and search (for both information and shopping).

Similar modularity could usefully separate such elements as:
  • Filtering algorithms – to be user selectable and adjustable, and to compete in an open market much as third-party financial analytics can plug in to work with market data feeds and user interfaces.
  • Social graphs – to enable different social media user interfaces to share a user’s social graph (much like email user agent / transfer agent).
  • Identity – verified / aliased / anonymous / bots could interoperate with clearly distinct levels of privilege and reputation.
  • Value transfer/extraction systems – this could address data, attention, and user-generated-content and the pricing that relates to that.
  • Analytics/metrics – controlled, transparent monitoring of activity for users and regulators.

2.  User-value objectives -- filtering algorithms controlled by and for users.  This is the true promise of information technology – not artificial intelligence, but the augmentation of human intelligence.
·       User value is complex and nuanced, but Google’s original PageRank algorithm for search results filtering demonstrates how sophisticated algorithms can optimize for user value by augmenting the human wisdom of crowds – the algorithm can infer user intent, and weigh implicit signals of authority and reputation derived from human activity at multiple levels, to find relevance in varying contexts. 
·       In search, the original PageRank signal was inward links to a Web page, taken as expressions of the value judgements of individual human webmasters regarding that page.  That has been enriched to weed out fraudulent “link farms” and other distortions and expanded in many other ways.
·       For the broader challenge of social media, I outline a generalization of the same recursive, multi-level weighting strategy in The Augmented Wisdom of Crowds: Rate the Raters and Weight the Ratings.  The algorithm ranks items (of all kinds) based on implicit and explicit feedback from users (in all available forms), partitioned to reflect communities of interest and subject domains, so that desired items bubble up, and undesired items are downranked.  This can also combat filter bubbles -- to augment serendipity and to identify “surprising validators” that might cut through biased assimilation.
·       That proposed architecture also provides for deeper levels of modularity:  to enable user control of filtering criteria, and flexible use of filtering tools from competing sources -- which users could combine and change at will, depending on the specific task at hand.  That enables continuous adaptation, emergence, and evolution, in an open, competitive market ecosystem of information and tools. (As noted below, the Masnick paper makes a nice case for this.) 
·       Filtering for user and societal value:  The objective is to allow for smart filtering that applies all the feedback signals available to provide what is valued by that the user at that time.  By allowing user selection of filtering parameters and algorithms, the filters can become increasingly well-tuned to each user's value system, as it applies within each community of interest, and each subject domain.
·       First amendment, Section 230, prohibited content issues, and community standards:  When done well, this filtering might largely address those concerns about bad content, greatly reducing the need for the blunt instrument of regulatory controls or censorship, and working in real time, at Internet-speed, with minimal need for manual intervention regarding specific items.  As I understand it, this finesses most of the legal issues:  users could retain the right to post information with very little restriction -- if objectionable content is automatically downranked enough in any filtering process that a service provides (an automated form of moderation) to avoid sending it to users who do not want such content -- or who reside in jurisdictions that do not permit it.  Freedom of speech (posting), not freedom of reach (delivery) to others who have not invited it
-- Thus Section 230 might be applied to posting, just as seemed acceptable when information was pulled from the open Web.
-- But the Section 230 safe harbor protections against liability might not apply to the added service of selective dissemination, when information is pushed through social media (and when ads are targeted into social media). The filtering that determines what users see might apply both user- and government-defined restrictions (as well as restrictions at the level of specific user communities that desire those restrictions). [See 2/4/20 update below on related Section 230 issues.]
(Such methods might evolve to become a broad architectural base for richly nuanced forms of digital democracy.)

[See 1/10/20 Update below on distribution filtering as the choke point for disinformation. It is here that we can reverse the wrong direction of social media that is so destructively making people dumber instead of smarter. This is now expanded slightly as a free standing post, The Dis-information Choke Point: Dis-tribution (Not Supply or Demand)]

3.  Business model value objectives – who does the platform serve?  This is widely observed to be the “original sin” of the Internet, one that prevents the emergence of better solutions in the above two areas.  Without solving this problem, it will be very difficult to solve the other problems.  “It is difficult to get a man to understand something when his job depends on not understanding it”  We call them services, but they do not serve us. Funding of services with the ad model makes those services seem free and affordable, but drives platform services businesses to optimize for engagement (to sell ads), instead of optimizing for the value to users and society.  Users are the product, not the customer, and value (attention) is extracted from the users to serve the platforms and the advertisers.

Also, modern online advertising is totally unlike prior forms of advertising because unprecedented detail in user data and precision targeting enables messaging and behavioral manipulations at an individual level.  That has driven algorithm design and use of the services in catastrophically harmful directions, instead of beneficial ones.

Many have recognized this business model problem, but few see any workable solution. I suggest a novel path forward at two levels:  an incentive ratchet to force the platforms to seek solutions, and some suggested solution mechanisms that suggest how that ratchet could bear fruit in ways that are both profitable and desirable ...in ways that few now imagine.

Ratchet the desired business model shift with a simple dial, based on a simple metric.  A very simple and powerful regulatory strategy could be to impose taxes or mandates that gradually ratchet toward the desired state. This leverages market forces and business innovation in the same way as the very successful model of the CAFE standards for auto fuel efficiency -- it leaves the details of how to meet the standard to each company
·       The ratchet here is to provide compelling incentives for dominant services to ensure that X% of revenue must come from users.  Such compelling taxes or mandates might be restricted to distribution services with ad revenues above some threshold level.  (Any tax or penalty revenue might be applied to ameliorate the harms.)
·       That X% might be permitted to still include advertising revenue if it is quantified as a credit back to the user (a “reverse meter” much as for co-generation of electricity).  Advertising can be valuable and non-intrusive and respectful of data -- explicitly putting a price on the value transfer from the consumer would incentivize the advertising market toward user value. 
·       This incentivizes individual companies to shift their behavior on their own, without need for the kind of new data intermediaries (“infomediaries” or fiduciaries) that others have proposed without success.  It could also create more favorable conditions for such intermediaries to arise.

Digital services business model issues -- for news services as well as platforms.  (Not addressed at the event, but included in some of the reports.)  Many (most prominently Zuckerberg) throw up their hands at finding business models for social media or search that are not ad-funded, primarily because of affordability issues.  The path to success here is uncertain (just as the path to fuel efficient autos is uncertain). But many innovations emerging at the margins offer reasons to believe that better solutions can be found.
·       One central thread is the recognition that the old economics of the invisible hand fails because there is no digital scarcity for the invisible hand to ration.  We need a new way to settle on value and price.
·       The related central thread is the idea of a social contract for digital services, emerging most prominently with regard to journalism (especially investigative and local).  We must pay now, not for what has been created already, but to fund continuing creation for the future. Behavioral economics has shown that people are not homo economicus but homo reciprocans – they want to be fair and do right, when the situation is managed to encourage win-win behaviors. 
·       Pricing for digital services can shift from one-size-fits-all, to mass-customization of pricing that is fair to each user with respect to the value they get, the services they want to sustain, and their ability to pay.  Current all-you-can-eat subscriptions or pay-per-item models track poorly to actual value.  And, unlike imposing secretive price discrimination, this value discrimination can be done cooperatively (or even voluntarily).  Important cases in point are The Guardian’s voluntary payment model, and recurring crowdfunding models like Patreon. Journalism is recognized to be a public good, and that can be an especially strong motivator for sustaining payments.
·       Synergizing with this, and breaking from norms we have become habituated to, the other important impact of digital is the shift toward a Relationship Economy – shifting focus from one-shot zero-sum transactions to ongoing win-win relationships such as subscriptions and membership.  This builds cooperation and provides new leverage for beneficial application of behavioral economic nudges to support this creative social contract, in an invisible handshake.  My own work on FairPay explains this and provides methods for applying it to make these services sustainable by user payments. (See this Overview with links, including journal articles with prominent marketing scholars, brief articles in HBR and Techonomy, and many blog posts, such as one specific to journalism.) 
·       Vouchers.  The Stigler Committee proposal for vouchers might be enhanced by integration with the above methods.  Voucher credits could be integrated with subscription/membership payments to directly subsidize individual payments, and to nudge users to donate above the voucher amounts.
·       Affordability. To see how this deeper focus on value changes our thinking, consider the economics of reverse meter credits for advertising, as suggested for the ratchet strategy above.  As an attendee noted at the event, reverse metering would seem to unfairly favor the rich, since they can better afford to pay to avoid ads.  But the platforms actually earn much more for affluent users (their targeted ad rates are much higher).  If prices map to the value surplus, that will tend to balance things out – if the less affluent want service to be ad-free, it should be less costly for them than for the affluent. And when ads become less intrusive and more relevant, even the affluent may be happy to accept them (how about the ads in Vogue?).

AI as a platform regulatory issue.  Discussion after the session raised the issue of regulating AI.  There is growing concern relating to concentrations of power and other abuses, including concentrations of data, bias in inference and in natural language understanding, and lack of transparency, controls, and explainability. That suggests a similar need for a regulator that can apply specialized technical expertise that overlaps and coordinates with the issues addressed here.  AI is fundamental to the workings of social media, search, and e-commerce platforms, and also has many broader applications for which pro-active regulation may be needed.

Some further reflections

From reviewing Harold Feld's book and discussing it with him:
  • He notes the growing calls for antitrust regulation to consider harms beyond price increases (which ignores the true costs of "free" services) and suggests "cost of exclusion" (COE) as a useful metric of harm to manage for. 
  • I suggest that similar logic argues for more attention to what platforms could and should be doing as a metric of harm. The idea is not to mandate what they should do, but to to avoid blocking it -- and to estimate the cost of not providing valuable services that a more competitive market that is incentivized to serve end-users would provide in some form.  
  • Feld also suggests that is is a proper objective of regulation to support promotion of good content and discourage bad content (just as was done for broadcast media). Further to that objective, my Augmented Wisdom of Crowds methods show how that can become nuanced, dynamic, reflective of user desires, domains of expertise, and communities of interest, and selectively match to the standards of many overlapping communities.  A related post highlights how this can serve as A Cognitive Immune System for Social Media -- Developing Systemic Resistance to Fake News.
  • On Section 230-related issues, an interesting question I have not seen well addressed is how the targeting of advertising interplays with filtering feeds for content of all kinds. 
    -- I advocate that filtering of content feeds should be controlled by and for the end-users of the feeds, and economic incentives should align to that.
    -- Targeting of ads (political or commercial) is currently a countervailing force that directs ads to users in ways that do not align with their wishes (and motivates filtering to inflame rather than enlighten).
    -- Reverse metering of attention and data could provide a basis to negotiate -- in this two-sided market -- over just how targeting meshes with the prioritization and presentation of items in feeds.  (A valuable new resource on the design of multi-sided platforms is The Platform Canvas.)
  • Push vs. pull: also related to managing harmful content, Feld draws useful distinctions of Broadcast/Many-to-Many vs. Common Carrier/One-to-One and Passive Listening vs. Active Participation, I suggest the distinction between Push versus Pull distribution/access is also very important to First Amendment issues:
    -- Pull is on demand requests for specific items, such as by actively searching, or direct access to a Web service.  In a free society there should presumably be very limited restrictions on what content users may pull.
    -- Push is a continuing feed, such as a social media news feed.  This can be a firehose of everything (subject to privacy constraints) or a filtered feed (as typical in current social media).  I think Feld's analysis supports the case that there is no First Amendment right of a speaker to have their speech pushed to others in a filtered feed (no free reach or free targeting, as in my posts below).  Note that filtering items in a feed uses much the same discrimination technology as filtering (ranking) of search results (for example, Google Alerts are “a standing search” that is applied to create a feed of newly posted items that match the standing search).  (I have fundamental patents from 1994, now expired, on a widely used class of push.)
  • Feld addresses the issues of filter bubbles and serendipity and proposes “wobbly algorithms” that introduce more variety (and I found recent support for that in this new CACM article). I have outlined methods for seeking Surprising Validators and serendipity in ways that are more purposeful in going far beyond just random variation.  
  • Regarding the quality of news, he addresses the widely supported idea of “tools for reliable sources,” I suggest that human rating services (like NewsGuard) are far too limited in scope and timeliness, and too open to dispute, to be more than a very partial solution.  The algorithmic methods I propose can include such expert rating services, as just one high-reputation component of a broader weighting of authority and relevance in which everyone with a reputation for sound judgement in a subject domain contributes, with a weighting that is based on their reputation.  The augmented crowd will often be smarter than the experts -- and can work far faster to flag problematic content at Internet scale.
Updating my comments from October, many observers have participated in the recent controversy over how the platforms deal with false political ads. Many fail to understand the critical difference between speech and distribution (nicely put in "Free Speech is Not the Same as Free Reach"*). I explained those issues in Free Speech, Not Free Targeting! (Using Our Own Data to Manipulate Us), and note the emerging agreement (including Feld) that limiting the microtargeting of political ads is a reasonable stopgap, until we can provide a more nuanced solution.

Technical architecture issues

I was very pleased to happen on the Masnick article, Protocols, Not Platforms: A Technological Approach to Free Speech (a couple weeks ago), as the nearest thing to the vision I have been developing that I have yet seen. It is not aimed at regulation, apparently in hopes that the market can correct itself (a hope I have shared, but no longer put much faith in). Our works are both overlapping and complementary – reinforcing and expanding in different ways on very similar visions for user-controlled, open filtering of social media and the marketplace of ideas. I recommend his paper to anyone who wants to understand how this technology can be far more supportive of user value by enabling users to mold their social media to their individual values, and as a foundation for better understanding my more specific proposals.

As background, in developing these ideas for an open market of user-controlled filtering tools, I drew on my experience in financial technology from around 1990. There was a growing open market ecosystem for transaction level financial market data (generated by the stock exchanges and other markets -- ticker feeds and the like), which was then gathered and redistributed to brokers and analysts by redistributors like Dow Jones, Telerate, and Bloomberg. An open market for analytic tools that could analyze this data and provide a rich variety of financial metrics was developing -- one that could interoperate, so that brokers and analysts could apply those analytics, or create their own custom variations (as an early form of mashup). That was an inspiration for work I did in 2002 to design a system for open collaboration on finding and developing innovations, in the days when "open innovation" was an emerging trend. That design provided very rich functions for flexible, mass collaboration that I later adapted to apply to social media (as described on my blog, starting in 2012, when I saw that current systems were not going in the direction I thought they should). 

Personal privacy versus openness, and interoperability

Privacy has emerged as a critical issue in Internet services, and one that is often in conflict with the objectives of openness and interoperability that are essential to the marketplace of services and to the broader marketplace of ideas (and also to making AI/ML as beneficial as possible). Here again there is a need for nuance and expertise to sensibly balance the issues, and there is reason to fear that current privacy legislation initiatives may fail to provide a proper balance. I believe there are more nuanced ways to meet these conflicting objectives, but leave more specific exploration of that for another time.

Moving forward

We have learned that our Web services are far more complex and have far more dangerous impacts on society than we realized. We need to move forward with more deliberation, and need a business and regulatory environment capable of guiding that. We have seen how dangerous it can be to "move fast and break things."

I am working independently on a pro-bono basis on these issues, and welcome opportunities to collaborate with others to move in the directions outlined here. (These ideas draw on two detailed patent filings from 2002 and 2010 that I have placed into the public domain.)

---
[*Update 1/2/20:] Mediating consent by augmenting the wisdom of crowds

Renee DiResta (who wrote the Free Speech is Not the Same as Free Reach post I cited above) recently wrote an excellent article, Mediating Consent, which I commented on today. Her article is an excellent statement of how we are now at a turning point in the evolution of how human society achieves consensus – or breaks down in strife. She says “The future that realizes this promise still remains to be invented.” As outlined above, I believe the core of that future has already been invented — the task is to decide to build out on that core, to validate and adjust it as needed, and to continuously evolve it as society evolves.

[Update 1/10/19:] The disinformation choke point:  distribution (not supply or demand) --

[This is now expanded slightly to be a free-standing post]

An excellent 1/8/20 report from the National Endowment for Democracy, “Demand for Deceit: How the Way We Think Drives Disinformation,” by Samuel Woolley and Katie Joseff, highlights the dual importance of both supply and demand side factors in the problem of disinformation.  That crystallizes in my mind an essential gap in this field -- smarter control of distribution -- that was implicit in my comments on algorithms (section #2 above).

There is little fundamentally new about the supply or the demand for disinformation.  What is fundamentally new is how disinformation is distributed.  That is what we most urgently need to fix. If disinformation falls in a forest… but appears in no one’s feed, does it disinform?

In social media a new form of distribution mediates between supply and demand.  The media platform does filtering that upranks or downranks content, and so governs what users see.  If disinformation is downranked, we will not see it -- even if it is posted and potentially accessible to billions of people.  Filtered distribution is what makes social media not just more information, faster, but an entirely new kind of medium.  Filtering is a new, automated form of moderation and amplification.  That has implications for both the design and the regulation of social media. 

By changing social media filtering algorithms we can dramatically reduce the distribution of disinformation.  It is widely recognized that there is a problem of distribution: current social media promote content that angers and polarizes because that increases engagement and thus ad revenues.  Instead the services could filter for quality and value to users, but they have little incentive to do so.  What little effort they ever have made to do that has been lost in their quest for ad revenue.

Social media marketers speak of "amplification." It is easy to see the supply and demand for disinformation, but marketing professionals know that it is amplification in distribution that makes all the difference. Distribution is the critical choke point for controlling this newly amplified spread of disinformation. (And as Feld points out, the First Amendment does not protect inappropriate uses of loudspeakers.)

While this is a complex area that warrants much study, as the report observes, the arguments cited against the importance of filter bubbles in the box on page 10 are less relevant to social media, where the filters are largely based on the user’s social graph (who promotes items to be fed to them, in the form of posts, likes, comments, and shares), not just active search behavior (what they search for). 

Changing the behavior of demand is clearly desirable, but a very long and costly effort. It is recognized that we cannot stop the supply. But we can control distribution -- changing filtering algorithms could have significant impact rapidly, and would apply across the board, at Internet scale and speed -- if the social media platforms could be motivated to design better algorithms. I explain further in A Cognitive Immune System for Social Media -- Developing Systemic Resistance to Fake News and In the War on Fake News, All of Us are Soldiers, Already! That is what I am advocating in my section #2.

Yes, "the way we think drives disinformation," and social media distribution algorithms drive how we think -- we can drive them for good, not bad!

[Update 2/4/20] Related Section 230 issues.

The discussion above related to posting versus distribution did not clearly address other issues that have driven lobbying against Section 230. These include companies concerned about illegal postings on Airbnb, and about copyright infringement, and other improper content. My initial take on this is that the distinction of posting versus filtered distribution outlined above should also distinguish posting from other forms of selective distribution, such as by search in which a selection or moderation function is present.

For example, Airbnb is a marketplace in which Airbnb may not offer a filtered feed, but offers search services. The essential point is that Airbnb filters searches by selection criteria -- and by its own listing standards. Thus there is an expectation of quality control. As long as Airbnb provides a quality control service, it is moderated, and thus should not have safe harbor under Section 230. If it did not do moderation, then posting on Airbnb should properly have safe harbor protections, but selective (filtered) search functions might not have safe harbor to include illegal postings. Access to such uncontrolled postings might be limited to explicit searches for a specific property identifier (essentially a URL) to retain safe harbor protection.

So here as well, it seems the proper and tractable understanding of the problem is not in the posting, but in the distribution.

[Update 9/9/20] A killer TED Talk and another excellent analysis

Yaël Eisenstat's TED Talk, "How Facebook profits from polarization," is very important, right on target, and well said! If you don’t understand why Facebook and other social media are the gravest threat to society (as they currently operate), this will be the most informative 14 minutes you can spend. (From a former CIA analyst, diplomat…and Facebook staffer.) (9/8/20)

New Digital Realities; New Oversight Solutions from the Harvard Shorenstein Center, by Tom Wheeler, Phil Verveer, and Gene Kimmelman, is another excellent think tank proposal that is right on target. (8/20/20)

[Update 12/14/20] A specific proposal - Stanford Working Group on Platform Scale

An important proposal that gets at the core of the problems in media platforms was published in Foreign AffairsHow to Save Democracy From Technology, by Francis Fukuyama and others. See also the report of the Stanford Working Group. The idea is to let users control their social media feeds with open market interoperable filters. That is something I have proposed, and provided details on how and why to do. 

[Update 2/12/21] Growing support for open market filtering services - Twitter too

More proposals for this have surfaced, including in Senate testimony, plus indications of interest from Twtter. This suggests this may be the best path for action. See this newer post and this update, and stay tuned for more.

[Important Update 4/26/21] 

This important strategy for a surgical restructuring was published in Tech Policy Press. An open market strategy that shifts control over our feeds to the users they serve complements the actions discussed here. This new article summarizes and expands on proposals from notable sources (including Twitter CEO Jack Dorsey) that get at the core of the problems in media platforms. 

---
See the Selected Items tab for more on this theme.

Wednesday, July 24, 2019

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 crowdfundingmembership 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.

---
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
  • 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.)

Tuesday, April 09, 2019

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

Summarizing Ben Thompson of Stratechery, plus my own targeted proposals

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

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

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

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

Ben identifies three clear questions that make regulation problematic:
First, what content should be regulated, if any, and by whom?
Second, what is a viable way to monitor the content generated on these platforms?
Third, how can privacy, competition, and free expression be preserved?

Exploring the viral spread of the Christchurch hate crime video, he gets to a key issue:
What is critical to note, though, is that it is not a direct leap from “pre-Internet” to the Internet as we experience it today. The terrorist in Christchurch didn’t set up a server to livestream video from his phone; rather, he used Facebook’s built-in functionality. And, when it came to the video’s spread, the culprit was not email or message boards, but social media generally. To put it another way, to have spread that video on the Internet would be possible but difficult; to spread it on social media was trivial.
The core issue is business models: to set up a live video streaming server is somewhat challenging, particularly if you are not technically inclined, and it costs money. More expensive still are the bandwidth costs of actually reaching a significant number of people. Large social media sites like Facebook or YouTube, though, are happy to bear those costs in service of a larger goal: building their advertising businesses.

Expanding on business models, he describes the ad-based platforms as "Super Aggregators:"
The key differentiator of Super Aggregators is that they have three-sided markets: users, content providers (which may include users!), and advertisers. Both content providers and advertisers want the user’s attention, and the latter are willing to pay for it. This leads to a beautiful business model from the perspective of a Super Aggregator:
Content providers provide content for free, facilitated by the Super Aggregator
Users view that content, and provide their own content, facilitated by the Super Aggregator
Advertisers can reach the exact users they want, paying the Super Aggregator 
...Moreover, this arrangement allows Super Aggregators to be relatively unconcerned with what exactly flows across their network: advertisers simply want eyeballs, and the revenue from serving them pays for the infrastructure to not only accommodate users but also give content suppliers the tools to provide whatever sort of content those users may want.
...while they would surely like to avoid PR black-eyes, what they like even more is the limitless supply of attention and content that comes from making it easier for anyone anywhere to upload and view content of any type.
...Note how much different this is than a traditional customer-supplier relationship, even one mediated by a market-maker... When users pay they have power; when users and those who pay are distinct, as is the case with these advertising-supported Super Aggregators, the power of persuasion — that is, the power of the market — is absent.
He then distinguishes the three types of "free" relevant to the Internet, and how they differ:
“Free as in speech” means the freedom or right to do something
“Free as in beer” means that you get something for free without any additional responsibility
“Free as in puppy” means that you get something for free, but the longterm costs are substantial
...The question that should be asked, though, is if preserving “free as in speech” should also mean preserving “free as in beer.”
Platforms that are paid for by their users are "regulated" by the operation of market forces, but those that are ad-supported are not, and so need external regulation.

Ben concludes that:
...platform providers that primarily monetize through advertising should be in their own category: as I noted above, because these platform providers separate monetization from content supply and consumption, there is no price or payment mechanism to incentivize them to be concerned with problematic content; in fact, the incentives of an advertising business drive them to focus on engagement, i.e. giving users what they want, no matter how noxious.
 This distinct categorization is critical to developing regulation that actually addresses problems without adverse side effects
...from a theoretical perspective, the appropriate place for regulation is where there is market failure; constraining the application to that failure is what is so difficult.
That leads to Ben's figure that brings these ideas together, and delineates critical distinctions:


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

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

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

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

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

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

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

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

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

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

I say the real issue is:
  1.  Not: rules for what can and cannot be said – speech is a protected right
  2.  But rather: rules for what statements are seen by who – distribution (how feeds are filtered and presented) is not a protected right.
The value of a social media service should be to disseminate the good, not the bad. (That is why we talk about “filter bubbles” – failures of value-based filtering.)

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

I have written about new ways to use PageRank-like algorithms to determine what to downrank or uprank – “rate the raters and weight the ratings.”
  • Facebook can have a fairly free hand in downranking objectionable speech
  • They can apply community standards to what they promote -- to any number of communities, each with varying standards.
  • They could also enable open filtering, so users/communities can chose someone else’s algorithm (or set their preferences in any algorithm). 
  • With smart filtering, the spread of harmful speech can be throttled before it does much harm.
  • The “augmented wisdom of the crowd” can do that very effectively, on Internet scale, in real time.
  • No pre-emptive, exclusionary, censorship technique is as effective at scale -- nor as protective of free speech rights or community standards.
That approach is addressed at some length in these posts (where “fake news” is meant to include anything objectionable to some community):
…and some further discussion on that:
---
More of my thinking on these issues is summarized in this Open Letter to Influencers Concerned About Facebook and Other Platforms

---
See the Selected Items tab for more on this theme.