Showing posts with label Twitter. Show all posts
Showing posts with label Twitter. Show all posts

Monday, January 27, 2020

Make it So, Now! - 10 Ways Tech Platforms Can Safeguard the 2020 Election

"Ten things technology platforms can do to safeguard the 2020 U.S. election" is an urgent and vital statement that we should all read -- and do all we can to make happen -- especially if you have any connection to the platforms, Congress, or regulators (or the press). Hopefully, anyone reading this understands why this is urgent (but the article begins with a brief reminder).

Thirteen prominent thought leaders "met...to discuss immediate steps the major social media companies can take to help safeguard our democratic process and mitigate the weaponization of their platforms in the run-up to the 2020 U.S. elections. They published this as a "living document."

Here is their list of  "What can be done … now" (the article explains each):
  1. Remove and archive fraudulent and automated accounts
  2. Clearly identify paid political posts — even when they’re shared
  3. Use consistent definitions of an ad or paid post
  4. Verify and accurately disclose advertising entities in political ads
  5. Require certification for political ads to receive organic reach
  6. Remove pricing incentives for presidential candidates that reward virality (including a limit on microtargeting)
  7. Provide detailed resources with accurate voting information at top of feeds
  8. Provide a more transparent and consistent set of data in political ad archives
  9. Clarifying where they draw the line on “lying”
  10. Be transparent about the resources they are putting into safety and security
All of these should be do-able in a matter of months.  While many of the signatories "...are working on longer-term ways to create a healthier, safer internet, [they] are proposing more immediate steps that could be implemented before the 2020 election for Facebook and other social media platforms to consider." 

The writers include "a Facebook co-founder, former Facebook, Google and Twitter employees, early Facebook and Twitter investors, academics, non-profit leaders, national security and public policy professionals:" John Borthwick, Sean Eldridge, Yael Eisenstat, Nir Erfat, Tristan Harris, Justin Hendrix, Chris Hughes, Young Mie Kim, Roger McNamee, Adav Noti, Eli Pariser, Trevor Potter and Vivian Schiller.

I, too, am working on longer term issues, as outlined in this recent summary in the context of some important think tank reports: Regulating our Platforms -- A Deeper Vision Similarly, I have addressed one of the most urgent stop-gap issues (which is part of their #6), in 2020: A Goldilocks Solution for False Political Ads on Social Media is Emerging).

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.

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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).
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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.)

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[*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. 

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Thursday, January 31, 2019

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 Newsfacts 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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