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).

Monday, January 20, 2020

Personalized Nutrition -- Because Everything is Deeply Intertwingled!

Nutrition is hard to get right because everything is deeply intertwingled. Personalized Nutrition is changing that!

This new perspective on nutrition is gaining attention, as an aspect of personalized medicine, and is the subject of a new paper, Toward the Definition of Personalized Nutrition: A Proposal by The American Nutrition Association.  (I saw it as it was finalized, since my wife, Dana Reed, is a co-author, and a board member and part of the nutrition science team at ANA.)

The key idea is:
Personalized nutrition (PN) is rooted in the concept that one size does not fit all; differences in biochemistry, metabolism, genetics, and microbiota contribute to the dramatic inter-individual differences observed in response to nutrition, nutrient status, dietary patterns, timing of eating, and environmental exposures. PN has been described in a variety of ways, and other terms such as “precision nutrition,” “individualized nutrition,” and “nutritional genomics” have similar, sometimes overlapping, meanings in the literature.
I have always been something less than a poster child for following nutrition guidelines, for reasons that this report cites:  "...guidelines have only limited ability to address the myriad inputs that influence the unique manifestation of an individual’s health or disease status."

I frequently cite the conundrum from Woody Allen's Sleeper, when the 1970s protagonist had just been awakened by doctors after 200 years:
Dr. Melik: This morning for breakfast he requested something called "wheat germ, organic honey and tiger's milk."
Dr. Aragon: [chuckling] Oh, yes. Those are the charmed substances that some years ago were thought to contain life-preserving properties.
Dr. Melik: You mean there was no deep fat? No steak or cream pies or... hot fudge?
Dr. Aragon: Those were thought to be unhealthy... precisely the opposite of what we now know to be true.
Overstated to be sure, but the real issue is that "one man's meat is another man's poison." Determining which is which for a given person has been impractical, but now we are not only learning that this is far more intertwingled than was thought, but we are gaining the ability to tease out what applies to a given person.

I come from this not from biology, but from machine learning and predictive analytics. My focus is on getting smarter about how everything is intertwingled.

One of the most intriguing companies I have run across is Nutrino, a startup acquired by Medtronic, that analyzes data from continuous glucose monitors used by diabetics to understand the factors that affect their glucose response over time. They correlate to specific food intakes, activity, sleep, mood, blood tests, genomics, biomics, and more. They call it a FoodPrint, "a digital signature of how our body reacts to different foods. It is contextually driven and provides correlations, insights and predictions that become the underpinning for personal and continually improving nutrition recommendations." This is one of the first successful efforts to tease out how what I eat (and what else I do) really affects me as an individual, in all of its real-world intertwingularity.

It is time to move beyond the current so-called "gold standard" of intervention-based studies, the randomized double blind placebo controlled (RDBPC) clinical tests. Reality is far too intertwingled for that to be more than narrowly useful. It is time to embrace big data, correlation, and predictive analytics. Some early recognition of this is that drugmakers are getting the FDA to accept mining of patient data as a way to avoid need for clinical trials.

We have a long way to go, but I want to know how likely it is that a given amount of deep fat or hot fudge, or wheat germ or kale (in combination with the rest of my diet, behavior and risk factors), will have a significant effect, over a time frame that can motivate whether or not I indulge in my chocolate or eat my spinach.

It is not enough to know that the dose makes the poison -- I want to know if the average man's poison is really just my meat.

Before very long we will know.

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.

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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 Howard 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.

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Thursday, December 26, 2019

2020 Vision -- The Restoration of the Customer

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

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

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

Those turns might just begin to undo many of the ills of the industrial revolution and of the computer revolution.  Both turns center on a return to enlightened human values:
  • The customer is not just a persona with a bundle of attributes that a business can learn how to manipulate, but a unique human being that has been bred since pre-history to thrive on a cooperative effort to create value and share it.
  • The user is not just a a source of attention that can be engaged to be sold to advertisers, but a customer to be served what they value -- again, a cooperative effort to create value and share it.
What those paying attention see

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

Here I point to some little recognized ideas on how re-centering on value can change not only the dynamic of commerce, but also a parallel dynamic of customer value that is equally important.
  • First, the commercial dynamic that Forrester describes is just the foundation for reversing how the "progress" of technology cost us the human dimension in commerce -- a dimension that we had when commerce was just the way villagers did business with one another -- with human beings on both sides of an ongoing relationship. 
  • Second, we humans, as "customers" of Web services, have lost control of our experience of the world.  Our central experience of human interaction has been hijacked by platforms who "engage" us in order to profit from bombarding us with advertising and paid propaganda.
First: Back to the future of commerce

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

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

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

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

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

Wednesday, November 06, 2019

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

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

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

The real problem

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

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

We must be clear that there is a right of speech, but only limited rights to amplification or targeting. We have always had political ads that lie. America was founded on the principle that the best counter to lies is not censorship, but truth. Policing lies is a very slippery slope, but when a lie is out in the open, it can be exposed, debunked, and shamed. Sunlight has proven the best disinfectant. With microtargeting there is no exposure to sunlight and shame.
  • This new microtargeted filtering service can direct user posts or paid advertising to those most vulnerable to being manipulated, without their informed permission or awareness.
  • The social media feedback cycle can further enlist those manipulated users to be used as conduits ("useful idiots") to amplify that harm throughout their social graphs (much like the familiar screech of audio feedback that is not properly damped). 
  • These abuses are hidden from others and generally not auditable. That is compounds the harm of lies, since they can be targeted to manipulate factions surreptitiously. 
Consensus for a stopgap solution

In the past week or so, limits on microtargeting have been suggested to take a range of forms, all of which seem workable and feasible:
  • Ellen Weintraub, chair of the Federal Election Commission (in the Washington Post), Don’t abolish political ads on social media. Stop microtargeting, suggests “A good rule of thumb could be for Internet advertisers to allow targeting no more specific than one political level below the election at which the ad is directed.
  • Alex Stamos, former Facebook security chief, in an interview with Columbia Journalism Review, suggests “There are a lot of ways you can try to regulate this, but I think the simplest is a requirement that the "segment" somebody can hit has a floor. Maybe 10,000 people for a presidential election, 1,000 for a Congressional.”
  • Siva Vaidhyanathan, in the NT Times, suggesting "here’s something Congress could do: restrict the targeting of political ads in any medium to the level of the electoral district of the race."
  • In my prior postI suggested “allow ads to only be run...in a way that is no more targeted than traditional media…such as to users within broad geographic areas, or to a single affinity category that is not more precise or personalized than traditional print or TV slotting options.
This seems to be an emerging consensus that this is the best we can expect to achieve in the short run, in time to protect the 2020 election. This is something that Zuckerberg, Dorsey, and others (such as Google) could just decide to do -- or might be pressured to do. NBC News reported yesterday that Facebook is considering such an action.

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

The longer term

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

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


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

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

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

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


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


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

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

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

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