I left Facebook in 2013, less for my own sake than for what my presence on the service was doing to others. I knew that anyone who “liked” my page could have their data harvested in ways they wouldn’t necessarily approve.

Over the past five years, people have not only become aware of this devil’s bargain but accepted it as the internet’s price of admission.”So what if they have my data,” I saw a graduate student ask her professor this week. “Why is my privacy so important?”

Bully for you if you don’t care what Facebook’s algorithms know about your sex life or health history, but that’s not the real threat. Neither Facebook nor the marketers buying your data particularly care about what you do with your clothes off, whom you’re cheating with or any other sordid details you may find embarrassing.

That’s the great fiction of social media: That you matter as a person. You don’t.

The platform cares only about your metadata, from which they can construct a psychological profile and then manipulate your behavior. They have been using and selling even the stuff you thought you were sharing confidentially with your friends in order to identify your neuroses and neurotic vulnerabilities and leverage them against you.

That’s what Facebook markets to its customers. The company has been doing it ever since its investors realized that, as owners of a mere social network, they would become only multi-millionaires; to become billionaires, they’d have to offer something more than our attention to ads. So they sold access to our brain stem.

With 2.2 billion active users, Facebook knew it had a big-data gold mine. While we’ve been busily shielding what we think of as our “personal” data, Facebook has been analyzing the stuff we think doesn’t matter: our clicks, likes and posts, as well as the frequency with which we make them. Looking at this metadata, Facebook, its psychologists and its clients put us into different psychographic “buckets.”

That’s how they came to be able to predict, with about 80% accuracy, our future behaviors, including whether we’re going to go on a diet, vote for a particular candidate or announce a change in sexual orientation. From there, the challenge is to compel the lagging 20% to fall in line — to get all the people who should be going on a diet or voting for a particular candidate to conform to what the algorithms have predicted.

That’s where companies like Cambridge Analytica come in. They paid thousands of people to take psychology tests and to surrender their own and their friends’ Facebook data. Then they compared all this data to infer how each of us would have answered that psychology test. Armed with our real or algorithmically determined psychological profiles, Cambridge Analytica surmised our individual neurotic makeups. And they figured out how to terrify each and every one of us.

That’s the greater collateral damage of social media. It’s not simply that they can get us to buy a particular product or vote for one candidate or another. It’s that their techniques bypass our higher brain functions. They use imagery and language specifically designed to evade our logic and empathy and appeal straight to our reptilian survival instincts.

These more primitive brain regions respond only to primitive stimulus: fear, hate and tribalism. It’s the part of us that gets activated when we see a car crash or a horror movie. That’s the state of mind these platforms want us to be in, because that’s when we are most easily manipulated.

Yes, we’ve been manipulated by ads for a century now. But TV and other forms of advertising generally happened in public. We all saw the same commercials, and they often cost so much that companies knew they had to get them right. Television networks would themselves censor ads that they felt would alienate their viewers or make fraudulent claims. It was manipulative, but for the most part, consumer advertising was aspirational.

Facebook figures out who or what each of us fears most, and then sells that information to the creators of false memes and the like, who deliver those fears directly to our news feeds. This, in turn, makes the world a more fearful, hostile and dangerous place.

To ask why one should care is a luxury of privilege. Data harvesting arguably matters most when it’s used against the economically disadvantaged. It’s not just in China that social media data are used to evaluate credit worthiness and immigration status. By normalizing the harvesting of data, those of us with little to fear imperil the most vulnerable.

When Mark Zuckerberg started Facebook, a friend of his expressed surprise that people were surrendering so much personal data to the platform. “I don’t know why,” Zuckerberg said. “They trust me. Dumb …”

We may have been dumb to trust Facebook with our data in the first place. Now we know they’ve been using the data to make us even dumber.

Reposted from the Rushkoff list, subscribe here

Photo by Book Catalog

The post How Facebook Exploited Us All appeared first on P2P Foundation.

For years, I’ve been looking beyond the technical and economic limits which we’ve developed during our fast few decades of personal computing and digital networking.  I’ve looked at co-creative potentials envisioned by Internet pioneers, and added ideas on the basic nature of communication and community.  This book reflects all of that research– but in most ways, it’s just a beginning.

Co- creating tech and culture

We can rebuild communications technologies (tools, techniques and systems) to foster the emergence of communities and inter-communities of autonomous peers. It’s an immense challenge, however, because we must displace corporations which marry communities to software platforms based on financially extractive models.

We can foster just and effective dialogue between tech and culture with these open tech goals:

1. All globally valuable communications software should be, or become, freely available to diverse digital networks.

2. Network participants should be able to use any software which meets systemic specifications for protocols or APIs.

Humanity first

I believe that p2p networking technology is crucial to a sustainable future.  However, it won’t get anyone out of the creative and social work we need to do together.

Communication and collaboration are deeply human. People must share ideas and activities to discover common interests, to plan and work fairly together, and to develop true community.

Putting humanity first, we won’t predetermine social tools and techniques according to currently usable software, including our evolving programming languages.  However, we’ll always need to refine tools and techniques through shared experiences with usable software.

Software applications and functions

Many processes described in Peer-to-Peer Digital Networking could be integrated into a small set of signaling and storage applications. However, they’ll probably be explored in various networks before they’re fully realized anywhere. Also, complex projects require coordinating functions which my book doesn’t yet mention. For instance, I’m developing a fractal process management system for objectives with unlimited levels of complexity.

I’m drafting a new paper on the open ecosystem of tools and techniques we need to support p2p organizing, including essential systems such as notifications, scheduling and calendars.  Designers can directly integrate such systems with this book’s networking models.

Coordinated Goals

I’ll openly develop Peer-to-Peer Digital Networking based on feedback, and I’ll use Agreement-Based Organization to help build co-authored versions of its networking models.  Ideally, participation will be open to all sincerely interested people, via distributive network management principles.

Agreement-Based Organization will always be separately available, and both documents will always be licensed for sharing and adaptation.  However, I believe that some form of agreement-based organization will receive increased attention as a component within a co-authored p2p networking model.

I’ll coordinate my goals with harmonious efforts in existing projects and communities such as Value Flows. We’ll only need a small fraction of our co-creative resources to develop open technologies for all people.

How you can help

I want and need feedback on Peer-to-Peer Digital Networking.  I’d especially appreciate if feedback reflects these framing questions:

1. Does this technology create ability for people to communicate or organize?

2. If this technology restricts ability to communicate or organize, should that be a technical standard or a community standard?

Maybe you have ideas I haven’t encountered before!  Feel free to comment directly on the document or email me, and share your thoughts on how we can emerge into an Information Age.

The post Peer-to-Peer Digital Networking: How the Internet Should Work appeared first on P2P Foundation.

Lifezones By Peter Halasz [CC-BY-SA-2.5-2.0-1.0 ], via Wikimedia Commons

I. First, the list of commons economic terms in the original article has a very notable omission:

cooperative

I suggest that we avoid coining new words, phrases and “commons jargon” for ideas and  terms that already exist and have reasonably well-established meanings in public and academic discourse. Language is one of our most important commons and its conservation and good stewardship is important.

Suggested terms with utility for commons economics:

• subsidiarity  In my opinion this may be the single most important concept in modern society. “Subsidiarity is an organising principle of decentralisation, stating that a matter ought to be handled by the smallest, lowest, or least centralised authority capable of addressing that matter effectively. The Oxford English Dictionary defines subsidiarity as the idea that a central authority should have a subsidiary function, performing only those tasks which cannot be performed effectively at a more immediate or local level. The concept is applicable in the fields of government, political science, cybernetics, management, military, and, … In political theory, subsidiarity is sometimes viewed as a principle entailed by the idea of federalism.” http://en.wikipedia.org/wiki/Subsidiarity
• conditional ownership
• equity
• commonwealth
• absentee ownership
• doctrine of laches
• rule against perpetuities
  
• community land trust
• community property
• concurrent estate
• conservation easement
• public easement
• public interest
• beneficial interest
• deed restriction
• covenant
• co-housing
• condominium
• benefit association
• mutual association
• benefit corporation

II. Secondly, we might consider referring to some existing top-level vocabularies (data dictionaries, ontologies, etc.) and perhaps building the commons-based economics vocabulary as an extension (specialized domain) of one or more of these.

Below is a graphic of the GoodRelations e-comerce vocabulary (click to enlarge in another window). I include this graphic not for its specific terminology but because it conveys several concepts at a glance. The use of a Unified Modeling Language (UML) diagram would allow us to  show terms grouped into logical classes and the relationships between those classes. But this is not only a step towards a standardized and machine-readable dictionary of terms; it is also a model of economic processes. I think this would be a very useful kind of model to create for an Economics of the Commons. Rather than invent the Commons Economy Model from scratch we could borrow from existing models like GoodRelations and adapt them as necessary.

At the most basic level, such a diagram would allow us to hyperlink each term to a standard definition such as those given in the UNITED NATIONS METADATA COMMON VOCABULARY. Note that in the UN Metadata dictionary each term is not only defined but there are references to relevant organizations, standards, specifications, urls, etc.

Such a model could be created and updated collaboratively using tools like Prezi, Mindmap, Debategraph, etc.

Once we create our model, software engineers can render it into various machine-readable protocols such as XML, RDF, OWL, etc.

(click on the image for an enlarged view)

Other examples of standardized vocabularies  designed for  both human-readable and machine-readable information exchange:

UNITED NATIONS METADATA COMMON VOCABULARY (2009) http://unstats.un.org/unsd/dnss/docs-nqaf/04_sdmx_cog_annex_4_mcv_2009.pdf

Other UN data and metadata dictionaries, vocabularies, data sets, etc. http://data.un.org/Default.aspx

The National Information Exchange Model (NIEM), which is XML-based, has a variety of schemas (vocabularies) used to facilitate information exchange among partners in various disciplines, government-wide. It’s about achieving interoperability. Think of the NIEM data model as a mature and stable data dictionary of agreed-upon terms, definitions, and formats, independent of how information is stored in individual agency systems. www.niem.gov/technical/Pages/niem.aspx

GoodRelations  is a standardized vocabulary (also known as “schema”, “data dictionary”, or “ontology”) for product, price, store, company data, etc. GoodRelations is now fully compatible with the HTML5 microdata specification and can be used as an extension for the schema.org vocabulary. www.heppnetz.de/ontologies/goodrelations/v1

Schema.org The schemas are a set of ‘types’, each associated with a set of properties. The types are arranged in a hierarchy. http://schema.org/docs/full.html

The geopolitical ontology, developed by the Food and Agriculture Organization of the United Nations (FAO), provides names in seven languages (Arabic, Chinese, French, English, Spanish, Russian and Italian) and identifiers in various international coding systems (ISO2, ISO3, AGROVOC, FAOSTAT, FAOTERM, GAUL, UN, UNDP and DBPediaID codes) for territories and groups and tracks historical changes from 1985 up until today;[2] provides geolocation (geographical coordinates); implements relationships among countries and countries, or countries and groups, including properties such as has border with, is predecessor of, is successor of, is administered by, has members, and is in group; and disseminates country statistics including country area, land area, agricultural area, GDP or population. http://en.wikipedia.org/wiki/Geopolitical_ontology

Lists of other ontologies:
http://en.wikipedia.org/wiki/Ontology_%28information_science%29

http://semanticweb.org/wiki/Ontology#Ontologies_on_semanticweb.org

Related articles

• Solidarity, subsidiarity, and principled sanity (insightscoop.typepad.com)
• Schema.org, Learning Resource Metadata Initiative Join Hands In Boost To Educational Content Searches (semanticweb.com)
• Schema.org Gets New eCommerce Features (nexcess.net)
• Science of the Commons? (Poor Richard’s Almanack 2.0)

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