Until now, technology that readily identifies everyone based on his or her face has been taboo because of its radical erosion of privacy. Tech companies capable of releasing such a tool have refrained from doing so; in 2011, Google’s chairman at the time said it was the one technology the company had held back because it could be used “in a very bad way.” Some large cities, including San Francisco, have barred police from using facial
But without public scrutiny, more than 600 law enforcement agencies have started using Clearview in the past year, according to the company, which declined to provide a list. recognition technology.
Facial recognition technology has always been controversial. It makes people nervous about Big Brother. It has a tendency to deliver false matches for certain groups, like people of color. And some facial recognition products used by the police — including Clearview’s — haven’t been vetted by independent experts.
Clearview deployed current and former Republican officials to approach police forces, offering free trials and annual licenses for as little as $2,000. Mr. Schwartz tapped his political connections to help make government officials aware of the tool, according to Mr. Ton-That.
“We have no data to suggest this tool is accurate,” said Clare Garvie, a researcher at Georgetown University’s Center on Privacy and Technology, who has studied the government’s use of facial recognition. “The larger the database, the larger the risk of misidentification because of the doppelgänger effect. They’re talking about a massive database of random people they’ve found on the internet.”
Part of the problem stems from a lack of oversight. There has been no real public input into adoption of Clearview’s software, and the company’s ability to safeguard data hasn’t been tested in practice. Clearview itself remained highly secretive until late 2019.
The software also appears to explicitly violate policies at Facebook and elsewhere against collecting users’ images en masse.
while there’s underlying code that could theoretically be used for augmented reality glasses that could identify people on the street, Ton-That said there were no plans for such a design.
facial recognition bans are the wrong way to fight against modern surveillance. Focusing on one particular identification method misconstrues the nature of the surveillance society we’re in the process of building. Ubiquitous mass surveillance is increasingly the norm. In countries like China, a surveillance infrastructure is being built by the government for social control. In countries like the United States, it’s being built by corporations in order to influence our buying behavior, and is incidentally used by the government.
People can be identified at a distance by their heart beat or by their gait, using a laser-based system. Cameras are so good that they can read fingerprints and irispatterns from meters away. And even without any of these technologies, we can always be identified because our smartphones broadcast unique numbers called MAC addresses.
The data broker industry is almost entirely unregulated; there’s only one law — passed in Vermont in 2018 — that requires data brokers to register and explain in broad terms what kind of data they collect.
Until now, technology that readily identifies everyone based on his or her face has been taboo because of its radical erosion of privacy. Tech companies capable of releasing such a tool have refrained from doing so; in 2011, Google’s chairman at the time said it was the one technology the company had held back because it could be used “in a very bad way.” Some large cities, including San Francisco, have barred police from using facial recognition technology.
the type of data: wikipedia. the dangers of learning from wikipedia. how individuals can organize mitigate some of these dangers. wikidata, algorithms.
IBM Watson is using wikipedia by algorythms making sense, AI system
youtube videos debunked of conspiracy theories by using wikipedia.
semantic relatedness, Word2Vec
how does algorithms work: large body of unstructured text. picks specific words
lots of AI learns about the world from wikipedia. the neutral point of view policy. WIkipedia asks editors present as proportionally as possible. Wikipedia biases: 1. gender bias (only 20-30 % are women).
conceptnet. debias along different demographic dimensions.
citations analysis gives also an idea about biases. localness of sources cited in spatial articles. structural biases.
geolocation on Twitter by County. predicting the people living in urban areas. FB wants to push more local news.
danger (biases) #3. wikipedia search results vs wkipedia knowledge panel.
collective action against tech: Reddit, boycott for FB and Instagram.
data labor: what the primary resources this companies have. posts, images, reviews etc.
boycott, data strike (data not being available for algorithms in the future). GDPR in EU – all historical data is like the CA Consumer Privacy Act. One can do data strike without data boycott. general vs homogeneous (group with shared identity) boycott.
the wikipedia SPAM policy is obstructing new editors and that hit communities such as women.
how to access at different levels. methods and methodological concerns. ethical concerns, legal concerns,
tweetdeck for advanced Twitter searches. quoting, likes is relevant, but not enough, sometimes screenshot
social listening platforms: crimson hexagon, parsely, sysomos – not yet academic platforms, tools to setup queries and visualization, but difficult to algorythm, the data samples etc. open sources tools (Urbana, Social Media microscope: SMILE (social media intelligence and learning environment) to collect data from twitter, reddit and within the platform they can query Twitter. create trend analysis, sentiment analysis, Voxgov (subscription service: analyzing political social media)
graduate level and faculty research: accessing SM large scale data web scraping & APIs Twitter APIs. Jason script, Python etc. Gnip Firehose API ($) ; Web SCraper Chrome plugin (easy tool, Pyhon and R created); Twint (Twitter scraper)
Facepager (open source) if not Python or R coder. structure and download the data sets.
TAGS archiving google sheets, uses twitter API. anything older 7 days not avaialble, so harvest every week.
social feed manager (GWUniversity) – Justin Litman with Stanford. Install on server but allows much more.
legal concerns: copyright (public info, but not beyond copyrighted). fair use argument is strong, but cannot publish the data. can analyize under fair use. contracts supercede copyright (terms of service/use) licensed data through library.
methods: sampling concerns tufekci, 2014 questions for sm. SM data is a good set for SM, but other fields? not according to her. hashtag studies: self selection bias. twitter as a model organism: over-represnted data in academic studies.
methodological concerns: scope of access – lack of historical data. mechanics of platform and contenxt: retweets are not necessarily endorsements.
ethical concerns. public info – IRB no informed consent. the right to be forgotten. anonymized data is often still traceable.
table discussion: digital humanities, journalism interested, but too narrow. tools are still difficult to find an operate. context of the visuals. how to spread around variety of majors and classes. controversial events more likely to be deleted.
takedowns, lies and corrosion: what is a librarian to do: trolls, takedown,
development kit circulation. familiarity with the Oculus Rift resulted in lesser reservation. Downturn also.
An experience station. clean up free apps.
question: spherical video, video 360.
safety issues: policies? instructional perspective: curating,WI people: user testing. touch controllers more intuitive then xbox controller. Retail Oculus Rift
app Scatchfab. 3modelviewer. obj or sdl file. Medium, Tiltbrush.
College of Liberal Arts at the U has their VR, 3D print set up.
Penn State (Paul, librarian, kiniseology, anatomy programs), Information Science and Technology. immersive experiences lab for video 360.
CALIPHA part of it is xrlibraries. libraries equal education. content provider LifeLiqe STEM library of AR and VR objects. https://www.lifeliqe.com/
digital humanities is born f the encounter between traditional humanities and computational methods.
p. 5. From Humanism to Humanities
While the foundations of of humanistic inquiry and the liberal arts can be traced back in the west to the medieval trivium and quadrivium, the modern and human sciences are rooted in the Renaissance shift from a medieval, church dominated, theocratic world view to be human centered one period the gradual transformation of early humanism into the disciplines that make up the humanities today Was profoundly shaped by the editorial practices involved in the recovery of the corpus of works from classical antiquity
P. 6. The shift from humanism to the institution only sanctioned disciplinary practices and protocols that we associate with the humanities today is best described as a gradual process of subdivision and specialization.
P. 7. Text-based disciplines in studies (classics, literature, philosophy, the history of ideas) make up, from the very start, the core of both the humanities and the great books curricular instituted in the 1920s and 1930s.
P. 10. Transmedia modes of argumentation
In the 21st-century, we communicate in media significantly more varied, extensible, and multiplicative then linear text. From scalable databases to information visualizations, from video lectures to multi-user virtual platforms serious content and rigorous argumentation take shape across multiple platforms in media. The best digital humanities pedagogy and research projects train students both in “reading “and “writing “this emergent rhetoric and in understanding how the reshape and three model humanistic knowledge. This means developing critically informed literacy expensive enough to include graphic design visual narrative time based media, and the development of interfaces (Rather then the rote acceptance of them as off-the-shelf products).
P. 11. The visual becomes ever more fundamental to the digital humanities, in ways that compliment, enhance, and sometimes are in pension with the textual.
There is no either/or, no simple interchangeability between language and the visual, no strict sub ordination of the one to the other. Words are themselves visual but other kinds of visual constructs do different things. The question is how to use each to its best effect into device meaningful interpret wing links, to use Theodor Nelson’s ludic neologism.
P. 11. The suite of expressive forms now encompasses the use of sound, motion graphics, animation, screen capture, video, audio, and the appropriation and into remix sink of code it underlines game engines. This expanded range of communicative tools requires those who are engaged in digital humanities world to familiarize themselves with issues, discussions, and debates in design fields, especially communication and interaction design. Like their print predecessors, form at the convention center screen environments can become naturalized all too quickly, with the results that the thinking that informed they were designed goes unperceived.
For digital humanists, design is a creative practice harnessing cultural, social, economic, and technological constraints in order to bring systems and objects into the world. Design in dialogue with research is simply a picnic, but when used to pose in frame questions about knowledge, design becomes an intellectual method. Digital humanities is a production based in Denver in which theoretical issues get tested in the design of implementations and implementations or loci after your radical reflection and elaboration.
Did you thaw humanists have much to learn from communication and media design about how to juxtapose and integrate words and images create hire he is of reading, Forge pathways of understanding, deployed grades in templates to best effect, and develop navigational schemata that guide in produce meaningful interactions.
P. 15. The field of digital digital humanities me see the emergence of polymaths who can “ do it all” : Who can research, write, shoot, edit, code, model, design, network, and dialogue with users. But there is also ample room for specialization and, particularly, for collaboration.
P. 16. Computational activities in digital humanities.
The foundational layer, computation, relies on principles that are, on the surface, at odds with humanistic methods.
P. 17. The second level involves processing in a way that conform to computational capacities, and this were explored in the first generation of digital scholarship and stylometrics, concordance development, and indexing.
Duration, analysis, editing, modeling.
Duration, analysis, editing, and modeling comprise fundamental activities at the core of digital humanities. Involving archives, collections, repositories, and other aggregations of materials, duration is the selection and organization of materials in an interpretive framework, argument, or exhibit.
P. 18. Analysis refers to the processing of text or data: statistical and quantitative methods of analysis have brought close readings of texts (stylometrics and genre analysis, correlation, comparisons of versions for alter attribution or usage patterns ) into dialogue with distant reading (The crunching cuff large quantities of information across the corpus of textual data or its metadata).
Edit think has been revived with the advent of digital media and the web and to continue to be an integral activity in textual as well as time based formats.
P. 18. Model link highlights the notion of content models- shapes of argument expressed in information structures in their design he digital project is always an expression of assumptions about knowledge: usually domain specific knowledge given an explicit form by the model in which it is designed.
P. 19. Each of these areas of activity- cure ration, analysis, editing, and modeling is supported by the basic building blocks of digital activity. But they also depend upon networks and infrastructure that are cultural and institutional as well as technical. Servers, software, and systems administration are key elements of any project design.
P. 30. Digital media are not more “evolved” have them print media nor are books obsolete; but the multiplicity of media in the very processes of mediation entry mediation in the formation of cultural knowledge and humanistic inquiry required close attention. Tug link between distant and clothes, macro and micro, and surface in depth becomes the norm. Here, we focus on the importance of visualization to the digital humanities before moving on to other, though often related, genre and methods such as Locative investigation, thick mapping, animated archives, database documentaries, platform studies, and emerging practices like cultural analytics, data mining and humanities gaming.
P. 35. Fluid texture out what he refers to the mutability of texts in the variants and versions Whether these are produced through Authorial changes, anything, transcription, translation, or print production
Cultural Analytics, aggregation, and data mining.
The field of cultural Analytics has emerged over the past few years, utilizing tools of high-end computational analysis and data visualization today sect large-scale coach data sets. Cultural Analytic does Not analyze cultural artifacts, but operates on the level of digital models of this materials in aggregate. Again, the point is not to pit “close” hermeneutic reading against “distant” data mapping, but rather to appreciate the synergistic possibilities and tensions that exist between a hyper localized, deep analysis and a microcosmic view
Data mining is a term that covers a host of picnics for analyzing digital material by “parameterizing” some feature of information and extract in it. This means that any element of a file or collection of files that can be given explicit specifications, or parameters, can be extracted from those files for analysis.
Understanding the rehtoric of graphics is another essential skill, therefore, in working at a skill where individual objects are lost in the mass of processed information and data. To date, much humanities data mining has merely involved counting. Much more sophisticated statistical methods and use of probability will be needed for humanists to absorb the lessons of the social sciences into their methods
P. 42. Visualization and data design
Currently, visualization in the humanities uses techniques drawn largely from the social sciences, Business applications, and the natural sciences, all of which require self-conscious criticality in their adoption. Such visual displays including graphs and charts, may present themselves is subjective or even unmediated views of reality, rather then is rhetorical constructs.
Warwick, C., Terras, M., & Nyhan, J. (2012). Digital humanities in practice . London: Facet Publishing in association with UCL Centre for Digital Humanities.
For abstainers, breaking up with Facebook freed up about an hour a day, on average, and more than twice that for the heaviest users.
research led by Ethan Kross, a professor of psychology at the University of Michigan, has found that high levels of passive browsing on social media predict lowered moods, compared to more active engagement.
The phenomenon of people requesting procedures to resemble their digital image has been referred to – sometimes flippantly, sometimes as a harbinger of end times – as “Snapchat dysmorphia”. The term was coined by the cosmetic doctor Tijion Esho, founder of the Esho clinics in London and Newcastle.
A recent report in the US medical journal JAMA Facial Plastic Surgery suggested that filtered images’ “blurring the line of reality and fantasy” could be triggering body dysmorphic disorder (BDD), a mental health condition where people become fixated on imagined defects in their appearance.
A 2017 study into “selfitis”, as the obsessive taking of selfies has been called, found a range of motivations, from seeking social status to shaking off depressive thoughts and – of course – capturing a memorable moment. Another study suggested that selfies served “a private and internal purpose”, with the majority never shared with anyone or posted anywhere – terabytes, even petabytes of photographs never to be seen by anyone other than their subject.
However, a 2017 study in the journal Cognitive Research: Principles and Implications found that people only recognised manipulated images 60%-65% of the time.
guide (available as PDF here and Google Doc here) to offer some explanations of how to avoid copyright infringement by using media that you can legally re-use for classroom projects including blog posts, web pages, videos, slideshows, and podcasts. The guide also includes 21 places to find media to use in classroom projects.
THERE’S A MEME on Instagram, circulated by a group called “Born Liberal.” “Born Liberal” was a creation of the Internet Research Agency, the Russian propaganda wing
Conversations around the IRA’s operations traditionally have focused on Facebook and Twitter, but like any hip millennial, the IRA was actually most obsessive about Instagram.
the IRA deployed 3,841 accounts, including several personas that “regularly played hashtag games.” That approach paid off; 1.4 million people engaged with the tweets, leading to nearly 73 million engagements. Most of this work was focused on news, while on Facebook and Instagram, the Russians prioritized “deeper relationships,” according to the researchers. On Facebook, the IRA notched a total of 3.3 million page followers, who engaged with their politically divisive content 76.5 million times. Russia’s most popular pages targeted the right wing and the black community. The trolls also knew their audiences; they deployed Pepe memes at pages intended for right-leaning millennials, but kept them away from posts directed at older conservative Facebook users. Not every attempt was a hit; while 33 of the 81 IRA Facebook pages had over 1,000 followers, dozens had none at all.
The report also points out new links between the IRA’s pages and Wikileaks, which helped disseminate hacked emails from Clinton campaign manager John Podesta
“While many people think of memes as “cat pictures with words,” the Defense Department and DARPA have studied them for years as a powerful tool of cultural influence, capable of reinforcing or even changing values and behavior.
“over the past five years, disinformation has evolved from a nuisance into high-stakes information war.” And yet, rather than fighting back effectively, Americans are battling each other over what to do about it.
A year after the Meme Warfare Center proposal was published, DARPA, the Pentagon agency that develops new military technology, commissioned a four-year study of memetics. The research was led by Dr. Robert Finkelstein, founder of the Robotic Technology Institute, and an academic with a background in physics and cybernetics.
Finkelstein’s study of “Military Memetics” centered on a basic problem in the field, determining “whether memetics can be established as a science with the ability to explain and predict phenomena.” It still had to be proved, in other words, that memes were actual components of reality and not just a nifty concept with great marketing.
United States digital literacy frameworks tend to focus on educational policy details and personal empowerment, the latter encouraging learners to become more effective students, better creators, smarter information consumers, and more influential members of their community.
National policies are vitally important in European digital literacy work, unsurprising for a continent well populated with nation-states and struggling to redefine itself, while still trying to grow economies in the wake of the 2008 financial crisis and subsequent financial pressures
African digital literacy is more business-oriented.
Middle Eastern nations offer yet another variation, with a strong focus on media literacy. As with other regions, this can be a response to countries with strong state influence or control over local media. It can also represent a drive to produce more locally-sourced content, as opposed to consuming material from abroad, which may elicit criticism of neocolonialism or religious challenges.
p. 14 Digital literacy for Humanities: What does it mean to be digitally literate in history, literature, or philosophy? Creativity in these disciplines often involves textuality, given the large role writing plays in them, as, for example, in the Folger Shakespeare Library’s instructor’s guide. In the digital realm, this can include web-based writing through social media, along with the creation of multimedia projects through posters, presentations, and video. Information literacy remains a key part of digital literacy in the humanities. The digital humanities movement has not seen much connection with digital literacy, unfortunately, but their alignment seems likely, given the turn toward using digital technologies to explore humanities questions. That development could then foster a spread of other technologies and approaches to the rest of the humanities, including mapping, data visualization, text mining, web-based digital archives, and “distant reading” (working with very large bodies of texts). The digital humanities’ emphasis on making projects may also increase
Digital Literacy for Business: Digital literacy in this world is focused on manipulation of data, from spreadsheets to more advanced modeling software, leading up to degrees in management information systems. Management classes unsurprisingly focus on how to organize people working on and with digital tools.
Digital Literacy for Computer Science: Naturally, coding appears as a central competency within this discipline. Other aspects of the digital world feature prominently, including hardware and network architecture. Some courses housed within the computer science discipline offer a deeper examination of the impact of computing on society and politics, along with how to use digital tools. Media production plays a minor role here, beyond publications (posters, videos), as many institutions assign multimedia to other departments. Looking forward to a future when automation has become both more widespread and powerful, developing artificial intelligence projects will potentially play a role in computer science literacy.
In traditional instruction, students’ first contact with new ideas happens in class, usually through direct instruction from the professor; after exposure to the basics, students are turned out of the classroom to tackle the most difficult tasks in learning — those that involve application, analysis, synthesis, and creativity — in their individual spaces. Flipped learning reverses this, by moving first contact with new concepts to the individual space and using the newly-expanded time in class for students to pursue difficult, higher-level tasks together, with the instructor as a guide.
Let’s take a look at some of the myths about flipped learning and try to find the facts.
Myth: Flipped learning is predicated on recording videos for students to watch before class.
Fact: Flipped learning does not require video. Although many real-life implementations of flipped learning use video, there’s nothing that says video must be used. In fact, one of the earliest instances of flipped learning — Eric Mazur’s peer instruction concept, used in Harvard physics classes — uses no video but rather an online text outfitted with social annotation software. And one of the most successful public instances of flipped learning, an edX course on numerical methods designed by Lorena Barba of George Washington University, uses precisely one video. Video is simply not necessary for flipped learning, and many alternatives to video can lead to effective flipped learning environments [http://rtalbert.org/flipped-learning-without-video/].
Fact: Flipped learning optimizes face-to-face teaching. Flipped learning may (but does not always) replace lectures in class, but this is not to say that it replaces teaching. Teaching and “telling” are not the same thing.
Myth: Flipped learning has no evidence to back up its effectiveness.
Fact: Flipped learning research is growing at an exponential pace and has been since at least 2014. That research — 131 peer-reviewed articles in the first half of 2017 alone — includes results from primary, secondary, and postsecondary education in nearly every discipline, most showing significant improvements in student learning, motivation, and critical thinking skills.
Myth: Flipped learning is a fad.
Fact: Flipped learning has been with us in the form defined here for nearly 20 years.
Myth: People have been doing flipped learning for centuries.
Fact: Flipped learning is not just a rebranding of old techniques. The basic concept of students doing individually active work to encounter new ideas that are then built upon in class is almost as old as the university itself. So flipped learning is, in a real sense, a modern means of returning higher education to its roots. Even so, flipped learning is different from these time-honored techniques.
Myth: Students and professors prefer lecture over flipped learning.
Fact: Students and professors embrace flipped learning once they understand the benefits. It’s true that professors often enjoy their lectures, and students often enjoy being lectured to. But the question is not who “enjoys” what, but rather what helps students learn the best.They know what the research says about the effectiveness of active learning
Assertion: Flipped learning provides a platform for implementing active learning in a way that works powerfully for students.
The Exposure Approach: we don’t provide a way for participants to determine if they learned anything new or now have the confidence or competence to apply what they learned.
The Exemplar Approach: from ‘show and tell’ for adults to show, tell, do and learn.
The Tutorial Approach: Getting a group that can meet at the same time and place can be challenging. That is why many faculty report a preference for self-paced professional development.build in simple self-assessment checks. We can add prompts that invite people to engage in some sort of follow up activity with a colleague. We can also add an elective option for faculty in a tutorial to actually create or do something with what they learned and then submit it for direct or narrative feedback.
The Course Approach: a non-credit format, these have the benefits of a more structured and lengthy learning experience, even if they are just three to five-week short courses that meet online or in-person once every week or two.involve badges, portfolios, peer assessment, self-assessment, or one-on-one feedback from a facilitator
The Academy Approach: like the course approach, is one that tends to be a deeper and more extended experience. People might gather in a cohort over a year or longer.Assessment through coaching and mentoring, the use of portfolios, peer feedback and much more can be easily incorporated to add a rich assessment element to such longer-term professional development programs.
The Mentoring Approach: The mentors often don’t set specific learning goals with the mentee. Instead, it is often a set of structured meetings, but also someone to whom mentees can turn with questions and tips along the way.
The Coaching Approach: A mentor tends to be a broader type of relationship with a person.A coaching relationship tends to be more focused upon specific goals, tasks or outcomes.
The Peer Approach:This can be done on a 1:1 basis or in small groups, where those who are teaching the same courses are able to compare notes on curricula and teaching models. They might give each other feedback on how to teach certain concepts, how to write syllabi, how to handle certain teaching and learning challenges, and much more. Faculty might sit in on each other’s courses, observe, and give feedback afterward.
The Self-Directed Approach:a self-assessment strategy such as setting goals and creating simple checklists and rubrics to monitor our progress. Or, we invite feedback from colleagues, often in a narrative and/or informal format. We might also create a portfolio of our work, or engage in some sort of learning journal that documents our thoughts, experiments, experiences, and learning along the way.
In 2014, administrators at Central Piedmont Community College (CPCC) in Charlotte, North Carolina, began talks with members of the North Carolina State Board of Community Colleges and North Carolina Community College System (NCCCS) leadership about starting a CBE program.
Building on an existing project at CPCC for identifying the elements of a digital learning environment (DLE), which was itself influenced by the EDUCAUSE publication The Next Generation Digital Learning Environment: A Report on Research,1 the committee reached consensus on a DLE concept and a shared lexicon: the “Digital Learning Environment Operational Definitions,