Community science draws on the power of the crowd


Essay by Amber Dance: “In community science, also called participatory science, non-professionals contribute their time, energy or expertise to research. (The term ‘citizen science’ is also used but can be perceived as excluding non-citizens.)

Whatever name is used, the approach is more popular than ever and even has journals dedicated to it. The number of annual publications mentioning ‘citizen science’ went from 151 in 2015 to more than 640 in 2021, according to the Web of Science database. Researchers from physiologists to palaeontologists to astronomers are finding that harnessing the efforts of ordinary people is often the best route to the answers they seek.

“More and more funding organizations are actually promoting this type of participatory- and citizen-science data gathering,” says Bálint Balázs, managing director of the Environmental Social Science Research Group in Budapest, a non-profit company focusing on socio-economic research for sustainability.

Community science is also a great tool for outreach, and scientists often delight in interactions with amateur researchers. But it’s important to remember that community science is, foremost, a research methodology like any other, with its own requirements in terms of skill and effort.

“To do a good project, it does require an investment in time,” says Darlene Cavalier, founder of SciStarter, an online clearing house that links research-project leaders with volunteers. “It’s not something where you’re just going to throw up a Google form and hope for the best.” Although there are occasions when scientific data are freely and easily available, other projects create significant costs.

No matter what the topic or approach, people skills are crucial: researchers must identify and cultivate a volunteer community and provide regular feedback or rewards. With the right protocols and checks and balances, the quality of volunteer-gathered data often rivals or surpasses that achieved by professionals.

“There is a two-way learning that happens,” says Tina Phillips, assistant director of the Center for Engagement in Science and Nature at Cornell University in Ithaca, New York. “We all know that science is better when there are more voices, more perspectives.”…(More)”

A Prehistory of Social Media


Essay by Kevin Driscoll: “Over the past few years, I’ve asked dozens of college students to write down, in a sentence or two, where the internet came from. Year after year, they recount the same stories about the US government, Silicon Valley, the military, and the threat of nuclear war. A few students mention the Department of Defense’s ARPANET by name. Several get the chronology wrong, placing the World Wide Web before the internet or expressing confusion about the invention of email. Others mention “tech wizards” or “geniuses” from Silicon Valley firms and university labs. No fewer than four students have simply written, “Bill Gates.”

Despite the internet’s staggering scale and global reach, its folk histories are surprisingly narrow. This mismatch reflects the uncertain definition of “the internet.” When nonexperts look for internet origin stories, they want to know about the internet as they know it, the internet they carry around in their pockets, the internet they turn to, day after day. Yet the internet of today is not a stable object with a single, coherent history. It is a dynamic socio-technical phenomenon that came into being during the 1990s, at the intersection of hundreds of regional, national, commercial, and cooperative networks—only one of which was previously known as “the internet.” In short, the best-known histories describe an internet that hasn’t existed since 1994. So why do my students continue to repeat stories from 25 years ago? Why haven’t our histories kept up?

The standard account of internet history took shape in the early 1990s, as a mixture of commercial online services, university networks, and local community networks mutated into something bigger, more commercial, and more accessible to the general public. As hype began to build around the “information superhighway,” people wanted a backstory. In countless magazines, TV news reports, and how-to books, the origin of the internet was traced back to ARPANET, the computer network created by the Advanced Research Projects Agency during the Cold War. This founding mythology has become a resource for advancing arguments on issues related to censorship, national sovereignty, cybersecurity, privacy, net neutrality, copyright, and more. But with only this narrow history of the early internet to rely on, the arguments put forth are similarly impoverished…(More)”.

Uncovering the genetic basis of mental illness requires data and tools that aren’t just based on white people


Article by Hailiang Huang: “Mental illness is a growing public health problem. In 2019, an estimated 1 in 8 people around the world were affected by mental disorders like depression, schizophrenia or bipolar disorder. While scientists have long known that many of these disorders run in families, their genetic basis isn’t entirely clear. One reason why is that the majority of existing genetic data used in research is overwhelmingly from white people.

In 2003, the Human Genome Project generated the first “reference genome” of human DNA from a combination of samples donated by upstate New Yorkers, all of whom were of European ancestry. Researchers across many biomedical fields still use this reference genome in their work. But it doesn’t provide a complete picture of human genetics. Someone with a different genetic ancestry will have a number of variations in their DNA that aren’t captured by the reference sequence.

When most of the world’s ancestries are not represented in genomic data sets, studies won’t be able to provide a true representation of how diseases manifest across all of humanity. Despite this, ancestral diversity in genetic analyses hasn’t improved in the two decades since the Human Genome Project announced its first results. As of June 2021, over 80% of genetic studies have been conducted on people of European descent. Less than 2% have included people of African descent, even though these individuals have the most genetic variation of all human populations.

To uncover the genetic factors driving mental illness, ISinéad Chapman and our colleagues at the Broad Institute of MIT and Harvard have partnered with collaborators around the world to launch Stanley Global, an initiative that seeks to collect a more diverse range of genetic samples from beyond the U.S. and Northern Europe, and train the next generation of researchers around the world. Not only does the genetic data lack diversity, but so do the tools and techniques scientists use to sequence and analyze human genomes. So we are implementing a new sequencing technology that addresses the inadequacies of previous approaches that don’t account for the genetic diversity of global populations…(More).

Cloud labs and remote research aren’t the future of science – they’re here


Article by Tom Ireland: “Cloud labs mean anybody, anywhere can conduct experiments by remote control, using nothing more than their web browser. Experiments are programmed through a subscription-based online interface – software then coordinates robots and automated scientific instruments to perform the experiment and process the data. Friday night is Emerald’s busiest time of the week, as scientists schedule experiments to run while they relax with their families over the weekend.

There are still some things robots can’t do, for example lifting giant carboys (containers for liquids) or unwrapping samples sent by mail, and there are a few instruments that just can’t be automated. Hence the people in blue coats, who look a little like pickers in an Amazon warehouse. It turns out that they are, in fact, mostly former Amazon employees.

Plugging an experiment into a browser forces researchers to translate the exact details of every step into unambiguous code

Emerald originally employed scientists and lab technicians to help the facility run smoothly, but they were creatively stifled with so little to do. Poaching Amazon employees has turned out to be an improvement. “We pay them twice what they were getting at Amazon to do something way more fulfilling than stuffing toilet paper into boxes,” says Frezza. “You’re keeping someone’s drug-discovery experiment running at full speed.”

Further south in the San Francisco Bay Area are two more cloud labs, run by the company Strateos. Racks of gleaming life science instruments – incubators, mixers, mass spectrometers, PCR machines – sit humming inside large Perspex boxes known as workcells. The setup is arguably even more futuristic than at Emerald. Here, reagents and samples whizz to the correct workcell on hi-tech magnetic conveyor belts and are gently loaded into place by dextrous robot arms. Researchers’ experiments are “delocalised”, as Strateos’s executive director of operations, Marc Siladi, puts it…(More)”.

The End of Real Social Networks


Essay by Daron Acemoglu: “Social media platforms are not only creating echo chambers, propagating falsehoods, and facilitating the circulation of extremist ideas. Previous media innovations, dating back at least to the printing press, did that, too, but none of them shook the very foundations of human communication and social interaction.

CAMBRIDGE – Not only are billions of people around the world glued to their mobile phones, but the information they consume has changed dramatically – and not for the better. On dominant social-media platforms like Facebook, researchers have documented that falsehoods spread faster and more widely than similar content that includes accurate information. Though users are not demanding misinformation, the algorithms that determine what people see tend to favor sensational, inaccurate, and misleading content, because that is what generates “engagement” and thus advertising revenue.

As the internet activist Eli Pariser noted in 2011, Facebook also creates filter bubbles, whereby individuals are more likely to be presented with content that reinforces their own ideological leanings and confirms their own biases. And more recent research has demonstrated that this process has a major influence on the type of information users see.

Even leaving aside Facebook’s algorithmic choices, the broader social-media ecosystem allows people to find subcommunities that align with their interests. This is not necessarily a bad thing. If you are the only person in your community with an interest in ornithology, you no longer have to be alone, because you can now connect with ornithology enthusiasts from around the world. But, of course, the same applies to the lone extremist who can now use the same platforms to access or propagate hate speech and conspiracy theories.

No one disputes that social-media platforms have been a major conduit for hate speech, disinformation, and propaganda. Reddit and YouTube are breeding grounds for right-wing extremism. The Oath Keepers used Facebook, especially, to organize their role in the January 6, 2021, attack on the United States Capitol. Former US President Donald Trump’s anti-Muslim tweets were found to have fueled violence against minorities in the US.

True, some find such observations alarmist, noting that large players like Facebook and YouTube (which is owned by Google/Alphabet) do much more to police hate speech and misinformation than their smaller rivals do, especially now that better moderation practices have been developed. Moreover, other researchers have challenged the finding that falsehoods spread faster on Facebook and Twitter, at least when compared to other media.

Still others argue that even if the current social-media environment is treacherous, the problem is transitory. After all, novel communication tools have always been misused. Martin Luther used the printing press to promote not just Protestantism but also virulent anti-Semitism. Radio proved to be a powerful tool in the hands of demagogues like Father Charles Coughlin in the US and the Nazis in Germany. Both print and broadcast outlets remain full of misinformation to this day, but society has adjusted to these media and managed to contain their negative effects…(More)”.

The Low Threshold for Face Recognition in New Delhi


Article by Varsha Bansal: “Indian law enforcement is starting to place huge importance on facial recognition technology. Delhi police, looking into identifying people involved in civil unrest in northern India in the past few years, said that they would consider 80 percent accuracy and above as a “positive” match, according to documents obtained by the Internet Freedom Foundation through a public records request.

Facial recognition’s arrival in India’s capital region marks the expansion of Indian law enforcement officials using facial recognition data as evidence for potential prosecution, ringing alarm bells among privacy and civil liberties experts. There are also concerns about the 80 percent accuracy threshold, which critics say is arbitrary and far too low, given the potential consequences for those marked as a match. India’s lack of a comprehensive data protection law makes matters even more concerning.

The documents further state that even if a match is under 80 percent, it would be considered a “false positive” rather than a negative, which would make that individual “subject to due verification with other corroborative evidence.”

“This means that even though facial recognition is not giving them the result that they themselves have decided is the threshold, they will continue to investigate,” says Anushka Jain, associate policy counsel for surveillance and technology with the IFF, who filed for this information. “This could lead to harassment of the individual just because the technology is saying that they look similar to the person the police are looking for.” She added that this move by the Delhi Police could also result in harassment of people from communities that have been historically targeted by law enforcement officials…(More)”

Smart Streetlights are Casting a Long Shadow Over Our Cities


Article by Zhile Xie: “This is not a surveillance system—nobody is watching it 24 hours a day,” said Erik Caldwell, director of economic development in San Diego, in an interview where he was asked if the wide deployment of “smart” streetlights had turned San Diego into a surveillance city. Innocuous at first glance, this statement demonstrates the pernicious impact of artificial intelligence on new “smart” streetlight systems. As Caldwell suggests, a central human vision is important for the streetlight to function as a surveillance instrument. However, the lack of human supervision only suggests its enhanced capacity. Smart sensors are able to process and communicate environmental information that does not present itself in a visual format and does not rely on human interpretation. On the one hand, they reinforce streetlights’ function as a surveillance instrument, historically associated with light and visibility. On the other hand, in tandem with a wide range of sensors embedded in our everyday environment, they also enable for-profit data extraction on a vast scale,  under the auspices of a partnership between local governments and tech corporations. 

The streetlight was originally designed as a surveillance device and has been refined to that end ever since then. Its association with surveillance and security can be found as early as 400 BC. Citizens of Ancient Rome started to install an oil lamp in front of every villa to prevent tripping or thefts, and an enslaved person would be designated to watch the lamp—lighting was already paired with the notion of control through slavery. As Wolfgang Schivelbusch has detailed in his book Disenchanted Light, street lighting also emerged in medieval European cities alongside practices of policing. Only designated watchmen who carried a torch and a weapon were allowed to be out on the street. This ancient connection between security and visibility has been the basis of the wide deployment of streetlights in modern cities. Moreover, as Edwin Heathcote has explained in a recent article for the Architectural Review, gas streetlights were first introduced to Paris during Baron Haussmann’s restructuring of the city between 1853 and 1870, which was designed in part to prevent revolutionary uprisings. The invention of electric light bulbs in the late nineteenth century in Europe triggered new fears and imaginations around the use of streetlights for social control. For instance, in his 1894 dystopian novel The Land of the Changing Sun, W.N. Harben envisions an electric-optical device that makes possible 24-hour surveillance over the entire population of an isolated country, Alpha. The telescopic system is aided by an artificial “sun” that lights up the atmosphere all year round, along with networked observatories across the land that capture images of their surroundings, which are transmitted to a “throne room” for inspection by the king and police…(More)”.

Without appropriate metadata, data-sharing mandates are pointless


Article by Mark A. Musen: “Last month, the US government announced that research articles and most underlying data generated with federal funds should be made publicly available without cost, a policy to be implemented by the end of 2025. That’s atop other important moves. The European Union’s programme for science funding, Horizon Europe, already mandates that almost all data be FAIR (that is, findable, accessible, interoperable and reusable). The motivation behind such data-sharing policies is to make data more accessible so others can use them to both verify results and conduct further analyses.

But just getting those data sets online will not bring anticipated benefits: few data sets will really be FAIR, because most will be unfindable. What’s needed are policies and infrastructure to organize metadata.

Imagine having to search for publications on some topic — say, methods for carbon reclamation — but you could use only the article titles (no keywords, abstracts or search terms). That’s essentially the situation for finding data sets. If I wanted to identify all the deposited data related to carbon reclamation, the task would be futile. Current metadata often contain only administrative and organizational information, such as the name of the investigator and the date when the data were acquired.

What’s more, for scientific data to be useful to other researchers, metadata must sensibly and consistently communicate essentials of the experiments — what was measured, and under what conditions. As an investigator who builds technology to assist with data annotation, it’s frustrating that, in the majority of fields, the metadata standards needed to make data FAIR don’t even exist.

Metadata about data sets typically lack experiment-specific descriptors. If present, they’re sparse and idiosyncratic. An investigator searching the Gene Expression Omnibus (GEO), for example, might seek genomic data sets containing information on how a disease or condition manifests itself in young animals or humans. Performing such a search requires knowledge of how the age of individuals is represented — which in the GEO repository, could be age, AGE, age (after birth), age (years), Age (yr-old) or dozens of other possibilities. (Often, such information is missing from data sets altogether.) Because the metadata are so ad hoc, automated searches fail, and investigators waste enormous amounts of time manually sifting through records to locate relevant data sets, with no guarantee that most (or any) can be found…(More)”.

Spirals of Delusion: How AI Distorts Decision-Making and Makes Dictators More Dangerous


Essay by Henry Farrell, Abraham Newman, and Jeremy Wallace: “In policy circles, discussions about artificial intelligence invariably pit China against the United States in a race for technological supremacy. If the key resource is data, then China, with its billion-plus citizens and lax protections against state surveillance, seems destined to win. Kai-Fu Lee, a famous computer scientist, has claimed that data is the new oil, and China the new OPEC. If superior technology is what provides the edge, however, then the United States, with its world class university system and talented workforce, still has a chance to come out ahead. For either country, pundits assume that superiority in AI will lead naturally to broader economic and military superiority.

But thinking about AI in terms of a race for dominance misses the more fundamental ways in which AI is transforming global politics. AI will not transform the rivalry between powers so much as it will transform the rivals themselves. The United States is a democracy, whereas China is an authoritarian regime, and machine learning challenges each political system in its own way. The challenges to democracies such as the United States are all too visible. Machine learning may increase polarization—reengineering the online world to promote political division. It will certainly increase disinformation in the future, generating convincing fake speech at scale. The challenges to autocracies are more subtle but possibly more corrosive. Just as machine learning reflects and reinforces the divisions of democracy, it may confound autocracies, creating a false appearance of consensus and concealing underlying societal fissures until it is too late.

Early pioneers of AI, including the political scientist Herbert Simon, realized that AI technology has more in common with markets, bureaucracies, and political institutions than with simple engineering applications. Another pioneer of artificial intelligence, Norbert Wiener, described AI as a “cybernetic” system—one that can respond and adapt to feedback. Neither Simon nor Wiener anticipated how machine learning would dominate AI, but its evolution fits with their way of thinking. Facebook and Google use machine learning as the analytic engine of a self-correcting system, which continually updates its understanding of the data depending on whether its predictions succeed or fail. It is this loop between statistical analysis and feedback from the environment that has made machine learning such a formidable force…(More)”

Belfast to launch ‘Citizen Office of Digital Innovation’


Article by Sarah Wray: The City of Belfast in Northern Ireland has launched a tender to develop and pilot a Citizen Office of Digital Innovation (CODI) – a capacity-building programme to boost resident engagement around data and technology.

The council says the pilot will support a ‘digital citizenship skillset’, enabling citizens to better understand and shape how technology is used in Belfast. It could also lead to the creation of tools that can be used and adapted by other cities under a creative commons licence.

The tender is seeking creative and interactive methods to explore topics such as co-design, citizen science, the Internet of Things, artificial intelligence and data science, and privacy. It cites examples of citizen-centric programmes elsewhere including Dublin’s Academy of the Near Future and the DTPR standard for visual icons to explain sensors and cameras that are deployed in public spaces…(More)”