As Jakarta floods again, humanitarian chatbots on social media support community-led disaster response


Blog by Petabencana: “On February 20th, #banjir and #JakartaBanjir were the highest trending topics on Twitter Indonesia, as the capital city was inundated for the third major time this year, following particularly heavy rainfall from Friday night (19/2/2021) to Saturday morning (20/02/2021). As Jakarta residents turned to social media to share updates about the flood, they were greeted by “Disaster Bot” – a novel AI-assisted chatbot that monitors social media for posts about disasters and automatically invites users to submit more detailed disaster reports. These crowd-sourced reports are used to map disasters in real-time, on a free and open source website, PetaBencana.id.

As flooding blocked major thoroughfares and toll roads, disrupted commuter lines, and cut off electricity to over 60,000 homes, residents continued to share updates about the flood situation in order to stay alert and make timely decisions about safety and response. Hundreds of residents submitted flood reports to PetaBencana.id, alerting each other about water levels, broken infrastructures and road accessibility. The Jakarta Emergency Management Agency also updated the map with official information about flood affected  areas, and monitored the map to respond to resident needs. PetaBencana.id experienced a 2000% in activity in under 12 hours as residents actively checked the map to understand the flooding situation, avoid flooded areas, and make decisions about safety and response. 

Residents share updates about flood-affected road access through the open source information sharing platform, PetaBencana.id. Thousands of residents used the map to navigate safely as heavy rainfall inundated the city for the third major time this year.

As flooding incidents continue to occur with increasing intensity across the country, community-led information sharing is once again proving its significance in supporting response and planning at multiple scales. …(More)”.

These crowdsourced maps will show exactly where surveillance cameras are watching


Mark Sullivan at FastCompany: “Amnesty International is producing a map of all the places in New York City where surveillance cameras are scanning residents’ faces.

The project will enlist volunteers to use their smartphones to identify, photograph, and locate government-owned surveillance cameras capable of shooting video that could be matched against people’s faces in a database through AI-powered facial recognition.

The map that will eventually result is meant to give New Yorkers the power of information against an invasive technology the usage of which and purpose is often not fully disclosed to the public. It’s also meant to put pressure on the New York City Council to write and pass a law restricting or banning it. Other U.S. cities, such as Boston, Portland, and San Francisco, have already passed such laws.

Facial recognition technology can be developed by scraping millions of images from social media profiles and driver’s licenses without people’s consent, Amnesty says. Software from companies like Clearview AI can then use computer vision algorithms to match those images against facial images captured by closed-circuit television (CCTV) or other video surveillance cameras and stored in a database.

Starting in May, volunteers will be able to use a software tool to identify all the facial recognition cameras within their view—like at an intersection where numerous cameras can often be found. The tool, which runs on a phone’s browser, lets users place a square around any cameras they see. The software integrates Google Street View and Google Earth to help volunteers label and attach geolocation data to the cameras they spot.

The map is part of a larger campaign called “Ban the Scan” that’s meant to educate people around the world on the civil rights dangers of facial recognition. Research has shown that facial recognition systems aren’t as accurate when it comes to analyzing dark-skinned faces, putting Black people at risk of being misidentified. Even when accurate, the technology exacerbates systemic racism because it is disproportionately used to identify people of color, who are already subject to discrimination by law enforcement officials. The campaign is sponsored by Amnesty in partnership with a number of other tech advocacy, privacy, and civil liberties groups.

In the initial phase of the project, which was announced last Thursday, Amnesty and its partners launched a website that New Yorkers can use to generate public comments on the New York Police Department’s (NYPD’s) use of facial recognition….(More)”.

Twitter’s misinformation problem is much bigger than Trump. The crowd may help solve it.


Elizabeth Dwoskin at the Washington Post: “A pilot program called Birdwatch lets selected users write corrections and fact checks on potentially misleading tweets…

The presidential election is over, but the fight against misinformation continues.

The latest volley in that effort comes from Twitter, which on MondayannouncedBirdwatch, a pilot project that uses crowdsourcing techniques to combat falsehoods and misleading statements on its service.

The pilot, which is open to only about 1,000 select users who can apply to be contributors, will allow people to write notes with corrections and accurate information directly into misleading tweets — a method that has the potential to get quality information to people more quickly than traditional fact-checking. Fact checks that are rated by other contributors as high quality may get bumped up or rewarded with greater visibility.

Birdwatch represents Twitter’s most experimental response to one of the biggest lessons that social media companies drew from the historic events of 2020: that their existing efforts to combat misinformation — including labeling, fact-checking and sometimes removing content — were not enough to prevent falsehoods about a stolen election or the coronavirus from reaching and influencing broad swaths of the population. Researchers who studied enforcement actions by social media companies last year found that fact checks and labels are usually implemented too late, after a post or a tweet has gone viral.

The Birdwatch project — which for the duration of the pilot will function as a separate website — is novel in that it attempts to build new mechanisms into Twitter’s product that foreground fact-checking by its community of 187 million daily users worldwide. Rather than having to comb through replies to tweets to sift through what’s true or false — or having Twitter employees append to a tweet a label providing additional context — users will be able to click on a separate notes folder attached to a tweet where they can see the consensus-driven responses from the community. Twitter will have a team reviewing winning responses to prevent manipulation, though a major question is whether any part of the process will be automated and therefore more easily gamed….(More)”

The Hidden Cost of Using Amazon Mechanical Turk for Research


Paper by Antonios Saravanos: “This work shares unexpected findings obtained from the use of the Amazon Mechanical Turk platform as a source of participants for the study of technology adoption. Expressly, of the 564 participants from the United States, 126 (22.34%) failed at least one of three forms of attention check (logic, honesty, and time). We also examined whether characteristics such as gender, age, education, and income affected participant attention. Amongst all characteristics assessed, only prior experience with the technology being studied was found to be related to attentiveness. We conclude this work by reaffirming the need for multiple forms of attention checks to gauge participant attention. Furthermore, we propose that researchers adjust their budgets accordingly to account for the possibility of having to discard responses from participants determined not to be displaying adequate attention….(More)”.

Why crowdsourcing fails


Paper by Linus Dahlander and Henning Piezunka: ” Crowdsourcing—asking an undefned group of external contributors to work on tasks—allows organizations to tap into the expertise of people around the world. Crowdsourcing is known to increase innovation and loyalty to brands, but many organizations struggle to leverage its potential, as our research shows. Most often this is because organizations fail to properly plan for all the diferent stages of crowd engagement. In this paper, we use several examples to explain these challenges and ofer advice for how organizations can overcome them….(More)”.

Crowdsourcing Crime Control


Paper by Wayne A. Logan: “Crowdsourcing, which leverages the collective expertise and resources of (mainly online) communities to achieve specified objectives, today figures prominently in a broad array of realms, including business, human rights, and medical and scientific research. It also now plays a significant role in governmental crime control efforts. Web and forensic–genetic sleuths, armchair detectives, and the like are collecting and analyzing evidence and identifying criminal suspects, at the behest of and with varying degrees of assistance from police officials.

Unfortunately, as with so many other aspects of modern society, current criminal procedure doctrine is ill-equipped to address this development. In particular, for decades it has been accepted that the Fourth Amendment only limits searches and seizures undertaken by public law enforcement, not private actors. Crowdsourcing, however, presents considerable taxonomic difficulty for existing doctrine, making the already often permeable line between public and private behavior considerably more so. Moreover, although crowdsourcing promises considerable benefit as an investigative force multiplier for police, it poses risks, including misidentification of suspects, violation of privacy, a diminution of governmental transparency and democratic accountability, and the fostering of a mutual social suspicion that is inimical to civil society.

Despite its importance, government use of crowdsourcing to achieve crime control goals has not yet been examined by legal scholars. Like the internet on which it predominantly relies, crowdsourcing is not going away; if anything, it will proliferate in coming years. The challenge lies in harnessing its potential, while protecting against the significant harms that will accrue should it go unregulated. This Essay describes the phenomenon and provides a framework for its regulation, in the hope of ensuring that the wisdom of the crowd does not become the tyranny of the crowd….(More)”.

Wikipedia @ 20


Stories of an Incomplete Revolution edited by Joseph Reagle and Jackie Koerner (Open Access): “We have been looking things up in Wikipedia for twenty years. What began almost by accident—a wiki attached to a nascent online encyclopedia—has become the world’s most popular reference work. Regarded at first as the scholarly equivalent of a Big Mac, Wikipedia is now known for its reliable sourcing and as a bastion of (mostly) reasoned interaction. How has Wikipedia, built on a model of radical collaboration, remained true to its original mission of “free access to the sum of all human knowledge” when other tech phenomena have devolved into advertising platforms? In this book, scholars, activists, and volunteers reflect on Wikipedia’s first twenty years, revealing connections across disciplines and borders, languages and data, the professional and personal.

The contributors consider Wikipedia’s history, the richness of the connections that underpin it, and its founding vision. Their essays look at, among other things, the shift from bewilderment to respect in press coverage of Wikipedia; Wikipedia as “the most important laboratory for social scientific and computing research in history”; and the acknowledgment that “free access” includes not just access to the material but freedom to contribute—that the summation of all human knowledge is biased by who documents it….(More)”

Silicon Valley’s next goal is 3D maps of the world — made by us


Tim Bradshaw at the Financial Times: “When technology transformed the camera, the shift from film to digital sensors was just the beginning. As standalone cameras were absorbed into our phones, they gained software smarts, enabling them not only to capture light but also to understand the contents of a photo and even recognise people in it.

A similar transformation is now starting to happen to maps — and it too is powered by those advances in camera technology. In the next 20 years, our collective understanding of a “map” will be unrecognisable from the familiar grid of roads and places that has endured even as the A-Z street atlas has been supplanted by Google Maps.

Before long, countless objects and places will be captured and recreated in 3D digital models that we can view through our phones or even, at some stage, on headsets. This digital world might be populated by our avatars, turned into a playing field for new kinds of games or used to discover routes, buildings and services around us. 

Nobody seems sure yet what the killer app for this “digital twin” of Planet Earth might be, but that hasn’t stopped Silicon Valley from racing to build it anyway. Facebook, Apple, Google and Microsoft, as well as the developers of Snapchat and Pokémon Go, are all hoping to bring this “mirrorworld” to life, as a precursor to the augmented-reality (AR) glasses that many in tech see as the next big thing.

To place virtual objects in our world, our devices need to know the textures and contours of their surroundings, which GPS cannot see. But instead of sending out cars with protruding cameras to scan the world, as Google did to build Street View over the past decade and a half, these maps will be plotted by hundreds of millions of users like you and me. The question is whether we even realise that we have been dragooned into Silicon Valley’s army of cartographers. They cannot do it without us.

This month, Google said it would ask Maps users to upload photos to Street View using their smartphones for the first time. Only handsets running its AR software can contribute.  As Michael Abrash, chief scientist at Facebook’s Oculus headset unit, recently told Fast Company magazine: “Crowdsourcing has to be the primary way that this works. There is no other way to scale.”…(More)”.

Crowdfunding during COVID-19: An international comparison of online fundraising


Paper by Greg Elmer, Sabrina Ward-Kimola and Anthony Glyn Burton: “This article performs a digital methods analysis on a sample of online crowdfunding campaigns seeking financial support for COVID related financial challenges. Building upon the crowdfunding literature this paper performs an international comparison of the goals of COVID related campaigns during the early spread of the pandemic. The paper seeks to determine the extent to which crowdfunding campaigns reflect current failures of governments to supress the COVID pandemic and support the financial challenges of families, communities and small businesses….(More)”.

CrowdHeritage: Improving the quality of Cultural Heritage through crowdsourcing methods


Paper by Maria Ralli et al: “The lack of granular and rich descriptive metadata highly affects the discoverability and usability of the digital content stored in museums, libraries and archives, aggregated and served through Europeana, thus often frustrating the user experience offered by these institutions’ portals. In this context, metadata enrichment services through automated analysis and feature extraction along with crowdsourcing annotation services can offer a great opportunity for improving the metadata quality of digital cultural content in a scalable way, while at the same time engaging different user communities and raising awareness about cultural heritage assets. Such an effort is Crowdheritage, an open crowdsourcing platform that aims to employ machine and human intelligence in order to improve the digital cultural content metadata quality….(More)”.