Michael Weiner in the Journal The Patient – Patient-Centered Outcomes Research: “Crowdsourcing (CS) is the outsourcing of a problem or task to a crowd. Although patient-centered care (PCC) may aim to be tailored to an individual’s needs, the uses of CS for generating ideas, identifying values, solving problems, facilitating research, and educating an audience represent powerful roles that can shape both allocation of shared resources and delivery of personalized care and treatment. CS can often be conducted quickly and at relatively low cost. Pitfalls include bias, risks of research ethics, inadequate quality of data, inadequate metrics, and observer-expectancy effect. Health professionals and consumers in the US should increase their attention to CS for the benefit of PCC. Patients’ participation in CS to shape health policy and decisions is one way to pursue PCC itself and may help to improve clinical outcomes through a better understanding of patients’ perspectives. CS should especially be used to traverse the quality-cost curve, or decrease costs while preserving or improving quality of care.”
Potholes and Big Data: Crowdsourcing Our Way to Better Government
Phil Simon in Wired: “Big Data is transforming many industries and functions within organizations with relatively limited budgets.
Consider Thomas M. Menino, up until recently Boston’s longest-serving mayor. At some point in the past few years, Menino realized that it was no longer 1950. Perhaps he was hobnobbing with some techies from MIT at dinner one night. Whatever his motivation, he decided that there just had to be a better, more cost-effective way to maintain and fix the city’s roads. Maybe smartphones could help the city take a more proactive approach to road maintenance.
To that end, in July 2012, the Mayor’s Office of New Urban Mechanics launched a new project called Street Bump, an app that allows drivers to automatically report the road hazards to the city as soon as they hear that unfortunate “thud,” with their smartphones doing all the work.
The app’s developers say their work has already sparked interest from other cities in the U.S., Europe, Africa and elsewhere that are imagining other ways to harness the technology.
Before they even start their trip, drivers using Street Bump fire up the app, then set their smartphones either on the dashboard or in a cup holder. The app takes care of the rest, using the phone’s accelerometer — a motion detector — to sense when a bump is hit. GPS records the location, and the phone transmits it to an AWS remote server.
But that’s not the end of the story. It turned out that the first version of the app reported far too many false positives (i.e., phantom potholes). This finding no doubt gave ammunition to the many naysayers who believe that technology will never be able to do what people can and that things are just fine as they are, thank you. Street Bump 1.0 “collected lots of data but couldn’t differentiate between potholes and other bumps.” After all, your smartphone or cell phone isn’t inert; it moves in the car naturally because the car is moving. And what about the scores of people whose phones “move” because they check their messages at a stoplight?
To their credit, Menino and his motley crew weren’t entirely discouraged by this initial setback. In their gut, they knew that they were on to something. The idea and potential of the Street Bump app were worth pursuing and refining, even if the first version was a bit lacking. Plus, they have plenty of examples from which to learn. It’s not like the iPad, iPod, and iPhone haven’t evolved considerably over time.
Enter InnoCentive, a Massachusetts-based firm specializing in open innovation and crowdsourcing. The City of Boston contracted InnoCentive to improve Street Bump and reduce the amount of tail chasing. The company accepted the challenge and essentially turned it into a contest, a process sometimes called gamification. InnoCentive offered a network of 400,000 experts a share of $25,000 in prize money donated by Liberty Mutual.
Almost immediately, the ideas to improve Street Bump poured in from unexpected places. This crowd had wisdom. Ultimately, the best suggestions came from:
- A group of hackers in Somerville, Massachusetts, that promotes community education and research
- The head of the mathematics department at Grand Valley State University in Allendale, MI.
- An anonymous software engineer
…Crowdsourcing roadside maintenance isn’t just cool. Increasingly, projects like Street Bump are resulting in substantial savings — and better government.”
Crowdsourced transit app shows what time the bus will really come
Springwise: “The problem with most transport apps is that they rely on fixed data from transport company schedules and don’t truly reflect exactly what’s going on with the city’s trains and buses at any given moment. Operating like a Waze for public transport, Israel’s Ototo app crowdsources real-time information from passengers to give users the best suggestions for their commute.
The app relies on a community of ‘Riders’, who allow anonymous location data to be sent from their smartphone whenever they’re using public transport. By collating this data together, Ototo offers more realistic information about bus and train routes. While a bus may be due in five minutes, a Rider currently on that bus might be located more than five minutes away, indicating that the bus isn’t on time. Ototo can then suggest a quicker route for users. According to Fast Company, the service currently has a 12,000-strong global Riders community that powers its travel recommendations. On top of this, the app is designed in an easy-to-use infographic format that quickly and efficiently tells users where they need to be going and how long it will take. The app is free to download from the App Store, and the video below offers a demonstration:
Ototo faces competition from similar services such as New York City’s Moovit, which also details how crowded buses are.”
Building a More Open Government
Corinna Zarek at the White House: “It’s Sunshine Week again—a chance to celebrate transparency and participation in government and freedom of information. Every year in mid-March, we take stock of our progress and where we are headed to make our government more open for the benefit of citizens.
In December, 2013, the Administration announced 23 ambitious commitments to further open up government over the next two years in U.S. Government’s second Open Government National Action Plan. Those commitments are now all underway or in development, including:
· Launching an improved Data.gov: The updated Data.gov debuted in January, 2014, and continues to grow with thousands of updated or new government data sets being proactively made available to the public.
· Increasing public collaboration: Through crowdsourcing, citizen science, and other methods, Federal agencies continue to expand the ways they collaborate with the public. For example, the National Aeronautics and Space Administration, for instance, recently launched its third Asteroid Grand Challenge, a broad call to action, seeking the best and brightest ideas from non-traditional partners to enhance and accelerate the work NASA is already doing for planetary defense.
· Improving We the People: The online petition platform We the People gives the public a direct way to participate in their government and is currently incorporating improvements to make it easier for the public to submit petitions and signatures.”
The Open Data/Environmental Justice Connection
Jeffrey Warren for Wilson’s Commons Lab: “… Open data initiatives seem to assume that all data is born in the hallowed halls of government, industry and academia, and that open data is primarily about convincing such institutions to share it to the public.
It is laudable when institutions with important datasets — such as campaign finance, pollution or scientific data — see the benefit of opening it to the public. But why do we assume unilateral control over data production?
The revolution in user-generated content shows the public has a great deal to contribute – and to gain—from the open data movement. Likewise, citizen science projects that solicit submissions or “task completion” from the public rarely invite higher-level participation in research –let alone true collaboration.
This has to change. Data isn’t just something you’re given if you ask nicely, or a kind of community service we perform to support experts. Increasingly, new technologies make it possible for local groups to generate and control data themselves — especially in environmental health. Communities on the front line of pollution’s effects have the best opportunities to monitor it and the most to gain by taking an active role in the research process.
DIY Data
Luckily, an emerging alliance between the maker/Do-It-Yourself (DIY) movement and watchdog groups is starting to challenge the conventional model.
The Smart Citizen project, the Air Quality Egg and a variety of projects in the Public Lab network are recasting members of the general public as actors in the framing of new research questions and designers of a new generation of data tools.
The Riffle, a <$100 water quality sensor built inside of hardware-store pipe, can be left in a creek near an industrial site to collect data around the clock for weeks or months. In the near future, when pollution happens – like the ash spill in North Carolina or the chemical spill in West Virginia – the public will be alerted and able to track its effects without depending on expensive equipment or distant labs.
This emerging movement is recasting environmental issues not as intractably large problems, but up-close-and-personal health issues — just what environmental justice (EJ) groups have been arguing for years. The difference is that these new initiatives hybridize such EJ community organizers and the technology hackers of the open hardware movement. Just as the Homebrew Computer Club’s tinkering with early prototypes led to the personal computer, a new generation of tinkerers sees that their affordable, accessible techniques can make an immediate difference in investigating lead in their backyard soil, nitrates in their tap water and particulate pollution in the air they breathe.
These practitioners see that environmental data collection is not a distant problem in a developing country, but an issue that anyone in a major metropolitan area, or an area affected by oil and gas extraction, faces on a daily basis. Though underserved communities are often disproportionally affected, these threats often transcend socioeconomic boundaries…”
The Next Frontier in Crowdsourcing: Your Smartphone
Rachel Metz in MIT TechnologyReview: “Rather than swiping the screen or entering a passcode to unlock the smartphone in my hand, I have to tell it how energetic the people around me are feeling by tapping one of four icons. I’m the only one here, and the one that best fits my actual energy level, to be honest, is a figure lying down and emitting a trail of z’s.
I’m trying out an Android app called Twitch. Created by Stanford researchers, it asks you to complete a few simple tasks—contributing information, as with the reported energy levels, or performing simple tasks like ranking images or structuring data extracted from Wikipedia pages—each time you unlock your phone. The information collected by apps like Twitch could be useful to academics, market researchers, or local businesses. Such software could also provide a low-cost way to perform useful work that can easily be broken up into pieces and fed to millions of devices.
Twitch is one of several projects exploring crowdsourcing via the lock screen. Plenty of people already contribute freely to crowdsourcing websites like Wikipedia and Quora or paid services like Amazon’s Mechanical Turk, and the sustained popularity of traffic app Waze shows that people are willing to contribute to a common cause from their handsets if it provides a timely, helpful result.
There are certainly enough smartphones with lock screens ready to be harnessed. According to data from market researcher comScore, 160 million people in the U.S.—or 67 percent of cell phone users—have smartphones, and nearly 52 percent of these run Google’s Android OS, which allows apps like Twitch to replace the standard lock screen….”
The Challenges of Challenge.Gov: Adopting Private Sector Business Innovations in the Federal Government
I Mergel, SI Bretschneider, C Louis, J Smith at the HICSS ’14 Proceedings of the 2014 47th Hawaii International Conference on System Sciences: “As part of the Open Government Initiative in the U.S. federal government, the White House has introduced a new policy instrument called “Challenges and Prizes”, implemented as Challenge.gov that allows federal departments to run Open Innovation (OI) contests. This initiative was motivated by similar OI initiatives in the private sector and to enhance innovativeness and performance among federal agencies. Here we first define the underlying theoretical concepts of OI, crowd sourcing and contests and apply them to the existing theory of public ness and the creation of public goods. We then analyze over 200 crowd sourcing contests on CHALLENGE.GOV and conclude that federal departments and agencies use this policy instrument for four different purpose: awareness, service, knowledge and technical solutions. We conclude that Challenge.gov is currently used as an innovative format to inform and educate the public about public management problems and less frequently to solicit complex technological solutions from problem solvers.”
Putting Crowdsourcing on the Map
MIT Technology Review: “Even in San Francisco, where Google’s roving Street View cars have mapped nearly every paved surface, there are still places that have remained untouched, such as the flights of stairs that serve as pathways between streets in some of the city’s hilliest neighborhoods.
It’s these places that a startup called Mapillary is focusing on. Cofounders Jan Erik Solem and Johan Gyllenspetz are attempting to build an open, crowdsourced, photographic map that lets smartphone users log all sorts of places, creating a richer view of the world than what is offered by Street View and other street-level mapping services. If contributors provide images often, that view could be more representative of how things look right now.
Google itself is no stranger to the benefits of crowdsourced map content: it paid $966 million last year for traffic and navigation app Waze, whose users contribute data. Google also lets people augment Street View content with their own images. But Solem and Gyllenspetz think there’s still plenty of room for Mapillary, which they say can be used for everything from tracking a nature hike to offering more up-to-date images to house hunters and Airbnb users.
Solem and Gyllenspetz have only been working on the project for four months; they released an iPhone app in November, and an Android app in January. So far, there are just a few hundred users who have shared about 100,000 photos on the service. While it’s free for anyone to use, the startup plans to eventually make money by licensing the data its users generate to companies.
With the app, a user can choose to collect images by walking, biking, or driving. Once you press a virtual shutter button within the app, it takes a photo every two seconds, until you press the button again. You can then upload the images to Mapillary’s service via Wi-Fi, where each photo’s location is noted through its GPS tag. Computer-vision software compares each photo with others that are within a radius of about 100 meters, searching for matching image features so it can find the geometric relationship between the photos. It then places those images properly on the map, and stitches them all together. When new images come in of an area that has already been mapped, Mapillary will add them to its database, too.
It can take less than 30 seconds for the images to show up on the Web-based map, but several minutes for the images to be fully processed. As with Google’s Street View photos, image-recognition software blurs out faces and license plate numbers.
Users can edit Mapillary’s map by moving around the icons that correspond to images—to fix a misplaced image, for instance. Eventually, users will also be able to add comments and tags.
So far, Mapillary’s map is quite sparse. But the few hundred users trying out Mapillary include some map providers in Europe, and the 100,000 or so images to the service ranging from a bike path on Venice Beach in California to a snow-covered ski slope in Sweden.
Street-level images can be viewed on the Web or through Mapillary’s smartphone apps (though the apps just pull up the Web page within the app). Blue lines and colored tags indicate where users have added photos to the map; you can zoom in to see them at the street level.
Navigating through photos is still quite rudimentary; you can tap or click to move from one image to the next with onscreen arrows, depending on the direction you want to explore.
Beyond technical and design challenges, the biggest issue Mapillary faces is convincing a large enough number of users to build up its store of images so that others will start using it and contributing as well, and then ensuring that these users keep coming back.”
Coordinating the Commons: Diversity & Dynamics in Open Collaborations
Dissertation by Jonathan T. Morgan: “The success of Wikipedia demonstrates that open collaboration can be an effective model for organizing geographically-distributed volunteers to perform complex, sustained work at a massive scale. However, Wikipedia’s history also demonstrates some of the challenges that large, long-term open collaborations face: the core community of Wikipedia editors—the volunteers who contribute most of the encyclopedia’s content and ensure that articles are correct and consistent — has been gradually shrinking since 2007, in part because Wikipedia’s social climate has become increasingly inhospitable for newcomers, female editors, and editors from other underrepresented demographics. Previous research studies of change over time within other work contexts, such as corporations, suggests that incremental processes such as bureaucratic formalization can make organizations more rule-bound and less adaptable — in effect, less open— as they grow and age. There has been little research on how open collaborations like Wikipedia change over time, and on the impact of those changes on the social dynamics of the collaborating community and the way community members prioritize and perform work. Learning from Wikipedia’s successes and failures can help researchers and designers understand how to support open collaborations in other domains — such as Free/Libre Open Source Software, Citizen Science, and Citizen Journalism.
In this dissertation, I examine the role of openness, and the potential antecedents and consequences of formalization, within Wikipedia through an analysis of three distinct but interrelated social structures: community-created rules within the Wikipedia policy environment, coordination work and group dynamics within self-organized open teams called WikiProjects, and the socialization mechanisms that Wikipedia editors use to teach new community members how to participate.To inquire further, I have designed a new editor peer support space, the Wikipedia Teahouse, based on the findings from my empirical studies. The Teahouse is a volunteer-driven project that provides a welcoming and engaging environment in which new editors can learn how to be productive members of the Wikipedia community, with the goal of increasing the number and diversity of newcomers who go on to make substantial contributions to Wikipedia …”
True Collective Intelligence? A Sketch of a Possible New Field
Paper by Geoff Mulgan in Philosophy & Technology :” Collective intelligence is much talked about but remains very underdeveloped as a field. There are small pockets in computer science and psychology and fragments in other fields, ranging from economics to biology. New networks and social media also provide a rich source of emerging evidence. However, there are surprisingly few useable theories, and many of the fashionable claims have not stood up to scrutiny. The field of analysis should be how intelligence is organised at large scale—in organisations, cities, nations and networks. The paper sets out some of the potential theoretical building blocks, suggests an experimental and research agenda, shows how it could be analysed within an organisation or business sector and points to the possible intellectual barriers to progress.”