crowdsourcing

The move toward 'crowdsourcing' public safety

Curated on October 11, 2013August 3, 2018 by Stefaan Verhulst

PhysOrg: “Earlier this year, Martin Dias, assistant professor in the D’Amore-McKim School of Business, presented research for the National Law Enforcement Telecommunications System in which he examined Nlets’ network and how its governance and technology helped enable inter-agency information sharing. This work builds on his research aimed at understanding design principles for this public safety “social networks” and other collaborative networks. We asked Dias to discuss how information sharing around public safety has evolved in recent years and the benefits and challenges of what he describes as “crowdsourcing public safety.” …

What is “crowdsourcing public safety” and why are public safety agencies moving toward this trend?
Crowdsourcing—the term coined by our own assistant professor of journalism Jeff Howe—involves taking a task or job traditionally performed by a distinct agent, or employee, and having that activity be executed by an “undefined, generally large group of people in an open call.” Crowdsourcing public safety involves engaging and enabling private citizens to assist public safety professionals in addressing natural disasters, terror attacks, organized crime incidents, and large-scale industrial accidents.
Public safety agencies have long recognized the need for citizen involvement. Tip lines and missing persons bulletins have been used to engage citizens for years, but with advances in mobile applications and big data analytics, the ability of public safety agencies to receive, process, and make use of high volume, tips, and leads makes crowdsourcing searches and investigations more feasible. You saw this in the FBI Boston Marathon Bombing web-based Tip Line. You see it in the “See Something Say Something” initiatives throughout the country. You see it in AMBER alerts or even remote search and rescue efforts. You even see it in more routine instances like Washington State’s HERO program to reduce traffic violations.
Have these efforts been successful, and what challenges remain?
There are a number of issues to overcome with regard to crowdsourcing public safety—such as maintaining privacy rights, ensuring data quality, and improving trust between citizens and law enforcement officers. Controversies over the National Security Agency’s surveillance program and neighborhood watch programs – particularly the shooting death of teenager Trayvon Martin by neighborhood watch captain George Zimmerman, reflect some of these challenges. It is not clear yet from research the precise set of success criteria, but those efforts that appear successful at the moment have tended to be centered around a particular crisis incident—such as a specific attack or missing person. But as more crowdsourcing public safety mobile applications are developed, adoption and use is likely to increase. One trend to watch is whether national public safety programs are able to tap into the existing social networks of community-based responders like American Red Cross volunteers, Community Emergency Response Teams, and United Way mentors.
The move toward crowdsourcing public safety is part of an overall trend toward improving community resilience, which refers to a system’s ability to bounce back after a crisis or disturbance. Stephen Flynn and his colleagues at Northeastern’s George J. Kostas Research Institute for Homeland Security are playing a key role in driving a national conversation in this area. Community resilience is inherently multi-disciplinary, so you see research being done regarding transportation infrastructure, social media use after a crisis event, and designing sustainable urban environments. Northeastern is a place where use-inspired research is addressing real-world problems. It will take a village to improve community resilience capabilities, and our institution is a vital part of thought leadership for that village.”

Towards an information systems perspective and research agenda on crowdsourcing for innovation

Curated on October 8, 2013August 3, 2018 by Stefaan Verhulst

New paper by A Majchrzak and A Malhotra in The Journal of Strategic Information Systems: “Recent years have seen an increasing emphasis on open innovation by firms to keep pace with the growing intricacy of products and services and the ever changing needs of the markets. Much has been written about open innovation and its manifestation in the form of crowdsourcing. Unfortunately, most management research has taken the information system (IS) as a given. In this essay we contend that IS is not just an enabler but rather can be a shaper that optimizes open innovation in general and crowdsourcing in particular. This essay is intended to frame crowdsourcing for innovation in a manner that makes more apparent the issues that require research from an IS perspective. In doing so, we delineate the contributions that the IS field can make to the field of crowdsourcing.

Reviews participation architectures supporting current crowdsourcing, finding them inadequate for innovation development by the crowd.
Identifies 3 tensions for explaining why a participation architecture for crowdsourced innovation is difficult.
Identifies affordances for the participation architectures that may help to manage the tension.
Uses the tensions and possible affordances to identify research questions for IS scholars.”

Commons at the Intersection of Peer Production, Citizen Science, and Big Data: Galaxy Zoo

Curated on October 5, 2013October 10, 2018 by Stefaan Verhulst

New paper by Michael J. Madison: “The knowledge commons research framework is applied to a case of commons governance grounded in research in modern astronomy. The case, Galaxy Zoo, is a leading example of at least three different contemporary phenomena. In the first place Galaxy Zoo is a global citizen science project, in which volunteer non-scientists have been recruited to participate in large-scale data analysis via the Internet. In the second place Galaxy Zoo is a highly successful example of peer production, some times known colloquially as crowdsourcing, by which data are gathered, supplied, and/or analyzed by very large numbers of anonymous and pseudonymous contributors to an enterprise that is centrally coordinated or managed. In the third place Galaxy Zoo is a highly visible example of data-intensive science, sometimes referred to as e-science or Big Data science, by which scientific researchers develop methods to grapple with the massive volumes of digital data now available to them via modern sensing and imaging technologies. This chapter synthesizes these three perspectives on Galaxy Zoo via the knowledge commons framework.”

New crowdsourcing platform links tech-skilled volunteers with charities

Curated on October 4, 2013October 9, 2018 by Stefaan Verhulst

Charity Digital News: “The Atlassian Foundation today previewed its innovative crowdsourcing platform, MakeaDiff.org, which will allow nonprofits to coordinate with technically-skilled volunteers who want to help convert ideas into successful projects…
Once vetted, nonprofits will be able to list their volunteer jobs on the site. Skilled volunteers such as developers, designers, business analysts and project managers will then be able to go online and quickly search the site for opportunities relevant and convenient to them.
Atlassian Foundation manager, Melissa Beaumont Lee, said: “We started hearing from nonprofits that what they valued even more than donations was access to Atlassian’s technology expertise. Similarly, we had lots of employees who were keen to volunteer, but didn’t know how to get involved; coordinating volunteers for all these amazing projects was just not scalable. Thus, MakeaDiff.org was born to benefit both nonprofits and volunteers. We wanted to reduce the friction in coordinating efforts so more time can be spent doing really meaningful work.”

Best Practices for Government Crowdsourcing Programs

Curated on October 4, 2013May 29, 2019 by Stefaan Verhulst

Anton Root: “Crowdsourcing helps communities connect and organize, so it makes sense that governments are increasingly making use of crowd-powered technologies and processes.
Just recently, for instance, we wrote about the Malaysian government’s initiative to crowdsource the national budget. Closer to home, we’ve seen government agencies from U.S. AID to NASA make use of the crowd.
Daren Brabham, professor at the University of Southern California, recently published a report titled “Using Crowdsourcing In Government” that introduces readers to the basics of crowdsourcing, highlights effective use cases, and establishes best practices when it comes to governments opening up to the crowd. Below, we take a look at a few of the suggestions Brabham makes to those considering crowdsourcing.
Brabham splits up his ten best practices into three phases: planning, implementation, and post-implementation. The first suggestion in the planning phase he makes may be the most critical of all: “Clearly define the problem and solution parameters.” If the community isn’t absolutely clear on what the problem is, the ideas and solutions that users submit will be equally vague and largely useless.
This applies not only to government agencies, but also to SMEs and large enterprises making use of crowdsourcing. At Massolution NYC 2013, for instance, we heard again and again the importance of meticulously defining a problem. And open innovation platform InnoCentive’s CEO Andy Zynga stressed the big role his company plays in helping organizations do away with the “curse of knowledge.”
Brabham also has advice for projects in their implementation phase, the key bit being: “Launch a promotional plan and a plan to grow and sustain the community.” Simply put, crowdsourcing cannot work without a crowd, so it’s important to build up the community before launching a campaign. It does take some balance, however, as a community that’s too large by the time a campaign launches can turn off newcomers who “may not feel welcome or may be unsure how to become initiated into the group or taken seriously.”
Brabham’s key advice for the post-implementation phase is: “Assess the project from many angles.” The author suggests tracking website traffic patterns, asking users to volunteer information about themselves when registering, and doing original research through surveys and interviews. The results of follow-up research can help to better understand the responses submitted, and also make it easier to show the successes of the crowdsourcing campaign. This is especially important for organizations partaking in ongoing crowdsourcing efforts.”

Using Participatory Crowdsourcing in South Africa to Create a Safer Living Environment

Curated on September 28, 2013August 3, 2018 by Stefaan Verhulst

New Paper by Bhaveer Bhana, Stephen Flowerday, and Aharon Satt in the International Journal of Distributed Sensor Networks: “The increase in urbanisation is making the management of city resources a difficult task. Data collected through observations (utilising humans as sensors) of the city surroundings can be used to improve decision making in terms of managing these resources. However, the data collected must be of a certain quality in order to ensure that effective and efficient decisions are made. This study is focused on the improvement of emergency and non-emergency services (city resources) through the use of participatory crowdsourcing (humans as sensors) as a data collection method (collect public safety data), utilising voice technology in the form of an interactive voice response (IVR) system.
The study illustrates how participatory crowdsourcing (specifically humans as sensors) can be used as a Smart City initiative focusing on public safety by illustrating what is required to contribute to the Smart City, and developing a roadmap in the form of a model to assist decision making when selecting an optimal crowdsourcing initiative. Public safety data quality criteria were developed to assess and identify the problems affecting data quality.
This study is guided by design science methodology and applies three driving theories: the Data Information Knowledge Action Result (DIKAR) model, the characteristics of a Smart City, and a credible Data Quality Framework. Four critical success factors were developed to ensure high quality public safety data is collected through participatory crowdsourcing utilising voice technologies.”

New book: "Crowdsourcing"

Curated on September 22, 2013August 3, 2018 by Stefaan Verhulst

New book by Jean-Fabrice Lebraty, Katia Lobre-Lebraty on Crowdsourcing: “Crowdsourcing is a relatively recent phenomenon that only appeared in 2006, but it continues to grow and diversify (crowdfunding, crowdcontrol, etc.). This book aims to review this concept and show how it leads to the creation of value and new business opportunities.
Chapter 1 is based on four examples: the online-banking sector, an informative television channel, the postal sector and the higher education sector. It shows that in the current context, for a company facing challenges, the crowd remains an untapped resource. The next chapter presents crowdsourcing as a new form of externalization and offers definitions of crowdsourcing. In Chapter 3, the authors attempt to explain how a company can create value by means of a crowdsourcing operation. To do this, authors use a model linking types of value, types of crowd, and the means by which these crowds are accessed.
Chapter 4 examines in detail various forms that crowdsourcing may take, by presenting and discussing ten types of crowdsourcing operation. In Chapter 5, the authors imagine and explore the ways in which the dark side of crowdsourcing might be manifested and Chapter 6 offers some insight into the future of crowdsourcing.
Contents
1. A Turbulent and Paradoxical Environment.
2. Crowdsourcing: A New Form of Externalization.
3. Crowdsourcing and Value Creation.
4. Forms of Crowdsourcing.
5. The Dangers of Crowdsourcing.
6. The Future of Crowdsourcing.”

(Appropriate) Big Data for Climate Resilience?

Curated on September 13, 2013August 15, 2018 by Stefaan Verhulst

Amy Luers at the Stanford Social Innovation Review: “The answer to whether big data can help communities build resilience to climate change is yes—there are huge opportunities, but there are also risks.

Opportunities

Feedback: Strong negative feedback is core to resilience. A simple example is our body’s response to heat stress—sweating, which is a natural feedback to cool down our body. In social systems, feedbacks are also critical for maintaining functions under stress. For example, communication by affected communities after a hurricane provides feedback for how and where organizations and individuals can provide help. While this kind of feedback used to rely completely on traditional communication channels, now crowdsourcing and data mining projects, such as Ushahidi and Twitter Earthquake detector, enable faster and more-targeted relief.
Diversity: Big data is enhancing diversity in a number of ways. Consider public health systems. Health officials are increasingly relying on digital detection methods, such as Google Flu Trends or Flu Near You, to augment and diversify traditional disease surveillance.
Self-Organization: A central characteristic of resilient communities is the ability to self-organize. This characteristic must exist within a community (see the National Research Council Resilience Report), not something you can impose on it. However, social media and related data-mining tools (InfoAmazonia, Healthmap) can enhance situational awareness and facilitate collective action by helping people identify others with common interests, communicate with them, and coordinate efforts.

Risks

Eroding trust: Trust is well established as a core feature of community resilience. Yet the NSA PRISM escapade made it clear that big data projects are raising privacy concerns and possibly eroding trust. And it is not just an issue in government. For example, Target analyzes shopping patterns and can fairly accurately guess if someone in your family is pregnant (which is awkward if they know your daughter is pregnant before you do). When our trust in government, business, and communities weakens, it can decrease a society’s resilience to climate stress.
Mistaking correlation for causation: Data mining seeks meaning in patterns that are completely independent of theory (suggesting to some that theory is dead). This approach can lead to erroneous conclusions when correlation is mistakenly taken for causation. For example, one study demonstrated that data mining techniques could show a strong (however spurious) correlation between the changes in the S&P 500 stock index and butter production in Bangladesh. While interesting, a decision support system based on this correlation would likely prove misleading.
Failing to see the big picture: One of the biggest challenges with big data mining for building climate resilience is its overemphasis on the hyper-local and hyper-now. While this hyper-local, hyper-now information may be critical for business decisions, without a broader understanding of the longer-term and more-systemic dynamism of social and biophysical systems, big data provides no ability to understand future trends or anticipate vulnerabilities. We must not let our obsession with the here and now divert us from slower-changing variables such as declining groundwater, loss of biodiversity, and melting ice caps—all of which may silently define our future. A related challenge is the fact that big data mining tends to overlook the most vulnerable populations. We must not let the lure of the big data microscope on the “well-to-do” populations of the world make us blind to the less well of populations within cities and communities that have more limited access to smart phones and the Internet.”

Dump the Prizes

Curated on September 5, 2013August 3, 2018 by Stefaan Verhulst

Kevin Starr in the Stanford Social Innovation Review: “Contests, challenges, awards—they do more harm than good. Let’s get rid of them….Here’s why:
1. It wastes huge amounts of time.
The Knight Foundation recently released a thoughtful, well-publicized report on its experience running a dozen or so open contests. These are well-run contests, but the report states that there have been 25,000 entries overall, with only 400 winners. That means there have been 24,600 losers. Let’s say that, on average, entrants spent 10 hours working on their entries—that’s 246,000 hours wasted, or 120 people working full-time for a year. Other contests generate worse numbers. I’ve spoken with capable organization leaders who’ve spent 40-plus hours on entries for these things, and too often they find out later that the eligibility criteria were misleading anyway. They are the last people whose time we should waste. …
2. There is way too much emphasis on innovation and not nearly enough on implementation.
Ideas are easy; implementation is hard. Too many competitions are just about generating ideas and “innovation.” Novelty is fun, but there is already an immense limbo-land populated by successful pilots and proven innovations that have gone nowhere. I don’t want to fund anything that doesn’t have someone capable enough to execute on the idea and committed enough to make it work over the long haul. Great social entrepreneurs are people with high-impact ideas, the chops to execute on them, and the commitment to go the distance. They are rare, and they shouldn’t have to enter a contest to get what they need.
The current enthusiasm for crowdsourcing innovation reflects this fallacy that ideas are somehow in short supply. I’ve watched many capable professionals struggle to find implementation support for doable—even proven—real-world ideas, and it is galling to watch all the hoopla around well-intentioned ideas that are doomed to fail. Most crowdsourced ideas prove unworkable, but even if good ones emerge, there is no implementation fairy out there, no army of social entrepreneurs eager to execute on someone else’s idea. Much of what captures media attention and public awareness barely rises above the level of entertainment if judged by its potential to drive real impact.
3. It gets too much wrong and too little right.
The Hilton Humanitarian prize is a single winner-take-all award of $1.5 million to one lucky organization each year. With a huge prize like that, everyone feels compelled to apply (that is, get nominated), and I can’t tell you how much time I’ve wasted on fruitless recommendations. Very smart people from the foundation spend a lot of time investigating candidates—and I don’t understand why. The list of winners over the past ten years includes a bunch of very well-known, mostly wonderful organizations: BRAC, PIH, Tostan, PATH, Aravind, Doctors Without Borders. I mean, c’mon—you could pick these names out of a hat. BRAC, for example, is an organization we should all revere and imitate, but its budget in 2012 was $449 million, and it’s already won a zillion prizes. If you gave even a third of the Hilton prize to an up-and-coming organization, it could be transformative.
Too many of these things are winner-or-very-few-take-all, and too many focus on the usual suspects. ..
4. It serves as a distraction from the social sector’s big problem.
The central problem with the social sector is that it does not function as a real market for impact, a market where smart funders channel the vast majority of resources toward those best able to create change. Contests are a sideshow masquerading as a main-stage event, a smokescreen that obscures the lack of efficient allocation of philanthropic and investment capital. We need real competition for impact among social sector organizations, not this faux version that makes the noise-to-signal ratio that much worse….”
See also response by Mayur Patel on Why Open Contests Work