The Hidden Pitfall of Innovation Prizes

Curated on Posted on by Stefaan Verhulst

Reto Hofstetter, John Zhang and Andreas Herrmann at Harvard Business Review: “…it is not so easy to get people to submit their ideas to online innovation platforms. Our data from an online panel reveal that 65% of the contributors do not come back more than twice, and that most of the rest quit after a few tries. This kind of user churn is endemic to online social platforms — on Twitter, for example, a majority of users become inactive over time — and crowdsourcing is no exception. In a way, this turnover is even worse than ordinary customer churn: When a customer defects, a firm knows the value of what it’s lost, but there is no telling how valuable the ideas not submitted might have been….

It is surprising, then, that crowdsourcing on popular platforms is typically designed in a way that amplifies churn. Right now, in typical innovation contests, rewards are granted to winners only and the rest get no return on their participation. This design choice is often motivated by the greater effort participants exert when there is a top prize much more valuable than the rest. Often, the structure is something like the Wimbledon Tennis Championship, where the winning player wins twice as much as the runner up and four times as much as the semifinalists — with the rest eventually leaving empty handed.

This winner-take-most prize spread increases the incentive to win and thus individual efforts. With only one winner, however, the others are left with nothing to show for their effort, which may significantly reduce their motivation to enter again.

An experiment we recently ran confirmed that the way entrants respond to this kind of winner-take-all prize structure. …

In line with the above reasoning, we found that winner-take-all contests yielded significantly better ideas compared to multiple prizes in the first round. Importantly, however, this result flipped when we invited the same cohort of innovators to participate again in the second subsequent contest. While 50% of the multiple-prize contest chose to participate again, only 37% did so when the winner-took-all in their first contest. Moreover, innovators who had received no reward in the first contest showed significantly lower effort in the second contest and generated fewer ideas. In the second contest, multiple prizes generated better ideas than the second round of the winner-take-all contest….

Other non-monetary positive feedback, such as encouraging comments or ratings, can have similar effects. These techniques are important, because alleviating innovator churn helps companies interested in longer-term success of their crowdsourcing activities….(More)”.

Sharing is Daring: An Experiment on Consent, Chilling Effects and a Salient Privacy Nudge

Curated on Posted on by Stefaan Verhulst

Hermstrüwer, Yoan and Dickert, Stephan at the International Review of Law and Economics: “Privacy law rests on the assumption that government surveillance may increase the general level of conformity and thus generate a chilling effect. In a study that combines elements of a lab and a field experiment, we show that salient and incentivized consent options are sufficient to trigger this behavioral effect. Salient ex ante consent options may lure people into giving up their privacy and increase their compliance with social norms – even when the only immediate risk of sharing information is mere publicity on a Google website. A right to be forgotten (right to deletion), however, seems to reduce neither privacy valuations nor chilling effects. In spite of low deletion costs people tend to stick with a retention default. The study suggests that consent architectures may play out on social conformity rather than on consent choices and privacy valuations. Salient notice and consent options may not merely empower users to make an informed consent decision. Instead, they can trigger the very effects that privacy law intends to curb….(More)”.

Nearly All of Wikipedia Is Written By Just 1 Percent of Its Editors

Curated on Posted on by Stefaan Verhulst

Daniel Oberhaus at Motherboard: “…Sixteen years later, the free encyclopedia and fifth most popular website in the world is well on its way to this goal. Today, Wikipedia is home to 43 million articles in 285 languages and all of these articles are written and edited by an autonomous group of international volunteers.

Although the non-profit Wikimedia Foundation diligently keeps track of how editors and users interact with the site, until recently it was unclear how content production on Wikipedia was distributed among editors. According to the results of a recent study that looked at the 250 million edits made on Wikipedia during its first ten years, only about 1 percent of Wikipedia’s editors have generated 77 percent of the site’s content.

“Wikipedia is both an organization and a social movement,” Sorin Matei, the director of the Purdue University Data Storytelling Network and lead author of the study, told me on the phone. “The assumption is that it’s a creation of the crowd, but this couldn’t be further from the truth. Wikipedia wouldn’t have been possible without a dedicated leadership.”

At the time of writing, there are roughly 132,000 registered editors who have been active on Wikipedia in the last month (there are also an unknown number of unregistered Wikipedians who contribute to the site). So statistically speaking, only about 1,300 people are creating over three-quarters of the 600 new articles posted to Wikipedia every day.

Of course, these “1 percenters” have changed over the last decade and a half. According to Matei, roughly 40 percent of the top 1 percent of editors bow out about every five weeks. In the early days, when there were only a few hundred thousand people collaborating on Wikipedia, Matei said the content production was significantly more equitable. But as the encyclopedia grew, and the number of collaborators grew with it, a cadre of die-hard editors emerged that have accounted for the bulk of Wikipedia’s growth ever since.

Matei and his colleague Brian Britt, an assistant professor of journalism at South Dakota State University, used a machine learning algorithm to crawl the quarter of a billion publicly available edit logs from Wikipedia’s first decade of existence. The results of this research, published September as a book, suggests that for all of Wikipedia’s pretension to being a site produced by a network of freely collaborating peers, “some peers are more equal than others,” according to Matei.

Matei and Britt argue that rather than being a decentralized, spontaneously evolving organization, Wikipedia is better described as an “adhocracy“—a stable hierarchical power structure which nevertheless allows for a high degree of individual mobility within that hierarchy….(More)”.

India Social: How Social Media Is Leading The Charge And Changing The Country

Curated on Posted on by Stefaan Verhulst

Book excerpt of Ankit Lal’s book ‘India Social’: on “How social media showed its unique power of crowdsourcing during the Chennai floods…

One ingenious resource that was circulated widely during the floods was a crowdsourced effort that mapped inundated roads in the city. Over 2,500 flooded roads were added to the city’s map via social media, which was put together by engineer and information designer, Arun Ganesh.

The Chennai floods were a superb example of the power of collective effort. Users across social media channels came together to offer shelter, food, transport, and even a place for people to charge their phones. SOS messages asking ground teams to rescue stranded family members also went back and forth, and there were many who offered their homes and offices to those who were stranded.

Perhaps the most simple yet effective tool during the floods was the website chennairains.org.

It began as a simple Google spreadsheet. Sowmya Rao was trying to help her uncle and aunt figure out whether it was safe to stay in their house in suburban Chennai or move to a friend’s place. When she found out that the area they lived in was under severe risk of flooding, she relayed the message to them. But she felt helpless about the countless others who were facing the same plight as her relatives. Acting on a suggestion by another Twitter user, she created the Google spreadsheet that went on to become the website chennairains.org.

The idea was simple: crowdsource details about those who could offer shelter, and pass it on to those who were tweeting about rising waters. A hastily put-together spreadsheet soon blossomed into a multi-faceted, volunteer-driven, highly energetic online movement to help Chennai, and ended up being used by the general public, police officers, government officials and celebrities alike….(More)”.

More Machine Learning About Congress’ Priorities

Curated on Posted on by Stefaan Verhulst

ProPublica: “We keep training machine learning models on Congress. Find out what this one learned about lawmakers’ top issues…

Speaker of the House Paul Ryan is a tax wonk ― and most observers of Congress know that. But knowing what interests the other 434 members of Congress is harder.

To make it easier to know what issues each lawmaker really focuses on, we’re launching a new feature in our Represent database called Policy Priorities. We had two goals in creating it: To help researchers and journalists understand what drives particular members of Congress and to enable regular citizens to compare their representatives’ priorities to their own and their communities.

We created Policy Priorities using some sophisticated computer algorithms (more on this in a second) to calculate interest based on what each congressperson talks ― and brags ― about in their press releases.

Voting and drafting legislation aren’t the only things members of Congress do with their time, but they’re often the main way we analyze congressional data, in part because they’re easily measured. But the job of a member of Congress goes well past voting. They go to committee meetings, discuss policy on the floor and in caucuses, raise funds and ― important for our purposes ― communicate with their constituents and journalists back home. They use press releases to talk about what they’ve accomplished and to demonstrate their commitment to their political ideals.

We’ve been gathering these press releases for a few years, and have a body of some 86,000 that we used for a kind of analysis called machine learning….(More)”.

The frontiers of data interoperability for sustainable development

Curated on Posted on by Stefaan Verhulst

Report from the Joined-Up Data Standards [JUDS] project: “…explores where progress has been made, what challenges still remain, and how the new Collaborative on SDG Data Interoperability will play a critical role in moving forward the agenda for interoperability policy.

There is an ever-growing need for a more holistic picture of development processes worldwide and interoperability solutions that can be scaled, driven by global development agendas such as the 2030 Agenda and the Open Data movement. This requires the ability to join up data across multiple data sources and standards to create actionable information.

Solutions that create value for front-line decision makers — health centre managers, local school authorities or water and sanitation committees, for example, and those engaged in government accountability – will be crucial to meet the data needs of the SDGs, and do so in an internationally comparable way. While progress has been made at both a national and international level, moving from principle to practice by embedding interoperability into day-to-day work continues to present challenges.

Based on research and learning generated by the JUDS project team at Development Initiatives and Publish What You Fund, as well as inputs from interviews with key stakeholders, this report aims to provide an overview of the different definitions and components of interoperability and why it is important, and an outline of the current policy landscape.

We offer a set of guiding principles that we consider essential to implementing interoperability, and contextualise the five frontiers of interoperability for sustainable development that we have identified. The report also offers recommendations on what the role of the Collaborative could be in this fast-evolving landscape….(More)”.

Leveraging the disruptive power of artificial intelligence for fairer opportunities

Curated on Posted on by Stefaan Verhulst

Makada Henry-Nickie at Brookings: “According to President Obama’s Council of Economic Advisers (CEA), approximately 3.1 million jobs will be rendered obsolete or permanently altered as a consequence of artificial intelligence technologies. Artificial intelligence (AI) will, for the foreseeable future, have a significant disruptive impact on jobs. That said, this disruption can create new opportunities if policymakers choose to harness them—including some with the potential to help address long-standing social inequities. Investing in quality training programs that deliver premium skills, such as computational analysis and cognitive thinking, provides a real opportunity to leverage AI’s disruptive power.

AI’s disruption presents a clear challenge: competition to traditional skilled workers arising from the cross-relevance of data scientists and code engineers, who can adapt quickly to new contexts. Data analytics has become an indispensable feature of successful companies across all industries. ….

Investing in high-quality education and training programs is one way that policymakers proactively attempt to address the workforce challenges presented by artificial intelligence. It is essential that we make affirmative, inclusive choices to ensure that marginalized communities participate equitably in these opportunities.

Policymakers should prioritize understanding the demographics of those most likely to lose jobs in the short-run. As opposed to obsessively assembling case studies, we need to proactively identify policy entrepreneurs who can conceive of training policies that equip workers with technical skills of “long-game” relevance. As IBM points out, “[d]ata democratization impacts every career path, so academia must strive to make data literacy an option, if not a requirement, for every student in any field of study.”

Machines are an equal opportunity displacer, blind to color and socioeconomic status. Effective policy responses require collaborative data collection and coordination among key stakeholders—policymakers, employers, and educational institutions—to  identify at-risk worker groups and to inform workforce development strategies. Machine substitution is purely an efficiency game in which workers overwhelmingly lose. Nevertheless, we can blunt these effects by identifying critical leverage points….

Policymakers can choose to harness AI’s disruptive power to address workforce challenges and redesign fair access to opportunity simultaneously. We should train our collective energies on identifying practical policies that update our current agrarian-based education model, which unfairly disadvantages children from economically segregated neighborhoods…(More)”

Democracy is dead: long live democracy!

Curated on Posted on by Stefaan Verhulst

Helen Margetts in OpenDemocracy: “In the course of the World Forum for Democracy 2017, and in political commentary more generally, social media are blamed for almost everything that is wrong with democracy. They are held responsible for pollution of the democratic environment through fake news, junk science, computational propaganda and aggressive micro-targeting. In turn, these phenomena have been blamed for the rise of populism, political polarization, far-right extremism and radicalisation, waves of hate against women and minorities, post-truth, the end of representative democracy, fake democracy and ultimately, the death of democracy. It feels like the tirade of relatives of the deceased at the trial of the murderer. It is extraordinary how much of this litany is taken almost as given, the most gloomy prognoses as certain visions of the future.

Yet actually we know rather little about the relationship between social media and democracy. Because ten years of the internet and social media have challenged everything we thought we knew.  They have injected volatility and instability into political systems, bringing a continual cast of unpredictable events. They bring into question normative models of democracy – by which we might understand the macro-level shifts at work  – seeming to make possible the highest hopes and worst fears of republicanism and pluralism.

They have transformed the ecology of interest groups and mobilizations. They have challenged élites and ruling institutions, bringing regulatory decay and policy sclerosis. They create undercurrents of political life that burst to the surface in seemingly random ways, making fools of opinion polls and pollsters. And although the platforms themselves generate new sources of real-time transactional data that might be used to understand and shape this changed environment, most of this data is proprietary and inaccessible to researchers, meaning that the revolution in big data and data science has passed by democracy research.

What do we know? The value of tiny acts

Certainly digital media are entwined with every democratic institution and the daily lives of citizens. When deciding whether to vote, to support, to campaign, to demonstrate, to complain – digital media are with us at every step, shaping our information environment and extending our social networks by creating hundreds or thousands of ‘weak ties’, particularly for users of social media platforms such as Facebook or Instagram….(More)”.

Open Data in Developing Economies: Toward Building an Evidence Base on What Works and How

Curated on Posted on by Stefaan Verhulst

New book by Stefaan Verhulst and Andrew Young: “Recent years have witnessed considerable speculation about the potential of open data to bring about wide-scale transformation. The bulk of existing evidence about the impact of open data, however, focuses on high-income countries. Much less is known about open data’s role and value in low- and middle-income countries, and more generally about its possible contributions to economic and social development.

Open Data in Developing Economies features in-depth case studies on how open data is having an impact across Screen Shot 2017-11-14 at 5.41.30 AMthe developing world-from an agriculture initiative in Colombia to data-driven healthcare
projects in Uganda and South Africa to crisis response in Nepal. The analysis built on these case studies aims to create actionable intelligence regarding:

(a) the conditions under which open data is most (and least) effective in development, presented in the form of a Periodic Table of Open Data;

(b) strategies to maximize the positive contributions of open data to development; and

(c) the means for limiting open data’s harms on developing countries.

Endorsements:

“An empirically grounded assessment that helps us move beyond the hype that greater access to information can improve the lives of people and outlines the enabling factors for open data to be leveraged for development.”-Ania Calderon, Executive Director, International Open Data Charter

“This book is compulsory reading for practitioners, researchers and decision-makers exploring how to harness open data for achieving development outcomes. In an intuitive and compelling way, it provides valuable recommendations and critical reflections to anyone working to share the benefits of an increasingly networked and data-driven society.”-Fernando Perini, Coordinator of the Open Data for Development (OD4D) Network, International Development Research Centre, Canada

Download full-text PDF – See also: http://odimpact.org/

When Data Science Destabilizes Democracy and Facilitates Genocide

Curated on Posted on by Stefaan Verhulst

Rachel Thomas in Fast.AI on “What is the ethical responsibility of data scientists?”…What we’re talking about is a cataclysmic change… What we’re talking about is a major foreign power with sophistication and ability to involve themselves in a presidential election and sow conflict and discontent all over this country… You bear this responsibility. You’ve created these platforms. And now they are being misused, Senator Feinstein said this week in a senate hearing. Who has created a cataclysmic change? Who bears this large responsibility? She was talking to executives at tech companies and referring to the work of data scientists.

Data science can have a devastating impact on our world, as illustrated by inflammatory Russian propaganda being shown on Facebook to 126 million Americans leading up to the 2016 election (and the subject of the senate hearing described above) or by lies spread via Facebook that are fueling ethnic cleansing in Myanmar. Over half a million Rohinyga have been driven from their homes due to systematic murder, rape, and burning. Data science is foundational to Facebook’s newsfeed, in determining what content is prioritized and who sees what….

The examples of bias in data science are myriad and include:

You can do awesome and meaningful things with data science (such as diagnosing cancer, stopping deforestation, increasing farm yields, and helping patients with Parkinson’s disease), and you can (often unintentionally) enable terrible things with data science, as the examples in this post illustrate. Being a data scientist entails both great opportunity, as well as great responsibility, to use our skills to not make the world a worse place. Ultimately, doing data science is about humans, not just the users of our products, but everyone who will be impacted by our work. (More)”.