Data Portals and Citizen Engagement

Curated on Posted on by Stefaan Verhulst

Series of blogs by Tim Davies: “Portals have been an integral part of the open data movement. They provided a space for publishing and curation of data for governments (usually), and a space to discover and access data for users (often individuals, civil society organisations or sometimes private sector organisations building services or deriving insights from this data). 

While many data portals are still maintained, and while some of them enable access to a sizeable amount of data, portals face some big questions in the decade ahead:

  1. Are open data portals still fit for purpose (and if so, which purpose)?
  2. Do open data portals still “make sense” in this decade, or are they a public sector anomaly in a context when data lakes, data meshes, data platforms are adopted across industry? Is there a minimum viable spec for a future-proof open data “portal”?
  3. What roles and activities have emerged around data platforms and portals that deserve to be codified and supported by the future type of platforms?
  4. Could re-imagined open data “platforms” create change in the role of the public service organisation with regards to data (from publisher to… steward?)?
  5. How can a new generation of portals or data platforms better support citizen engagement and civic participation?
  6. What differences are there between the private and public approaches, and why? Does any difference introduce any significant dynamics in private / public open data ecosystems?…(More)”.

Strengthening CRVS Systems to Improve Migration Policy: A Promising Innovation

Curated on Posted on by Stefaan Verhulst

Blog by Tawheeda Wahabzada and Deirdre Appel: “Migration is one of the most pressing issues of our time and innovation for migration policy can take on several different shapes to help solve challenges. It is seen through radical technological breakthrough such as biometric identifiers that completely transform the status quo as well as technological disruptions like mobile phone fund transforms that alter an existing process. There is also incremental innovation, or the gradual improvement of an existing process or institution even. Regardless of where the fall on the spectrum, their innovative applications are all relevant to migration policy.

Incremental innovation for civil registration and vital statistics (CRVS) systems can greatly benefit migrants and the policymakers trying to help them. According to World Health Organization, a well-functioning CRVS system registers all births and deaths, issues birth and death certificates, and compiles and disseminates vital statistics, including cause of death information. It may also record marriages and divorces. Each of these services brings a world of crucial advantages. But despite the social and legal benefits for individuals, especially migrants, these systems remain underfunded and under functioning. More than 100 low and middle-income countries lack functional CRVS systems and about one-third of all births are not registered. This amounts to more than one billion people without a legal identity leaving them unable to prove who they are and creating serious barriers to access health, education, financial, and other social services.

Throughout countries in Africa, there are great differences in CRVS coverage, where birth coverage ranges from above 90 percent in some North African countries to under 50 percent across several countries in different regions; and with death registration having greater gaps with either no information or lower coverage rates. For countries with low functioning CRVS systems, potential migrants from these countries could face additional obstacles in obtaining birth certificates and proof of identification….(More)”. See also https://data4migration.org/blog/

“If Everybody’s White, There Can’t Be Any Racial Bias”: The Disappearance of Hispanic Drivers From Traffic Records

Curated on Posted on by Stefaan Verhulst

Article by Richard A. Webster: “When sheriff’s deputies in Jefferson Parish, Louisiana, pulled over Octavio Lopez for an expired inspection tag in 2018, they wrote on his traffic ticket that he is white. Lopez, who is from Nicaragua, is Hispanic and speaks only Spanish, said his wife.

In fact, of the 167 tickets issued by deputies to drivers with the last name Lopez over a nearly six-year span, not one of the motorists was labeled as Hispanic, according to records provided by the Jefferson Parish clerk of court. The same was true of the 252 tickets issued to people with the last name of Rodriguez, 234 named Martinez, 223 with the last name Hernandez and 189 with the surname Garcia.

This kind of misidentification is widespread — and not without harm. Across America, law enforcement agencies have been accused of targeting Hispanic drivers, failing to collect data on those traffic stops, and covering up potential officer misconduct and aggressive immigration enforcement by identifying people as white on tickets.

“If everybody’s white, there can’t be any racial bias,” Frank Baumgartner, a political science professor at the University of North Carolina of Chapel Hill, told WWNO/WRKF and ProPublica.

Nationally, states have tried to patch this data loophole and tighten controls against racial profiling. In recent years, legislators have passed widely hailed traffic stop data-collection laws in California, Colorado, Illinois, Oregon, Virginia and Washington, D.C. This April, Alabama became the 22nd state to enact similar legislation.

Though Louisiana has had its own data-collection requirement for two decades, it contains a loophole unlike any other state: It exempts law enforcement agencies from collecting and delivering data to the state if they have an anti-racial-profiling policy in place. This has rendered the law essentially worthless, said Josh Parker, a senior staff attorney at the Policing Project, a public safety research nonprofit at the New York University School of Law.

Louisiana State Rep. Royce Duplessis, D-New Orleans, attempted to remove the exemption two years ago, but law enforcement agencies protested. Instead, he was forced to convene a task force to study the issue, which thus far hasn’t produced any results, he said.

“They don’t want the data because they know what it would reveal,” Duplessis said of law enforcement agencies….(More)”.

How digital minilaterals can revive international cooperation

Curated on Posted on by Stefaan Verhulst

Blog by Tanya Filer and Antonio Weiss: “From London to the Organisation for Economic Co-operation and Development, calls to “reimagine” or “revive” multilateralism have been a dime a dozen this year. The global upheaval of COVID-19 and emerging megatrends—from the climate crisis to global population growth—have afforded a new urgency to international cooperation and highlighted a growing sclerosis within multilateralism that even its greatest proponents admit. 

While these calls—and the rethinking they are beginning to provoke—are crucial, a truly new and nuanced multilateralism will require room for other models too. As we described in a paper published last year at the Bennett Institute for Public Policy at the University of Cambridge, digital minilaterals are providing a new model for international cooperation. Made up of small, trust-based, innovation-oriented networks, digital minilaterals use digital culture, practices, processes, and technologies as tools to advance peer learning, support, and cooperation between governments. 

Though far removed from great power politics, digital minilaterals are beginning to help nation-states navigate an environment of rapid technological change and problems of complex systems, including through facilitating peer-learning, sharing code base, and deliberating on major ethical questions, such as the appropriate use of artificial intelligence in society. Digital minilateralism is providing a decentralized form of global cooperation and could help revive multilateralism. To be truly effective, digital minilaterals must place as much emphasis on common values as on pooled knowledge, but it remains to be seen whether these new diplomatic groupings will deliver on their promise….(More)”.

Do we know what jobs are in high demand?

Curated on Posted on by Stefaan Verhulst

Emma Rindlisbacher at Work Shift: “…Measuring which fields are in demand is harder than it sounds. Many of the available data sources, experts say, have significant flaws. And that causes problems for education providers who are trying to understand market demand and map their programs to it.

“If you are in higher education and trying to understand where the labor market is going, use BLS data as a general guide but do not rely too heavily on it when it comes to building programs and making investments,” said Jason Tyszko, the Vice President of the Center for Education and Workforce at the US Chamber of Commerce Foundation.

What’s In-Demand?

Why it matters: Colleges are turning to labor market data as they face increasing pressure from lawmakers and the public to demonstrate value and financial ROI. A number of states also have launched specialized grant and “free college” programs for residents pursuing education in high-demand fields. And many require state agencies to determine which fields are in high demand as part of workforce planning processes.

Virginia is one of those states. To comply with state law, the Board of Workforce Development has to regularly update a list of high demand occupations. Deciding how to do so can be challenging.

According to a presentation given at a September 2021 meeting, the board chose to determine which occupations are in high demand by using BLS data. The reason: the BLS data is publicly available.

“Although in some instances, proprietary data sources have different or additional nuances, in service of guiding principle #1 (transparency, replicability), our team has relied exclusively on publicly available data for this exercise,” the presentation said. (A representative from the board declined to comment, citing the still ongoing nature of constructing the high demand occupations list.)

The limits of the gold standard

For institutions looking to study job market trends, there are typically two main data sources available. The first, from BLS, are official government statistics primarily designed to track economic indicators such as the unemployment rate. The second, from proprietary companies such as Emsi Burning Glass, typically relies on postings to job board websites like LinkedIn. 

The details: The two sources have different strengths and weaknesses. The Emsi Burning Glass data can be considered “real time” data, because it identifies new job postings as they are released online. The BLS data, on the other hand, is updated less frequently but is comprehensive.

The BLS data is designed to compare economic trends across decades, and to map to state systems so that statistics like unemployment rates can be compared across states. For those reasons, the agency is reluctant to change the definitions underlying the data. That consistency, however, can make it difficult for education providers to use the data to determine which fields are in high demand.

BLS data is broken down according to the Standard Occupation Classification system, or SOC, a taxonomy used to classify different occupations. That taxonomy is designed to be public facing—the BLS website, for example, features a guide for job seekers that purports to tell them which occupation codes have the highest wages or the greatest potential for growth.

But the taxonomy was last updated in 2010, according to a BLS spokesperson…(More)”.

New York City passed a bill requiring ‘bias audits’ of AI hiring tech

Curated on Posted on by Stefaan Verhulst

Kate Kaye at Protocol: “Let the AI auditing vendor brigade begin. A year since it was introduced, New York City Council passed a bill earlier this week requiring companies that sell AI technologies for hiring to obtain audits assessing the potential of those products to discriminate against job candidates. The bill requiring “bias audits” passed with overwhelming support in a 38-4 vote.

The bill is intended to weed out the use of tools that enable already unlawful employment discrimination in New York City. If signed into law, it will require providers of automated employment decision tools to have those systems evaluated each year by an audit service and provide the results to companies using those systems.

AI for recruitment can include software that uses machine learning to sift through resumes and help make hiring decisions, systems that attempt to decipher the sentiments of a job candidate, or even tech involving games to pick up on subtle clues about someone’s hiring worthiness. The NYC bill attempts to encompass the full gamut of AI by covering everything from old-school decision trees to more complex systems operating through neural networks.

The legislation calls on companies using automated decision tools for recruitment not only to tell job candidates when they’re being used, but to tell them what information the technology used to evaluate their suitability for a job.

The bill, however, fails to go into detail on what constitutes a bias audit other than to define one as “an impartial evaluation” that involves testing. And it already has critics who say it was rushed into passage and doesn’t address discrimination related to disability or age…(More)”.

The Census Mapper

Curated on Posted on by Stefaan Verhulst

Google blog: “…The U.S. Census is one of the largest data sets journalists can access. It has layers and layers of important data that can help reporters tell detailed stories about their own communities. But the challenge is sorting through that data and visualizing it in a way that helps readers understand trends and the bigger picture.

Today we’re launching a new tool to help reporters dig through all that data to find stories and embed visualizations on their sites. The Census Mapper project is an embeddable map that displays Census data at the national, state and county level, as well as census tracts. It was produced in partnership with Pitch Interactive and Big Local News, as part of the 2020 Census Co-op (supported by the Google News Initiative and in cooperation with the JSK Journalism Fellowships).

This image shows a detailed, country level view of the Census Mapper, showing arrows across the US depicting movements of people and other demographic information from the Census

Census Mapper shows where populations have grown over time.

The Census data is pulled from the data collected and processed by The Associated Press, one of the Census Co-op partners. Census Mapper then lets local journalists easily embed maps showing population change at any level, helping them tell powerful stories in a more visual way about their communities.

This image shows changing demographic data from North Carolina, with arrows showing different movements around the state.

With the tool, you can zoom into states and below, such as North Carolina, shown here.

As part of our investment in data journalism we’re also making improvements to our Common Knowledge Project, a data explorer and visual journalism project to allow US journalists to explore local data. Built with journalists for journalists, the new version of Common Knowledge integrates journalist feedback and new features including geographic comparisons, new charts and visuals…(More)”.

Why Are We Failing at AI Ethics?

Curated on Posted on by Stefaan Verhulst

Article by Anja Kaspersen and Wendell Wallach: “…Extremely troubling is the fact that the people who are most vulnerable to negative impacts from such rapid expansions of AI systems are often the least likely to be able to join the conversation about these systems, either because they have no or restricted digital access or their lack of digital literacy makes them ripe for exploitation.

Such vulnerable groups are often theoretically included in discussions, but not empowered to take a meaningful part in making decisions. This engineered inequity, alongside human biases, risks amplifying otherness through neglect, exclusion, misinformation, and disinformation.

Society should be deeply concerned that nowhere near enough substantive progress is being made to develop and scale actionable legal, ethical oversight while simultaneously addressing existing inequalities.

So, why hasn’t more been done? There are three main issues at play: 

First, many of the existing dialogues around the ethics of AI and governance are too narrow and fail to understand the subtleties and life cycles of AI systems and their impacts.

Often, these efforts focus only on the development and deployment stages of the technology life cycle, when many of the problems occur during the earlier stages of conceptualization, research, and design. Or they fail to comprehend when and if AI system operates at a level of maturity required to avoid failure in complex adaptive systems.

Or they focus on some aspects of ethics, while ignoring other aspects that are more fundamental and challenging. This is the problem known as “ethics washing” – creating a superficially reassuring but illusory sense that ethical issues are being adequately addressed, to justify pressing forward with systems that end up deepening current patterns.

Let’s be clear: every choice entails tradeoffs. “Ethics talk” is often about underscoring the various tradeoffs entailed in differing courses of action. Once a course has been selected, comprehensive ethical oversight is also about addressing the considerations not accommodated by the options selected, which is essential to any future verification effort. This vital part of the process is often a stumbling block for those trying to address the ethics of AI.

The second major issue is that to date all the talk about ethics is simply that: talk. 

We’ve yet to see these discussions translate into meaningful change in managing the ways in which AI systems are being embedded into various aspect of our lives….

A third issue at play is that discussions on AI and ethics are still largely confined to the ivory tower.

There is an urgent need for more informed public discourse and serious investment in civic education around the societal impact of the bio-digital revolution. This could help address the first two problems, but most of what the general public currently perceives about AI comes from sci-fi tropes and blockbuster movies.

A few examples of algorithmic bias have penetrated the public discourse. But the most headline-grabbing research on AI and ethics tends to focus on far-horizon existential risks. More effort needs to be invested in communicating to the public that, beyond the hypothetical risks of future AI, there are real and imminent risks posed by why and how we embed AI systems that currently shape everyone’s daily lives….(More)”.

Do Awards Incentivize Non-Winners to Work Harder on CSR?

Curated on Posted on by Stefaan Verhulst

Article by Jiangyan Li, Juelin Yin, Wei Shi, And Xiwei Yi: “As corporate lists and awards that rank and recognize firms for superior social reputation have proliferated in recent years, the field of CSR is also replete with various types of awards given out to firms or CEOs, such as Fortune’s “Most Admired Companies” rankings and “Best 100 Companies to Work For” lists. Such awards serve to both reward and incentivize firms to become more dedicated to CSR. Prior research has primarily focused on the effects of awards on award-winning firms; however, the effectiveness and implications of such awards as incentives to non-winning firms remain understudied. Therefore, in the article of “Keeping up with the Joneses: Role of CSR Awards in Incentivizing Non-Winners’ CSR” published by Business & Society, we are curious about whether such CSR awards could successfully incentivize non-winning firms to catch up with their winning competitors.

Drawing on the awareness-motivation-capability (AMC) framework developed in the competitive dynamics literature, we use a sample of Chinese listed firms from 2009 to 2015 to investigate how competitors’ CSR award winning can influence focal firms’ CSR. The empirical results show that non-winning firms indeed improve their CSR after their competitors have won CSR awards. However, non-winning firms’ improvement in CSR may vary in different scenarios. For instance, media exposure can play an important informational role in reducing information asymmetries and inducing competitive actions among competitors, therefore, non-winning firms’ improvement in CSR is more salient when award-winning firms are more visible in the media. Meanwhile, when CSR award winners perform better financially, non-winners will be more motivated to respond to their competitors’ wins. Further, firms with a higher level of prior CSR are more capable of improving their CSR and therefore are more likely to respond to their competitors’ wins…(More)”.

The “9Rs Framework”: Establishing the Business Case for Data Collaboration and Re-Using Data in the Public Interest

Curated on Posted on by Stefaan Verhulst

Article by Stefaan G. Verhulst, Andrew Young, and Andrew J. Zahuranec: “When made accessible and re-used responsibly, privately held data has the potential to generate enormous public value. Whether it’s enabling better science, supporting evidence-based government programs, or helping community groups to identify people who need help, data can be used to make better public interest decisions and improve people’s lives.

Yet, for all the discussion of the societal value of having organizations provide access to their data, there’s been little discussion of the business case on why to make data available for reuse. What motivates an organization to make its datasets accessible for societal purposes? How does doing so support their organizational goals? What’s the return on investment of using organizational resources to make data available to others?

GRAPHIC: The 9Rs Framework: The Business Case for Data Reuse in the Public Interest

The Open Data Policy Lab addresses these questions with its “9R Framework,” a method for describing and identifying the business case for data reuse for the public good. The 9R Framework consists of nine motivations identified through several years of studying and establishing data collaboratives, categorized by different types of return on investment: license to operate, brand equity, or knowledge and insights. Considered together, these nine motivations add up to a model to help organizations understand the business value of making their data assets accessible….(More)”.