Google is using AI to predict floods in India and warn users

Curated on Posted on by Stefaan Verhulst

James Vincent at The Verge: “For years Google has warned users about natural disasters by incorporating alerts from government agencies like FEMA into apps like Maps and Search. Now, the company is making predictions of its own. As part of a partnership with the Central Water Commission of India, Google will now alert users in the country about impending floods. The service is only currently available in the Patna region, with the first alert going out earlier this month.

As Google’s engineering VP Yossi Matias outlines in a blog post, these predictions are being made using a combination of machine learning, rainfall records, and flood simulations.

“A variety of elements — from historical events, to river level readings, to the terrain and elevation of a specific area — feed into our models,” writes Matias. “With this information, we’ve created river flood forecasting models that can more accurately predict not only when and where a flood might occur, but the severity of the event as well.”

The US tech giant announced its partnership with the Central Water Commission back in June. The two organizations agreed to share technical expertise and data to work on the predictions, with the Commission calling the collaboration a “milestone in flood management and in mitigating the flood losses.” Such warnings are particularly important in India, where 20 percent of the world’s flood-related fatalities are estimated to occur….(More)”.

Mission Failure

Curated on Posted on by Stefaan Verhulst

Matthew Sawh at Stanford Social Innovation Review: “Exposing the problems of policy schools can ignite new ways to realize the mission of educating public servants in the 21st century….

Public policy schools were founded with the aim to educate public servants with academic insights that could be applied to government administration. And while these programs have adapted the tools and vocabularies of the Reagan Revolution, such as the use of privatization and the rhetoric of competition, they have not come to terms with his philosophical legacy that describes our contemporary political culture. To do so, public policy schools need to acknowledge that the public perceives the government as the problem, not the solution, to society’s ills. Today, these programs need to ask how decisionmakers should improve the design of their organizations, their decision-making processes, and their curriculum in order to address the public’s skeptical mindset.

I recently attended a public policy school, Columbia University’s School of International and Public Affairs (SIPA), hoping to learn how to bridge the distrust between public servants and citizens, and to help forge bonds between bureaucracies and voters who feel ignored by their government officials. Instead of building bridges across these divides, the curriculum of my policy program reinforced them—training students to navigate bureaucratic silos in our democracy. Of course, public policy students go to work in the government we have, not the government we wish we had—but that’s the point. These schools should lead the national conversation and equip their graduates to think and act beyond the divides between the governing and the governed.

Most US public policy programs require a core set of courses, including macroeconomics, microeconomics, statistics, and organizational management. SIPA has broader requirements, including a financial management course, a client consulting workshop, and an internship. Both sets of core curricula undervalue the intrapersonal and interpersonal elements of leadership, particularly politics, which I define aspersuasion, particularly within groups and institutions.

Public service is more than developing smart ideas; it entails the ability to marshal the financial, political, and organizational supports to make those ideas resonate with the public and take effect in government policy. Unfortunately, these programs aren’t adequately training early career professionals to implement their ideas by giving short shrift to the intrapersonal and institutional contexts of real changemaking.

Within the core curriculum, the story of change is told as the product of processes wherein policymakers can know the rational expectations of the public. But the people themselves have concerns beyond those perceived by policymakers. As public servants, our success depends on our ability to meet people where they are, rather than where we suppose they should be.  …

Public policy schools must reach a consensus on core identity questions: Who is best placed to lead a policy school? What are their aims in crafting a professional class? What exactly should a policy degree mean in the wider world? The problem is that these programs are meant to teach students about not only the science of good government, but the human art of good governance.

Curricula based on an outdated sense both of the political process and of advocacy is a predominant feature of policy programs. Instead, core courses should cover how to advocate effectively in this new political world of the 21st century. Students should learn how to raise money for a political campaign; how to lobby; how to make an advertising budget; and how to purchase airtime in the digital age…(More)”

Making Wage Data Work: Creating a Federal Resource for Evidence and Transparency

Curated on Posted on by Stefaan Verhulst

Christina Pena at the National Skills Coalition: “Administrative data on employment and earnings, commonly referred to as wage data or wage records, can be used to assess the labor market outcomes of workforce, education, and other programs, providing policymakers, administrators, researchers, and the public with valuable information. However, there is no single readily accessible federal source of wage data which covers all workers. Noting the importance of employment and earnings data to decision makers, the Commission on Evidence-Based Policymaking called for the creation of a single federal source of wage data for statistical purposes and evaluation. They recommended three options for further exploration: expanding access to systems that already exist at the U.S. Census Bureau or the U.S. Department of Health and Human Services (HHS), or creating a new database at the U.S. Department of Labor (DOL).

This paper reviews current coverage and allowable uses, as well as federal and state actions required to make each option viable as a single federal source of wage data that can be accessed by government agencies and authorized researchers. Congress and the President, in conjunction with relevant federal and state agencies, should develop one or more of those options to improve wage information for multiple purposes. Although not assessed in the following review, financial as well as privacy and security considerations would influence the viability of each scenario. Moreover, if a system like the Commission-recommended National Secure Data Service for sharing data between agencies comes to fruition, then a wage system might require additional changes to work with the new service….(More)”

Computers Can Solve Your Problem. You May Not Like The Answer

Curated on Posted on by Stefaan Verhulst

David Scharfenberg at the Boston Globe: “Years of research have shown that teenagers need their sleep. Yet high schools often start very early in the morning. Starting them later in Boston would require tinkering with elementary and middle school schedules, too — a Gordian knot of logistics, pulled tight by the weight of inertia, that proved impossible to untangle.

Until the computers came along.

Last year, the Boston Public Schools asked MIT graduate students Sébastien Martin and Arthur Delarue to build an algorithm that could do the enormously complicated work of changing start times at dozens of schools — and rerouting the hundreds of buses that serve them….

The algorithm was poised to put Boston on the leading edge of a digital transformation of government. In New York, officials were using a regression analysis tool to focus fire inspections on the most vulnerable buildings. And in Allegheny County, Pa., computers were churning through thousands of health, welfare, and criminal justice records to help identify children at risk of abuse….

While elected officials tend to legislate by anecdote and oversimplify the choices that voters face, algorithms can chew through huge amounts of complicated information. The hope is that they’ll offer solutions we’ve never imagined ­— much as Google Maps, when you’re stuck in traffic, puts you on an alternate route, down streets you’ve never traveled.

Dataphiles say algorithms may even allow us to filter out the human biases that run through our criminal justice, social service, and education systems. And the MIT algorithm offered a small window into that possibility. The data showed that schools in whiter, better-off sections of Boston were more likely to have the school start times that parents prize most — between 8 and 9 a.m. The mere act of redistributing start times, if aimed at solving the sleep deprivation problem and saving money, could bring some racial equity to the system, too.

Or, the whole thing could turn into a political disaster.

District officials expected some pushback when they released the new school schedule on a Thursday night in December, with plans to implement in the fall of 2018. After all, they’d be messing with the schedules of families all over the city.

But no one anticipated the crush of opposition that followed. Angry parents signed an online petition and filled the school committee chamber, turning the plan into one of the biggest crises of Mayor Marty Walsh’s tenure. The city summarily dropped it. The failure would eventually play a role in the superintendent’s resignation.

It was a sobering moment for a public sector increasingly turning to computer scientists for help in solving nagging policy problems. What had gone wrong? Was it a problem with the machine? Or was it a problem with the people — both the bureaucrats charged with introducing the algorithm to the public, and the public itself?…(More)”

The role of corporations in addressing AI’s ethical dilemmas

Curated on Posted on by Stefaan Verhulst

Darrell M. West at Brookings: “In this paper, I examine five AI ethical dilemmas: weapons and military-related applications, law and border enforcement, government surveillance, issues of racial bias, and social credit systems. I discuss how technology companies are handling these issues and the importance of having principles and processes for addressing these concerns. I close by noting ways to strengthen ethics in AI-related corporate decisions.

Briefly, I argue it is important for firms to undertake several steps in order to ensure that AI ethics are taken seriously:

  1. Hire ethicists who work with corporate decisionmakers and software developers
  2. Develop a code of AI ethics that lays out how various issues will be handled
  3. Have an AI review board that regularly addresses corporate ethical questions
  4. Develop AI audit trails that show how various coding decisions have been made
  5. Implement AI training programs so staff operationalizes ethical considerations in their daily work, and
  6. Provide a means for remediation when AI solutions inflict harm or damages on people or organizations….(More)”.

Data-Driven Government: The Role of Chief Data Officers

Curated on Posted on by Stefaan Verhulst

Jane Wiseman for IBM Center for The Business of Government: “Governments at all levels have seen dramatic increases in availability and use of data over the past decade.

The push for data-driven government is currently of intense interest at the federal level as it develops an integrated federal data strategy as part of its goal to “leverage data as a strategic asset.” There is also pending legislation to require agencies to designate chief data officers (CDOs).

This report focuses on the expanding use of data at the federal level and how to best manage it. Ms. Wiseman says: “The purpose of this report is to advance the use of data in government by describing the work of pioneering federal CDOs and providing a framework for thinking about how a new analytics leader might establish his or her office and use data to advance the mission of the agency.”

Ms. Wiseman’s report provides rich profiles of five pioneering CDOs in the federal government and how they have defined their new roles. Based on her research and interviews, she offers insights into how the role of agency CDOs is evolving in different agencies and the reasons agency leaders are establishing these roles.  She also offers advice on how new CDOs can be successful at the federal level, based on the experiences of the pioneers as well as the experiences of state and local CDOs….(More)”.

Swarm AI Outperforms in Stanford Medical Study

Curated on Posted on by Stefaan Verhulst

Press Release: “Stanford University School of Medicine and Unanimous AI presented a new study today showing that a small group of doctors, connected by intelligence algorithms that enable them to work together as a “hive mind,” could achieve higher diagnostic accuracy than the individual doctors or machine learning algorithms alone.  The technology used is called Swarm AI and it empowers networked human groups to combine their individual insights in real-time, using AI algorithms to converge on optimal solutions.

As presented at the 2018 SIIM Conference on Machine Intelligence in Medical Imaging, the study tasked a group of experienced radiologists with diagnosing the presence of pneumonia in chest X-rays. This is one of the most widely performed imaging procedures in the US, with more than 1 million adults hospitalized with pneumonia each year. But, despite this prevalence, accurately diagnosing X-rays is highly challenging with significant variability across radiologists. This makes it both an optimal task for applying new AI technologies, and an important problem to solve for the medical community.

When generating diagnoses using Swarm AI technology, the average error rate was reduced by 33% compared to traditional diagnoses by individual practitioners.  This is an exciting result, showing the potential of AI technologies to amplify the accuracy of human practitioners while maintaining their direct participation in the diagnostic process.

Swarm AI technology was also compared to the state-of-the-art in automated diagnosis using software algorithms that do not employ human practitioners.  Currently, the best system in the world for the automated diagnosing of pneumonia from chest X-rays is the CheXNet system from Stanford University, which made headlines in 2017 by significantly outperforming individual practitioners using deep-learning derived algorithms.

The Swarm AI system, which combines real-time human insights with AI technology, was 22% more accurate in binary classification than the software-only CheXNet system.  In other words, by connecting a group of radiologists into a medical “hive mind”, the hybrid human-machine system was able to outperform individual human doctors as well as the state-of-the-art in deep-learning derived algorithms….(More)”.

Don’t forget people in the use of big data for development

Curated on Posted on by Stefaan Verhulst

Joshua Blumenstock at Nature: “Today, 95% of the global population has mobile-phone coverage, and the number of people who own a phone is rising fast (see ‘Dialling up’)1. Phones generate troves of personal data on billions of people, including those who live on a few dollars a day. So aid organizations, researchers and private companies are looking at ways in which this ‘data revolution’ could transform international development.

Some businesses are starting to make their data and tools available to those trying to solve humanitarian problems. The Earth-imaging company Planet in San Francisco, California, for example, makes its high-resolution satellite pictures freely available after natural disasters so that researchers and aid organizations can coordinate relief efforts. Meanwhile, organizations such as the World Bank and the United Nations are recruiting teams of data scientists to apply their skills in statistics and machine learning to challenges in international development.

But in the rush to find technological solutions to complex global problems there’s a danger of researchers and others being distracted by the technology and losing track of the key hardships and constraints that are unique to each local context. Designing data-enabled applications that work in the real world will require a slower approach that pays much more attention to the people behind the numbers…(More)”.

Ethics and Data Science

Curated on Posted on by Stefaan Verhulst

(Open) Ebook by Mike Loukides, Hilary Mason and DJ Patil: “As the impact of data science continues to grow on society there is an increased need to discuss how data is appropriately used and how to address misuse. Yet, ethical principles for working with data have been available for decades. The real issue today is how to put those principles into action. With this report, authors Mike Loukides, Hilary Mason, and DJ Patil examine practical ways for making ethical data standards part of your work every day.

To help you consider all of possible ramifications of your work on data projects, this report includes:

  • A sample checklist that you can adapt for your own procedures
  • Five framing guidelines (the Five C’s) for building data products: consent, clarity, consistency, control, and consequences
  • Suggestions for building ethics into your data-driven culture

Now is the time to invest in a deliberate practice of data ethics, for better products, better teams, and better outcomes….(More)”.

Decentralisation: the next big step for the world wide web

Curated on Posted on by Stefaan Verhulst

Zoë Corbyn at The Observer: “The decentralised web, or DWeb, could be a chance to take control of our data back from the big tech firms. So how does it work and when will it be here?...What is the decentralised web? 
It is supposed to be like the web you know but without relying on centralised operators. In the early days of the world wide web, which came into existence in 1989, you connected directly with your friends through desktop computers that talked to each other. But from the early 2000s, with the advent of Web 2.0, we began to communicate with each other and share information through centralised services provided by big companies such as Google, Facebook, Microsoft and Amazon. It is now on Facebook’s platform, in its so called “walled garden”, that you talk to your friends. “Our laptops have become just screens. They cannot do anything useful without the cloud,” says Muneeb Ali, co-founder of Blockstack, a platform for building decentralised apps. The DWeb is about re-decentralising things – so we aren’t reliant on these intermediaries to connect us. Instead users keep control of their data and connect and interact and exchange messages directly with others in their network.

Why do we need an alternative? 
With the current web, all that user data concentrated in the hands of a few creates risk that our data will be hacked. It also makes it easier for governments to conduct surveillance and impose censorship. And if any of these centralised entities shuts down, your data and connections are lost. Then there are privacy concerns stemming from the business models of many of the companies, which use the private information we provide freely to target us with ads. “The services are kind of creepy in how much they know about you,” says Brewster Kahle, the founder of the Internet Archive. The DWeb, say proponents, is about giving people a choice: the same services, but decentralised and not creepy. It promises control and privacy, and things can’t all of a sudden disappear because someone decides they should. On the DWeb, it would be harder for the Chinese government to block a site it didn’t like, because the information can come from other places.

How does the DWeb work that is different? 

There are two big differences in how the DWeb works compared to the world wide web, explains Matt Zumwalt, the programme manager at Protocol Labs, which builds systems and tools for the DWeb. First, there is this peer-to-peer connectivity, where your computer not only requests services but provides them. Second, how information is stored and retrieved is different. Currently we use http and https links to identify information on the web. Those links point to content by its location, telling our computers to find and retrieve things from those locations using the http protocol. By contrast, DWeb protocols use links that identify information based on its content – what it is rather than where it is. This content-addressed approach makes it possible for websites and files to be stored and passed around in many ways from computer to computer rather than always relying on a single server as the one conduit for exchanging information. “[In the traditional web] we are pointing to this location and pretending [the information] exists in only one place,” says Zumwalt. “And from this comes this whole monopolisation that has followed… because whoever controls the location controls access to the information.”…(More)”.