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)”.

Commonism: A New Aesthetics of the Real

Curated on Posted on by Stefaan Verhulst

Book edited by Nico Dockx and Pascal Gielen: “After half a century of neoliberalism, a new radical, practice-based ideology is making its way from the margins: commonism, with an o in the middle. It is based on the values of sharing, common (intellectual) ownership and new social co-operations. Commoners assert that social relationships can replace money (contract) relationships. They advocate solidarity and they trust in peer-to-peer relationships to develop new ways of production.

Commonism maps those new ideological thoughts. How do they work and, especially, what is their aesthetics? How do they shape the reality of our living together? Is there another, more just future imaginable through the commons? What strategies and what aesthetics do commoners adopt? This book explores this new political belief system, alternating between theoretical analysis, wild artistic speculation, inspiring art examples, almost empirical observations and critical reflection….(More)”.

Google launches new search engine to help scientists find the datasets they need

Curated on Posted on by Stefaan Verhulst

James Vincent at The Verge: “The service, called Dataset Search, launches today, and it will be a companion of sorts to Google Scholar, the company’s popular search engine for academic studies and reports. Institutions that publish their data online, like universities and governments, will need to include metadata tags in their webpages that describe their data, including who created it, when it was published, how it was collected, and so on. This information will then be indexed by Google’s search engine and combined with information from the Knowledge Graph. (So if dataset X was published by CERN, a little information about the institute will also be included in the search.)

Speaking to The Verge, Natasha Noy, a research scientist at Google AI who helped created Dataset Search, says the aim is to unify the tens of thousands of different repositories for datasets online. “We want to make that data discoverable, but keep it where it is,” says Noy.

At the moment, dataset publication is extremely fragmented. Different scientific domains have their own preferred repositories, as do different governments and local authorities. “Scientists say, ‘I know where I need to go to find my datasets, but that’s not what I always want,’” says Noy. “Once they step out of their unique community, that’s when it gets hard.”

Noy gives the example of a climate scientist she spoke to recently who told her she’d been looking for a specific dataset on ocean temperatures for an upcoming study but couldn’t find it anywhere. She didn’t track it down until she ran into a colleague at a conference who recognized the dataset and told her where it was hosted. Only then could she continue with her work. “And this wasn’t even a particularly boutique depository,” says Noy. “The dataset was well written up in a fairly prominent place, but it was still difficult to find.”

An example search for weather records in Google Dataset Search.
 Image: Google

The initial release of Dataset Search will cover the environmental and social sciences, government data, and datasets from news organizations like ProPublica. However, if the service becomes popular, the amount of data it indexes should quickly snowball as institutions and scientists scramble to make their information accessible….(More)”.

Reflecting the Past, Shaping the Future: Making AI Work for International Development

Curated on Posted on by Stefaan Verhulst

USAID Report: “We are in the midst of an unprecedented surge of interest in machine learning (ML) and artificial intelligence (AI) technologies. These tools, which allow computers to make data-derived predictions and automate decisions, have become part of daily life for billions of people. Ubiquitous digital services such as interactive maps, tailored advertisements, and voice-activated personal assistants are likely only the beginning. Some AI advocates even claim that AI’s impact will be as profound as “electricity or fire” that it will revolutionize nearly every field of human activity. This enthusiasm has reached international development as well. Emerging ML/AI applications promise to reshape healthcare, agriculture, and democracy in the developing world. ML and AI show tremendous potential for helping to achieve sustainable development objectives globally. They can improve efficiency by automating labor-intensive tasks, or offer new insights by finding patterns in large, complex datasets. A recent report suggests that AI advances could double economic growth rates and increase labor productivity 40% by 2035. At the same time, the very nature of these tools — their ability to codify and reproduce patterns they detect — introduces significant concerns alongside promise.

In developed countries, ML tools have sometimes been found to automate racial profiling, to foster surveillance, and to perpetuate racial stereotypes. Algorithms may be used, either intentionally or unintentionally, in ways that result in disparate or unfair outcomes between minority and majority populations. Complex models can make it difficult to establish accountability or seek redress when models make mistakes. These shortcomings are not restricted to developed countries. They can manifest in any setting, especially in places with histories of ethnic conflict or inequality. As the development community adopts tools enabled by ML and AI, we need a cleareyed understanding of how to ensure their application is effective, inclusive, and fair. This requires knowing when ML and AI offer a suitable solution to the challenge at hand. It also requires appreciating that these technologies can do harm — and committing to addressing and mitigating these harms.

ML and AI applications may sometimes seem like science fiction, and the technical intricacies of ML and AI can be off-putting for those who haven’t been formally trained in the field. However, there is a critical role for development actors to play as we begin to lean on these tools more and more in our work. Even without technical training in ML, development professionals have the ability — and the responsibility — to meaningfully influence how these technologies impact people.

You don’t need to be an ML or AI expert to shape the development and use of these tools. All of us can learn to ask the hard questions that will keep solutions working for, and not against, the development challenges we care about. Development practitioners already have deep expertise in their respective sectors or regions. They bring necessary experience in engaging local stakeholders, working with complex social systems, and identifying structural inequities that undermine inclusive progress. Unless this expert perspective informs the construction and adoption of ML/AI technologies, ML and AI will fail to reach their transformative potential in development.

This document aims to inform and empower those who may have limited technical experience as they navigate an emerging ML/AI landscape in developing countries. Donors, implementers, and other development partners should expect to come away with a basic grasp of common ML techniques and the problems ML is uniquely well-suited to solve. We will also explore some of the ways in which ML/AI may fail or be ill-suited for deployment in developing-country contexts. Awareness of these risks, and acknowledgement of our role in perpetuating or minimizing them, will help us work together to protect against harmful outcomes and ensure that AI and ML are contributing to a fair, equitable, and empowering future…(More)”.