Data versus Democracy

Curated on Posted on by Stefaan Verhulst

Book by  Kris Shaffer: “Human attention is in the highest demand it has ever been. The drastic increase in available information has compelled individuals to find a way to sift through the media that is literally at their fingertips. Content recommendation systems have emerged as the technological solution to this social and informational problem, but they’ve also created a bigger crisis in confirming our biases by showing us only, and exactly, what it predicts we want to see. Data versus Democracy investigates and explores how, in the era of social media, human cognition, algorithmic recommendation systems, and human psychology are all working together to reinforce (and exaggerate) human bias. The dangerous confluence of these factors is driving media narratives, influencing opinions, and possibly changing election results. 


In this book, algorithmic recommendations, clickbait, familiarity bias, propaganda, and other pivotal concepts are analyzed and then expanded upon via fascinating and timely case studies: the 2016 US presidential election, Ferguson, GamerGate, international political movements, and more events that come to affect every one of us. What are the implications of how we engage with information in the digital age? Data versus Democracy explores this topic and an abundance of related crucial questions. We live in a culture vastly different from any that has come before. In a society where engagement is currency, we are the product. Understanding the value of our attention, how organizations operate based on this concept, and how engagement can be used against our best interests is essential in responsibly equipping ourselves against the perils of disinformation….(More)”.

This High-Tech Solution to Disaster Response May Be Too Good to Be True

Curated on Posted on by Stefaan Verhulst

Sheri Fink in The New York Times: “The company called One Concern has all the characteristics of a buzzy and promising Silicon Valley start-up: young founders from Stanford, tens of millions of dollars in venture capital and a board with prominent names.

Its particular niche is disaster response. And it markets a way to use artificial intelligence to address one of the most vexing issues facing emergency responders in disasters: figuring out where people need help in time to save them.

That promise to bring new smarts and resources to an anachronistic field has generated excitement. Arizona, Pennsylvania and the World Bank have entered into contracts with One Concern over the past year. New York City and San Jose, Calif., are in talks with the company. And a Japanese city recently became One Concern’s first overseas client.

But when T.J. McDonald, who works for Seattle’s office of emergency management, reviewed a simulated earthquake on the company’s damage prediction platform, he spotted problems. A popular big-box store was grayed out on the web-based map, meaning there was no analysis of the conditions there, and shoppers and workers who might be in danger would not receive immediate help if rescuers relied on One Concern’s results.

“If that Costco collapses in the middle of the day, there’s going to be a lot of people who are hurt,” he said.

The error? The simulation, the company acknowledged, missed many commercial areas because damage calculations relied largely on residential census data.

One Concern has marketed its products as lifesaving tools for emergency responders after earthquakes, floods and, soon, wildfires. But interviews and documents show the company has often exaggerated its tools’ abilities and has kept outside experts from reviewing its methodology. In addition, some product features are available elsewhere at no charge, and data-hungry insurance companies — whose interests can diverge from those of emergency workers — are among One Concern’s biggest investors and customers.

Some critics even suggest that shortcomings in One Concern’s approach could jeopardize lives….(More)”.

Tackling Climate Change with Machine Learning

Curated on Posted on by Stefaan Verhulst

Paper by David Rolnick et al: “Climate change is one of the greatest challenges facing humanity, and we, as machine learning experts, may wonder how we can help. Here we describe how machine learning can be a powerful tool in reducing greenhouse gas emissions and helping society adapt to a changing climate. From smart grids to disaster management, we identify high impact problems where existing gaps can be filled by machine learning, in collaboration with other fields. Our recommendations encompass exciting research questions as well as promising business opportunities. We call on the machine learning community to join the global effort against climate change….(More)”.

Bringing machine learning to the masses

Curated on Posted on by Stefaan Verhulst

Matthew Hutson at Science: “Artificial intelligence (AI) used to be the specialized domain of data scientists and computer programmers. But companies such as Wolfram Research, which makes Mathematica, are trying to democratize the field, so scientists without AI skills can harness the technology for recognizing patterns in big data. In some cases, they don’t need to code at all. Insights are just a drag-and-drop away. One of the latest systems is software called Ludwig, first made open-source by Uber in February and updated last week. Uber used Ludwig for projects such as predicting food delivery times before releasing it publicly. At least a dozen startups are using it, plus big companies such as Apple, IBM, and Nvidia. And scientists: Tobias Boothe, a biologist at the Max Planck Institute of Molecular Cell Biology and Genetics in Dresden, Germany, uses it to visually distinguish thousands of species of flatworms, a difficult task even for experts. To train Ludwig, he just uploads images and labels….(More)”.

Value in the Age of AI

Curated on Posted on by Stefaan Verhulst

Project Syndicate: “Much has been written about Big Data, artificial intelligence, and automation. The Fourth Industrial Revolution will have far-reaching implications for jobs, ethics, privacy, and equality. But more than that, it will also transform how we think about value – where it comes from, how it is captured, and by whom.

In “Value in the Age of AI,” Project Syndicate, with support from the Dubai Future Foundation, GovLab (New York University), and the Centre for Data & Society (Brussels), will host an ongoing debate about the changing nature of value in the twenty-first century. In the commentaries below, leading thinkers at the intersection of technology, economics, culture, and politics discuss how new technologies are changing our societies, businesses, and individual lived experiences, and what that might mean for our collective future….(More)”.

The effective and ethical development of Artificial Intelligence: an opportunity to improve our wellbeing

Curated on Posted on by Stefaan Verhulst

Paper by Toby Walsh, Neil Levy, Genevieve Bell, Anthony Elliott, James Maclaurin, Iven Mareels, Fiona Woods: “As Artificial Intelligence (AI) becomes more advanced its applications will become increasingly complex and will find their place in homes, work places and cities.

AI offers broad-reaching opportunities, but uptake also carries serious implications for human capital, social inclusion, privacy and cultural values to name a few. These must be considered to pre-empt responsible deployment.

This project examined the potential that Artificial Intelligence (AI) technologies have in enhancing Australia’s wellbeing, lifting the economy, improving environmental sustainability and creating a more equitable, inclusive and fair society. Placing society at the core of AI development, the report analyses the opportunities, challenges and prospects that AI technologies present, and explores considerations such as workforce, education, human rights and our regulatory environment.

Key findings:

  1. AI offers major opportunities to improve our economic, societal and environmental wellbeing, while also presenting potentially significant global risks, including technological unemployment and the use of lethal autonomous weapons. Further development of AI must be directed to allow well-considered implementation that supports our society in becoming what we would like it to be – one centred on improving prosperity, reducing inequity and achieving continued betterment.
  2. Proactive engagement, consultation and ongoing communication with the public about the changes and effects of AI will be essential for building community awareness. Earning public trust will be critical to enable acceptance and uptake of the technology.
  3. The application of AI is growing rapidly. Ensuring its continued safe and appropriate development will be dependent on strong governance and a responsive regulatory system that encourages innovation. It will also be important to engender public confidence that the goods and services driven by AI are at, or above, benchmark standards and preserve the values that society seeks.
  4. AI is enabled by access to data. To support successful implementation of AI, there is a need for effective digital infrastructure, including data centres and structures for data sharing, that makes AI secure, trusted and accessible, particularly for rural and remote populations. If such essential infrastructure is not carefully and appropriately developed, the advancement of AI and the immense benefits it offers will be diminished.
  5. Successful development and implementation of AI will require a broad range of new skills and enhanced capabilities that span the humanities, arts and social sciences (HASS) and science, technology, engineering and mathematics (STEM) disciplines. Building a talent base and establishing an adaptable and skilled workforce for the future will need education programs that start in early childhood and continue throughout working life and a supportive immigration policy.
  6. An independently led AI body that brings stakeholders together from government, academia and the public and private sectors would provide a critical mass of skills and institutional leadership to develop AI technologies, as well as promote engagement with international initiatives and to develop appropriate ethical frameworks….(More)”.

The Hidden Costs of Automated Thinking

Curated on Posted on by Stefaan Verhulst

Jonathan Zittrain in The New Yorker: “Like many medications, the wakefulness drug modafinil, which is marketed under the trade name Provigil, comes with a small, tightly folded paper pamphlet. For the most part, its contents—lists of instructions and precautions, a diagram of the drug’s molecular structure—make for anodyne reading. The subsection called “Mechanism of Action,” however, contains a sentence that might induce sleeplessness by itself: “The mechanism(s) through which modafinil promotes wakefulness is unknown.”

Provigil isn’t uniquely mysterious. Many drugs receive regulatory approval, and are widely prescribed, even though no one knows exactly how they work. This mystery is built into the process of drug discovery, which often proceeds by trial and error. Each year, any number of new substances are tested in cultured cells or animals; the best and safest of those are tried out in people. In some cases, the success of a drug promptly inspires new research that ends up explaining how it works—but not always. Aspirin was discovered in 1897, and yet no one convincingly explained how it worked until 1995. The same phenomenon exists elsewhere in medicine. Deep-brain stimulation involves the implantation of electrodes in the brains of people who suffer from specific movement disorders, such as Parkinson’s disease; it’s been in widespread use for more than twenty years, and some think it should be employed for other purposes, including general cognitive enhancement. No one can say how it works.

This approach to discovery—answers first, explanations later—accrues what I call intellectual debt. It’s possible to discover what works without knowing why it works, and then to put that insight to use immediately, assuming that the underlying mechanism will be figured out later. In some cases, we pay off this intellectual debt quickly. But, in others, we let it compound, relying, for decades, on knowledge that’s not fully known.

In the past, intellectual debt has been confined to a few areas amenable to trial-and-error discovery, such as medicine. But that may be changing, as new techniques in artificial intelligence—specifically, machine learning—increase our collective intellectual credit line. Machine-learning systems work by identifying patterns in oceans of data. Using those patterns, they hazard answers to fuzzy, open-ended questions. Provide a neural network with labelled pictures of cats and other, non-feline objects, and it will learn to distinguish cats from everything else; give it access to medical records, and it can attempt to predict a new hospital patient’s likelihood of dying. And yet, most machine-learning systems don’t uncover causal mechanisms. They are statistical-correlation engines. They can’t explain why they think some patients are more likely to die, because they don’t “think” in any colloquial sense of the word—they only answer. As we begin to integrate their insights into our lives, we will, collectively, begin to rack up more and more intellectual debt….(More)”.

Artificial Intelligence and Law: An Overview

Curated on Posted on by Stefaan Verhulst

Paper by Harry Surden: “Much has been written recently about artificial intelligence (AI) and law. But what is AI, and what is its relation to the practice and administration of law? This article addresses those questions by providing a high-level overview of AI and its use within law. The discussion aims to be nuanced but also understandable to those without a technical background. To that end, I first discuss AI generally. I then turn to AI and how it is being used by lawyers in the practice of law, people and companies who are governed by the law, and government officials who administer the law. A key motivation in writing this article is to provide a realistic, demystified view of AI that is rooted in the actual capabilities of the technology. This is meant to contrast with discussions about AI and law that are decidedly futurist in nature…(More)”.

What Restaurant Reviews Reveal About Cities

Curated on Posted on by Stefaan Verhulst

Linda Poon at CityLab: “Online review sites can tell you a lot about a city’s restaurant scene, and they can reveal a lot about the city itself, too.

Researchers at MIT recently found that information about restaurants gathered from popular review sites can be used to uncover a number of socioeconomic factors of a neighborhood, including its employment rates and demographic profiles of the people who live, work, and travel there.

A report published last week in the Proceedings of the National Academy of Sciences explains how the researchers used information found on Dianping—a Yelp-like site in China—to find information that might usually be gleaned from an official government census. The model could prove especially useful for gathering information about cities that don’t have that kind of reliable or up-to-date government data, especially in developing countries with limited resources to conduct regular surveys….

Zheng and her colleagues tested out their machine-learning model using restaurant data from nine Chinese cities of various sizes—from crowded ones like Beijing, with a population of more than 10 million, to smaller ones like Baoding, a city of fewer than 3 million people.

They pulled data from 630,000 restaurants listed on Dianping, including each business’s location, menu prices, opening day, and customer ratings. Then they ran it through a machine-learning model with official census data and with anonymous location and spending data gathered from cell phones and bank cards. By comparing the information, they were able to determine where the restaurant data reflected the other data they had about neighborhoods’ characteristics.

They found that the local restaurant scene can predict, with 95 percent accuracy, variations in a neighborhood’s daytime and nighttime populations, which are measured using mobile phone data. They can also predict, with 90 and 93 percent accuracy, respectively, the number of businesses and the volume of consumer consumption. The type of cuisines offered and kind of eateries available (coffeeshop vs. traditional teahouses, for example), can also predict the proportion of immigrants or age and income breakdown of residents. The predictions are more accurate for neighborhoods near urban centers as opposed to those near suburbs, and for smaller cities, where neighborhoods don’t vary as widely as those in bigger metropolises….(More)”.

Review into bias in algorithmic decision-making

Curated on Posted on by Stefaan Verhulst

Interim Report by the Centre for Data Ethics and Innovation (UK): The use of algorithms has the potential to improve the quality of decision- making by increasing the speed and accuracy with which decisions are made. If designed well, they can reduce human bias in decision-making processes. However, as the volume and variety of data used to inform decisions increases, and the algorithms used to interpret the data become more complex, concerns are growing that without proper oversight, algorithms risk entrenching and potentially worsening bias.

The way in which decisions are made, the potential biases which they are subject to and the impact these decisions have on individuals are highly context dependent. Our Review focuses on exploring bias in four key sectors: policing, financial services, recruitment and local government. These have been selected because they all involve significant decisions being made about individuals, there is evidence of the growing uptake of machine learning algorithms in the sectors and there is evidence of historic bias in decision-making within these sectors. This Review seeks to answer three sets of questions:

  1. Data: Do organisations and regulators have access to the data they require to adequately identify and mitigate bias?
  2. Tools and techniques: What statistical and technical solutions are available now or will be required in future to identify and mitigate bias and which represent best practice?
  3. Governance: Who should be responsible for governing, auditing and assuring these algorithmic decision-making systems?

Our work to date has led to some emerging insights that respond to these three sets of questions and will guide our subsequent work….(More)”.