How the Math Men Overthrew the Mad Men

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 in the New Yorker: “Once, Mad Men ruled advertising. They’ve now been eclipsed by Math Men—the engineers and data scientists whose province is machines, algorithms, pureed data, and artificial intelligence. Yet Math Men are beleaguered, as Mark Zuckerberg demonstrated when he humbled himself before Congress, in April. Math Men’s adoration of data—coupled with their truculence and an arrogant conviction that their “science” is nearly flawless—has aroused government anger, much as Microsoft did two decades ago.

The power of Math Men is awesome. Google and Facebook each has a market value exceeding the combined value of the six largest advertising and marketing holding companies. Together, they claim six out of every ten dollars spent on digital advertising, and nine out of ten new digital ad dollars. They have become more dominant in what is estimated to be an up to two-trillion-dollar annual global advertising and marketing business. Facebook alone generates more ad dollars than all of America’s newspapers, and Google has twice the ad revenues of Facebook.

In the advertising world, Big Data is the Holy Grail, because it enables marketers to target messages to individuals rather than general groups, creating what’s called addressable advertising. And only the digital giants possess state-of-the-art Big Data. “The game is no longer about sending you a mail order catalogue or even about targeting online advertising,” Shoshana Zuboff, a professor of business administration at the Harvard Business School, wrote on faz.net, in 2016. “The game is selling access to the real-time flow of your daily life—your reality—in order to directly influence and modify your behavior for profit.” Success at this “game” flows to those with the “ability to predict the future—specifically the future of behavior,” Zuboff writes. She dubs this “surveillance capitalism.”

However, to thrash just Facebook and Google is to miss the larger truth: everyone in advertising strives to eliminate risk by perfecting targeting data. Protecting privacy is not foremost among the concerns of marketers; protecting and expanding their business is. The business model adopted by ad agencies and their clients parallels Facebook and Google’s. Each aims to massage data to better identify potential customers. Each aims to influence consumer behavior. To appreciate how alike their aims are, sit in an agency or client marketing meeting and you will hear wails about Facebook and Google’s “walled garden,” their unwillingness to share data on their users. When Facebook or Google counter that they must protect “the privacy” of their users, advertisers cry foul: You’re using the data to target ads we paid for—why won’t you share it, so that we can use it in other ad campaigns?…(More)”

AI trust and AI fears: A media debate that could divide society

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Article by Vyacheslav Polonski: “Unless you live under a rock, you probably have been inundated with recent news on machine learning and artificial intelligence (AI). With all the recent breakthroughs, it almost seems like AI can already predict the future. Police forces are using it to map when and where crime is likely to occur. Doctors can use it to predict when a patient is most likely to have a heart attack or stroke. Researchers are even trying to give AI imagination so it can plan for unexpected consequences.

Of course, many decisions in our lives require a good forecast, and AI agents are almost always better at forecasting than their human counterparts. Yet for all these technological advances, we still seem to deeply lack confidence in AI predictions. Recent cases show that people don’t like relying on AI and prefer to trust human experts, even if these experts are wrong.

If we want AI to really benefit people, we need to find a way to get people to trust it. To do that, we need to understand why people are so reluctant to trust AI in the first place….

Many people are also simply not familiar with many instances of AI actually working, because it often happens in the background. Instead, they are acutely aware of instances where AI goes terribly wrong:

These unfortunate examples have received a disproportionate amount of media attention, emphasising the message that humans cannot always rely on technology. In the end, it all goes back to the simple truth that machine learning is not foolproof, in part because the humans who design it aren’t….

Fortunately we already have some ideas about how to improve trust in AI — there’s light at the end of the tunnel.

  1. Experience: One solution may be to provide more hands-on experiences with automation apps and other AI applications in everyday situations (like this robot that can get you a beer from the fridge). Thus, instead of presenting the Sony’s new robot dog Aibo as an exclusive product for the upper-class, we’d recommend making these kinds of innovations more accessible to the masses. Simply having previous experience with AI can significantly improve people’s attitudes towards the technology, as we found in our experimental study. And this is especially important for the general public that may not have a very sophisticated understanding of the technology. Similar evidence also suggests the more you use other technologies such as the Internet, the more you trust them.
  2. Insight: Another solution may be to open the “black-box” of machine learning algorithms and be slightly more transparent about how they work. Companies such as Google, Airbnb and Twitter already release transparency reports on a regular basis. These reports provide information about government requests and surveillance disclosures. A similar practice for AI systems could help people have a better understanding of how algorithmic decisions are made. Therefore, providing people with a top-level understanding of machine learning systems could go a long way towards alleviating algorithmic aversion.
  3. Control: Lastly, creating more of a collaborative decision-making process will help build trust and allow the AI to learn from human experience. In our work at Avantgarde Analytics, we have also found that involving people more in the AI decision-making process could improve trust and transparency. In a similar vein, a group of researchers at the University of Pennsylvania recently found that giving people control over algorithms can help create more trust in AI predictions. Volunteers in their study who were given the freedom to slightly modify an algorithm felt more satisfied with it, more likely to believe it was superior and more likely to use in in the future.

These guidelines (experience, insight and control) could help making AI systems more transparent and comprehensible to the individuals affected by their decisions….(More)”.

Open data work: understanding open data usage from a practice lens

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Paper by Emma Ruijer in the International Review of Administrative Sciences: “During recent years, the amount of data released on platforms by public administrations around the world have exploded. Open government data platforms are aimed at enhancing transparency and participation. Even though the promises of these platforms are high, their full potential has not yet been reached. Scholars have identified technical and quality barriers of open data usage. Although useful, these issues fail to acknowledge that the meaning of open data also depends on the context and people involved. In this study we analyze open data usage from a practice lens – as a social construction that emerges over time in interaction with governments and users in a specific context – to enhance our understanding of the role of context and agency in the development of open data platforms. This study is based on innovative action-based research in which civil servants’ and citizens’ initiatives collaborate to find solutions for public problems using an open data platform. It provides an insider perspective of Open Data Work. The findings show that an absence of a shared cognitive framework for understanding open data and a lack of high-quality datasets can prevent processes of collaborative learning. Our contextual approach stresses the need for open data practices that work on the basis of rich interactions with users rather than government-centric implementations….(More)”.

Crowdbreaks: Tracking Health Trends using Public Social Media Data and Crowdsourcing

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Paper by Martin Mueller and Marcel Salath: “In the past decade, tracking health trends using social media data has shown great promise, due to a powerful combination of massive adoption of social media around the world, and increasingly potent hardware and software that enables us to work with these new big data streams.

At the same time, many challenging problems have been identified. First, there is often a mismatch between how rapidly online data can change, and how rapidly algorithms are updated, which means that there is limited reusability for algorithms trained on past data as their performance decreases over time. Second, much of the work is focusing on specific issues during a specific past period in time, even though public health institutions would need flexible tools to assess multiple evolving situations in real time. Third, most tools providing such capabilities are proprietary systems with little algorithmic or data transparency, and thus little buy-in from the global public health and research community.

Here, we introduce Crowdbreaks, an open platform which allows tracking of health trends by making use of continuous crowdsourced labelling of public social media content. The system is built in a way which automatizes the typical workflow from data collection, filtering, labelling and training of machine learning classifiers and therefore can greatly accelerate the research process in the public health domain. This work introduces the technical aspects of the platform and explores its future use cases…(More)”.

How the Enlightenment Ends

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Henry Kissinger in the Atlantic: “…Heretofore, the technological advance that most altered the course of modern history was the invention of the printing press in the 15th century, which allowed the search for empirical knowledge to supplant liturgical doctrine, and the Age of Reason to gradually supersede the Age of Religion. Individual insight and scientific knowledge replaced faith as the principal criterion of human consciousness. Information was stored and systematized in expanding libraries. The Age of Reason originated the thoughts and actions that shaped the contemporary world order.

But that order is now in upheaval amid a new, even more sweeping technological revolution whose consequences we have failed to fully reckon with, and whose culmination may be a world relying on machines powered by data and algorithms and ungoverned by ethical or philosophical norms.

he internet age in which we already live prefigures some of the questions and issues that AI will only make more acute. The Enlightenment sought to submit traditional verities to a liberated, analytic human reason. The internet’s purpose is to ratify knowledge through the accumulation and manipulation of ever expanding data. Human cognition loses its personal character. Individuals turn into data, and data become regnant.

Users of the internet emphasize retrieving and manipulating information over contextualizing or conceptualizing its meaning. They rarely interrogate history or philosophy; as a rule, they demand information relevant to their immediate practical needs. In the process, search-engine algorithms acquire the capacity to predict the preferences of individual clients, enabling the algorithms to personalize results and make them available to other parties for political or commercial purposes. Truth becomes relative. Information threatens to overwhelm wisdom.

Inundated via social media with the opinions of multitudes, users are diverted from introspection; in truth many technophiles use the internet to avoid the solitude they dread. All of these pressures weaken the fortitude required to develop and sustain convictions that can be implemented only by traveling a lonely road, which is the essence of creativity.

The impact of internet technology on politics is particularly pronounced. The ability to target micro-groups has broken up the previous consensus on priorities by permitting a focus on specialized purposes or grievances. Political leaders, overwhelmed by niche pressures, are deprived of time to think or reflect on context, contracting the space available for them to develop vision.
The digital world’s emphasis on speed inhibits reflection; its incentive empowers the radical over the thoughtful; its values are shaped by subgroup consensus, not by introspection. For all its achievements, it runs the risk of turning on itself as its impositions overwhelm its conveniences….

There are three areas of special concern:

First, that AI may achieve unintended results….

Second, that in achieving intended goals, AI may change human thought processes and human values….

Third, that AI may reach intended goals, but be unable to explain the rationale for its conclusions…..(More)”

Behavioral economics from nuts to ‘nudges’

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Richard Thaler at ChicagoBoothReview: “…Behavioral economics has come a long way from my initial set of stories. Behavioral economists of the current generation are using all the modern tools of economics, from theory to big data to structural models to neuroscience, and they are applying those tools to most of the domains in which economists practice their craft. This is crucial to making descriptive economics more accurate. As the last section of this lecture highlighted, they are also influencing public-policy makers around the world, with those in the private sector not far behind. Sunstein and I did not invent nudging—we just gave it a word. People have been nudging as long as they have been trying to influence other people.

And much as we might wish it to be so, not all nudging is nudging for good. The same passive behavior we saw among Swedish savers applies to nearly everyone agreeing to software terms, or mortgage documents, or car payments, or employment contracts. We click “agree” without reading, and can find ourselves locked into a long-term contract that can only be terminated with considerable time and aggravation, or worse. Some firms are actively making use of behaviorally informed strategies to profit from the lack of scrutiny most shoppers apply. I call this kind of exploitive behavior “sludge.” It is the exact opposite of nudging for good. But whether the use of sludge is a long-term profit-maximizing strategy remains to be seen. Creating the reputation as a sludge-free supplier of goods and services may be a winning long-term strategy, just like delivering free bottles of water to victims of a hurricane.

Although not every application of behavioral economics will make the world a better place, I believe that giving economics a more human dimension and creating theories that apply to humans, not just econs, will make our discipline stronger, more useful, and undoubtedly more accurate….(More)”.

Data Violence and How Bad Engineering Choices Can Damage Society

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Blog by Anna Lauren Hoffmann: “…In 2015, a black developer in New York discovered that Google’s algorithmic photo recognition software had tagged pictures of him and his friends as gorillas.

The same year, Facebook auto-suspended Native Americans for using their real names, and in 2016, facial recognition was found to struggle to read black faces.

Software in airport body scanners has flagged transgender bodies as threatsfor years. In 2017, Google Translate took gender-neutral pronouns in Turkish and converted them to gendered pronouns in English — with startlingly biased results.

“Violence” might seem like a dramatic way to talk about these accidents of engineering and the processes of gathering data and using algorithms to interpret it. Yet just like physical violence in the real world, this kind of “data violence” (a term inspired by Dean Spade’s concept of administrative violence) occurs as the result of choices that implicitly and explicitly lead to harmful or even fatal outcomes.

Those choices are built on assumptions and prejudices about people, intimately weaving them into processes and results that reinforce biases and, worse, make them seem natural or given.

Take the experience of being a woman and having to constantly push back against rigid stereotypes and aggressive objectification.

Writer and novelist Kate Zambreno describes these biases as “ghosts,” a violent haunting of our true reality. “A return to these old roles that we play, that we didn’t even originate. All the ghosts of the past. Ghosts that aren’t even our ghosts.”

Structural bias is reinforced by the stereotypes fed to us in novels, films, and a pervasive cultural narrative that shapes the lives of real women every day, Zambreno describes. This extends to data and automated systems that now mediate our lives as well. Our viewing and shopping habits, our health and fitness tracking, our financial information all conspire to create a “data double” of ourselves, produced about us by third parties and standing in for us on data-driven systems and platforms.

These fabrications don’t emerge de novo, disconnected from history or social context. Rather, they often pick up and unwittingly spit out a tangled mess of historical conditions and current realities.

Search engines are a prime example of how data and algorithms can conspire to amplify racist and sexist biases. The academic Safiya Umoja Noble threw these messy entanglements into sharp relief in her book Algorithms of Oppression. Google Search, she explains, has a history of offering up pages of porn for women from particular racial or ethnic groups, and especially black women. Google have also served up ads for criminal background checksalongside search results for African American–sounding names, as former Federal Trade Commission CTO Latanya Sweeney discovered.

“These search engine results for women whose identities are already maligned in the media, such as Black women and girls, only further debase and erode efforts for social, political, and economic recognition and justice,” Noble says.

These kinds of cultural harms go well beyond search results. Sociologist Rena Bivens has shown how the gender categories employed by platforms like Facebook can inflict symbolic violences against transgender and nonbinary users in ways that may never be made obvious to users….(More)”.

Data Stewards: Data Leadership to Address the Challenges of the 21st Century

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Data Stewards_screenshot

The GovLab at the NYU Tandon School of Engineering is pleased to announce the launch of its Data Stewards website — a new portal for connecting organizations across sectors that seek to promote responsible data leadership that can address the challenges of the 21st century — developed with generous support from the William and Flora Hewlett Foundation.

Increasingly, the private sector is collaborating with the public sector and researchers on ways to use private-sector data and analytical expertise for public good. With these new practices of data collaborations come the need to reimagine roles and responsibilities to steer the process of using this data, and the insights it can generate, to address society’s biggest questions and challenges: Data Stewards.

Today, establishing and sustaining these new collaborative and accountable approaches requires significant and time-consuming effort and investment of resources for both data holders on the supply side, and institutions that represent the demand. By establishing Data Stewardship as a function — recognized within the private sector as a valued responsibility — the practice of Data Collaboratives can become more predictable, scaleable, sustainable and de-risked.

Together with BrightFront Group and Adapt, we are:

  • Exploring the needs and priorities of current private sector Data Stewards who act as change agents within their firms. Responsible for determining what, when, how and with whom to share private data for public good, these individuals are critical catalysts for ensuring insights are turned into action.
  • Identifying and connecting existing Data Stewards across sectors and regions to create an online and in-person community for exchanging knowledge and best practices.
  • Developing methodologies, tools and frameworks to use data more responsibly, systematically and efficiently to decrease the transaction cost, time and energy currently needed to establish Data Collaboratives.

To learn more about the Data Stewards Initiative, including new insights, ideas, tools and information about the Data Steward of the Year Award program, visit datastewards.net.

If you are a Data Steward, or would like to join a community of practice to learn from your peers, please contact [email protected] to join the Network of Data Stewards.

For more information about The GovLab, visit thegovlab.org.

Using Blockchain Technology to Create Positive Social Impact

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Randall Minas in Healthcare Informatics: “…Healthcare is yet another area where blockchain can make a substantial impact. Blockchain technology could be used to enable the WHO and CDC to better monitor disease outbreaks over time by creating distributed “ledgers” that are both secure and updated hundreds of times per day. Issued in near real-time, these updates would alert healthcare professionals to spikes in local cases almost immediately. Additionally, using blockchain would allow accurate diagnosis and streamline the isolation of clusters of cases as quickly as possible. Providing blocks of real-time disease information—especially in urban areas—would be invaluable.

In the United States, disease updates are provided in a Morbidity and Mortality Weekly Report (MMWR) from the CDC. This weekly report provides tables of current disease trends for hospitals and public health officials. Another disease reporting mechanism is the National Outbreak Reporting System (NORS), launched in 2009. NORS’ web-based tool provides outbreak data through 2016 and is accessible to the general public. There are two current weaknesses in the NORS reporting system and both can be addressed by blockchain technology.

The first issue lies in the number of steps required to accurately report each outbreak. A health department reports an outbreak to the NORS system, the CDC checks it for accuracy, analyzes the data, then provides a summary via the MMRW. Instantiating blockchain as the technology through which the NORS data is reported, every health department in the country could have preliminary data on disease trends at their fingertips without having to wait for the next MMRW publication.

The second issue is the inherent cybersecurity vulnerabilities using a web-based platform to monitor disease reporting. As we have seen with cyberattacks both domestic and abroad, cybersecurity vulnerabilities underlie most of our modern-day computing infrastructure. Blockchain was designed to be secure because it is decentralized across many computer networks and, since it was designed as a digital ledger, the previous data (or “blocks”) in the blockchain are difficult to alter.

While the NORS platform could be hacked with malware to gain access to our electricity and water infrastructure, instituting blockchain technology would limit the potential damage of the malware based on the inherent security of the technology. If this does not sound important, imagine the damage and ensuing panic that could be caused by a compromised NORS reporting a widespread Ebola outbreak.

The use of blockchain in monitoring epidemic outbreaks might not only apply to fast-spreading outbreaks like the flu, but also to epidemics that have lasted for decades. Since blockchain allows an unchangeable snapshot of data over time and can be anonymous, partner organizations could provide HIV test results to an individual’s “digital ledger” with a date of the test and the results.

Individuals could then exchange their HIV status securely, in an application, before engaging in high-risk behaviors. Since many municipalities provide free or low-cost, anonymous HIV testing, the use of blockchain would allow disease monitoring and exchange of status in a secure and trusted manner. The LGBTQ community and other high-risk communities could use an application to securely exchange HIV status with potential partners. With widespread adoption of this status-exchange system, an individual’s high-risk exposure could be limited, further reducing the spread of the epidemic.

While much of the creative application around blockchain has focused on supply chain-like models, including distribution of renewable energy and local sourcing of goods, it is important also to think innovatively about how blockchain can be used outside of supply chain and accounting.

In healthcare, blockchain has been discussed frequently in relation to electronic health records (EHRs), yet even that could be underappreciating the technology’s potential. Leaders in the blockchain arena should invest in application development for epidemic monitoring and disease control using blockchain technology. …(More)”.

Why Policymakers Should Care About “Big Data” in Healthcare

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David W.Bates et al at Health Policy and Technology: “The term “big data” has gotten increasing popular attention, and there is growing focus on how such data can be used to measure and improve health and healthcare. Analytic techniques for extracting information from these data have grown vastly more powerful, and they are now broadly available. But for these approaches to be most useful, large amounts of data must be available, and barriers to use should be low. We discuss how “smart cities” are beginning to invest in this area to improve the health of their populations; provide examples around model approaches for making large quantities of data available to researchers and clinicians among other stakeholders; discuss the current state of big data approaches to improve clinical care including specific examples, and then discuss some of the policy issues around and examples of successful regulatory approaches, including deidentification and privacy protection….(More)”.