Systems change and philanthropy

Curated on Posted on by Stefaan Verhulst

Introduction by Julian Corner to Special Issue of Alliance: “This special feature explores a growing aspiration in philanthropy to achieve system-level change. It looks at the potential and pitfalls by profiling a number of approaches adopted by different foundations….

While the fortunes of systems thinking have ebbed and flowed over the decades, it has mainly occurred on the margins of organisations. This time something different seems to be happening, at least in terms of philanthropy. A number of major foundations are embracing systems approaches as a core methodology. How should we understand this?…

I detect at least four broad approaches or attitudes to systems in foundations’ work, all of which have been at play in Lankelly Chase’s work at different points:

1.The system as a unit of intervention
Many foundations are trying to take in a broader canvas, recognising that both problems and solutions are generated by the interplay of multiple variables. They hope to find leverage points among these variables, so that their investment can unlock so-called system-level change. Some of their strategies include: working for policy changes, scaling disruptive innovations, supporting advocacy for people’s rights, and improving the evidence base used by system actors. These approaches seem to work best when there is common agreement on an identifiable system, such as the criminal justice system, which can be mapped and acted on.

2.Messy contested systems
Some foundations find they are drawn deeper into complexity. They unearth conflicting perspectives on the nature of the problem, especially when there is a power inequality between those defining it and those experiencing it. As greater interconnection emerges, the frame put around the canvas is shown to be arbitrary and the hope of identifying leverage points begins to look reductive. One person’s solution turns out to be another’s problem. Unable to predict how change might occur, foundations shift towards more exploratory and inquiring approaches. Rather than funding programmes or institutions, they seek to influence the conditions of change, focusing on collaborations, place-based approaches, collective impact, amplifying lesser heard voices, building skills and capacities, and reframing the narratives people hold.

3.Seeing yourself in the system
As appreciation of interconnection deepens, the way foundations earn money, how they make decisions, the people they choose to include in (and exclude from) their work, how they specify success, all come into play as parts of the system that need to change. These foundations realise that they aren’t just looking at a canvas, they are part of it. At Lankelly Chase, we now view our position as fundamentally paradoxical, given that we are seeking to tackle inequality by holding accumulated wealth. We have sought to model the behaviours of healthier systems, including delegated decision-making, mutual accountability, trust-based relationships, promoting equality of voice. By aiming for congruence between means and ends, we and our peers contend that effective practice and ethical practice become the same.

4.Beyond systems
There comes a point when the idea of systems itself can feel reductive. Different values are invoked, those of kindness and solidarity. The basis on which humans relate to each other becomes the core concern. Inspiration is sought in other histories and forms of spiritualty, as suppressed narratives are surfaced. The frame of philanthropy itself is no longer a given, with mutuality and even reparation becoming the basis of an alternative paradigm.

….Foundations can be viewed as both ‘of’ and ‘outside’ any system. This is a tension that isn’t resolvable, but if handled with sufficient self-awareness could make foundations powerful systems practitioners….(More)”.


Seeing and Being Seen

Curated on Posted on by Stefaan Verhulst

Russell C. Bogue in The Hedgehog Review: “On May 20, 2013, a pale, nervous American landed in Hong Kong and made his way to the Mira Hotel. Once there, he met with reporters from The Guardian and the Washington Post and turned over thousands of documents his high-level security clearance had enabled him to acquire while working as a contractor for the National Security Agency. Soon after this exchange, the world learned about PRISM, a top-secret NSA program that granted (court-ordered) direct access to Facebook, Apple, Google, and other US Internet giants, including users’ search histories, e-mails, file transfers, and live chats.1 Additionally, Verizon had been providing information to the NSA on an “ongoing, daily basis” about customers’ telephone calls, including location data and call duration (although not the content of conversations).2 Everyone, in short, was being monitored. Glenn Greenwald, one of the first journalists to meet with Edward Snowden, and one of his most vocal supporters, wrote later that “the NSA is collecting all forms of electronic communications between Americans…and thereby attempting by definition to destroy any remnants of privacy both in the US and globally.”3

According to a 2014 Pew Research Center poll, fully 91 percent of Americans believe they have lost control over their personal information.4 What is such a public to do? Anxious computer owners have taken to covering their devices’ built-in cameras with bits of tape.5Messaging services tout their end-to-end encryption.6 Researchers from Harvard Business School have started investigating the effectiveness of those creepy online ads that seem to know a little too much about your preferences.7

For some, this pushback has come far too late to be of any use. In a recent article in The Atlantic depressingly titled “Welcome to the Age of Privacy Nihilism,” Ian Bogost observes that we have already become unduly reliant on services that ask us to relinquish personal data in exchange for convenience. To reassert control over one’s privacy, one would have to abstain from credit card activity and use the Internet only sparingly. The worst part? We don’t get the simple pleasure of blaming this state of affairs on Big Government or the tech giants. Instead, our enemy is, as Bogost intones, “a hazy murk, a chilling, Lovecraftian murmur that can’t be seen, let alone touched, let alone vanquished.”8

The enemy may be a bit closer to home, however. While we fear being surveilled, recorded, and watched, especially when we are unaware, we also compulsively expose ourselves to others….(More)”.

Is Ethical A.I. Even Possible?

Curated on Posted on by Stefaan Verhulst

Cade Metz at The New York Times: ” When a news article revealed that Clarifaiwas working with the Pentagon and some employees questioned the ethics of building artificial intelligence that analyzed video captured by drones, the company said the project would save the lives of civilians and soldiers.

“Clarifai’s mission is to accelerate the progress of humanity with continually improving A.I.,” read a blog post from Matt Zeiler, the company’s founder and chief executive, and a prominent A.I. researcher. Later, in a news media interview, Mr. Zeiler announced a new management position that would ensure all company projects were ethically sound.

As activists, researchers, and journalists voice concerns over the rise of artificial intelligence, warning against biased, deceptive and malicious applications, the companies building this technology are responding. From tech giants like Google and Microsoft to scrappy A.I. start-ups, many are creating corporate principles meant to ensure their systems are designed and deployed in an ethical way. Some set up ethics officers or review boards to oversee these principles.

But tensions continue to rise as some question whether these promises will ultimately be kept. Companies can change course. Idealism can bow to financial pressure. Some activists — and even some companies — are beginning to argue that the only way to ensure ethical practices is through government regulation....

As companies and governments deploy these A.I. technologies, researchers are also realizing that some systems are woefully biased. Facial recognition services, for instance, can be significantly less accurate when trying to identify women or someone with darker skin. Other systems may include security holes unlike any seen in the past. Researchers have shown that driverless cars can be fooled into seeing things that are not really there.

All this means that building ethical artificial intelligence is an enormously complex task. It gets even harder when stakeholders realize that ethics are in the eye of the beholder.

As some Microsoft employees protest the company’s military contracts, Mr. Smith said that American tech companies had long supported the military and that they must continue to do so. “The U.S. military is charged with protecting the freedoms of this country,” he told the conference. “We have to stand by the people who are risking their lives.”

Though some Clarifai employees draw an ethical line at autonomous weapons, others do not. Mr. Zeiler argued that autonomous weapons will ultimately save lives because they would be more accurate than weapons controlled by human operators. “A.I. is an essential tool in helping weapons become more accurate, reducing collateral damage, minimizing civilian casualties and friendly fire incidents,” he said in a statement.

Google worked on the same Pentagon project as Clarifai, and after a protest from company employees, the tech giant ultimately ended its involvement. But like Clarifai, as many as 20 other companies have worked on the project without bowing to ethical concerns.

After the controversy over its Pentagon work, Google laid down a set of “A.I. principles” meant as a guide for future projects. But even with the corporate rules in place, some employees left the company in protest. The new principles are open to interpretation. And they are overseen by executives who must also protect the company’s financial interests….

In their open letter, the Clarifai employees said they were unsure whether regulation was the answer to the many ethical questions swirling around A.I. technology, arguing that the immediate responsibility rested with the company itself….(More)”.

Can Data Save U.N. Peacekeeping?

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Adam Day at World Policy Review: “Does international peacekeeping protect civilians caught up in civil wars? Do the 16,000 United Nations peacekeepers deployed in the Democratic Republic of the Congo actually save lives, and if so how many? Did the 9,000 patrols conducted by the U.N. Mission in South Sudan in the past three months protect civilians there? 

The answer is a dissatisfying “maybe.” Without a convincing story of saving lives, the U.N. is open to attacks by the likes of White House national security adviser John Bolton, who call peacekeeping “unproductive” and push for further cuts to the organization’s already diminished budget. But peacekeeping can—and must—make a case for its own utility, using data already at its fingertips. …(More)”.

Whose Rules? The Quest for Digital Standards

Curated on Posted on by Stefaan Verhulst

Stephanie Segal at CSIS: “Prime Minister Shinzo Abe of Japan made news at the World Economic Forum in Davos last month when he announced Japan’s aspiration to make the G20 summit in Osaka a launch pad for “world-wide data governance.” This is not the first time in recent memory that Japan has taken a leadership role on an issue of keen economic importance. Most notably, the Trans-Pacific Partnership (TPP) lives on as the Comprehensive and Progressive Agreement on Trans-Pacific Partnership (CPTPP), thanks in large part to Japan’s efforts to keep the trading bloc together after President Trump announced U.S. withdrawal from the TPP. But it’s in the area of data and digital governance that Japan’s efforts will perhaps be most consequential for future economic growth.

Data has famously been called “the new oil” in the global economy. A 2016 report by the McKinsey Global Institute estimated that global data flows contributed $2.8 trillion in value to the global economy back in 2014, while cross-border data flows and digital trade continue to be key drivers of global trade and economic growth. Japan’s focus on data and digital governance is therefore consistent with its recent efforts to support global growth, deepen global trade linkages, and advance regional and global standards.

Data governance refers to the rules directing the collection, processing, storage, and use of data. The proliferation of smart devices and the emergence of a data-driven Internet of Things portends an exponential growth in digital data. At the same time, recent reporting on overly aggressive commercial practices of personal data collection, as well as the separate topic of illegal data breaches, have elevated public awareness and interest in the laws and policies that govern the treatment of data, and personal data in particular. Finally, a growing appreciation of data’s central role in driving innovation and future technological and economic leadership is generating concern in many capitals that different data and digital governance standards and regimes will convey a competitive (dis)advantage to certain countries.

Bringing these various threads together—the inevitable explosion of digital data; the need to protect an individual’s right to privacy; and the appreciation that data has economic value and conveys economic advantage—is precisely why Japan’s initiative is both timely and likely to face significant challenges….(More)”.

The privacy threat posed by detailed census data

Curated on Posted on by Stefaan Verhulst

Gillian Tett at the Financial Times: “Wilbur Ross suffered the political equivalent of a small(ish) black eye last month: a federal judge blocked the US commerce secretary’s attempts to insert a question about citizenship into the 2020 census and accused him of committing “egregious” legal violations.

The Supreme Court has agreed to hear the administration’s appeal in April. But while this high-profile fight unfolds, there is a second, less noticed, census issue about data privacy emerging that could have big implications for businesses (and citizens). Last weekend John Abowd, the Census Bureau’s chief scientist, told an academic gathering that statisticians had uncovered shortcomings in the protection of personal data in past censuses. There is no public evidence that anyone has actually used these weaknesses to hack records, and Mr Abowd insisted that the bureau is using cutting-edge tools to fight back. But, if nothing else, this revelation shows the mounting problem around data privacy. Or, as Mr Abowd, noted: “These developments are sobering to everyone.” These flaws are “not just a challenge for statistical agencies or internet giants,” he added, but affect any institution engaged in internet commerce and “bioinformatics”, as well as commercial lenders and non-profit survey groups. Bluntly, this includes most companies and banks.

The crucial problem revolves around what is known as “re-identification” risk. When companies and government institutions amass sensitive information about individuals, they typically protect privacy in two ways: they hide the full data set from outside eyes or they release it in an “anonymous” manner, stripped of identifying details. The census bureau does both: it is required by law to publish detailed data and protect confidentiality. Since 1990, it has tried to resolve these contradictory mandates by using “household-level swapping” — moving some households from one geographic location to another to generate enough uncertainty to prevent re-identification. This used to work. But today there are so many commercially-available data sets and computers are so powerful that it is possible to re-identify “anonymous” data by combining data sets. …

Thankfully, statisticians think there is a solution. The Census Bureau now plans to use a technique known as “differential privacy” which would introduce “noise” into the public statistics, using complex algorithms. This technique is expected to create just enough statistical fog to protect personal confidentiality in published data — while also preserving information in an encrypted form that statisticians can later unscramble, as needed. Companies such as Google, Microsoft and Apple have already used variants of this technique for several years, seemingly successfully. However, nobody has employed this system on the scale that the Census Bureau needs — or in relation to such a high stakes event. And the idea has sparked some controversy because some statisticians fear that even “differential privacy” tools can be hacked — and others fret it makes data too “noisy” to be useful….(More)”.

Tomorrow’s Data Heroes

Curated on Posted on by Stefaan Verhulst

Article by Florian Gröne, Pierre Péladeau, and Rawia Abdel Samad: “Telecom companies are struggling to find a profitable identity in today’s digital sphere. What about helping customers control their information?…

By 2025, Alex had had enough. There no longer seemed to be any distinction between her analog and digital lives. Everywhere she went, every purchase she completed, and just about every move she made, from exercising at the gym to idly surfing the Web, triggered a vast flow of data. That in turn meant she was bombarded with personalized advertising messages, targeted more and more eerily to her. As she walked down the street, messages appeared on her phone about the stores she was passing. Ads popped up on her all-purpose tablet–computer–phone pushing drugs for minor health problems she didn’t know she had — until the symptoms appeared the next day. Worse, she had recently learned that she was being reassigned at work. An AI machine had mastered her current job by analyzing her use of the firm’s productivity software.

It was as if the algorithms of global companies knew more about her than she knew herself — and they probably did. How was it that her every action and conversation, even her thoughts, added to the store of data held about her? After all, it was her data: her preferences, dislikes, interests, friendships, consumer choices, activities, and whereabouts — her very identity — that was being collected, analyzed, profited from, and even used to manage her. All these companies seemed to be making money buying and selling this information. Why shouldn’t she gain some control over the data she generated, and maybe earn some cash by selling it to the companies that had long collected it free of charge?

So Alex signed up for the “personal data manager,” a new service that promised to give her control over her privacy and identity. It was offered by her U.S.-based connectivity company (in this article, we’ll call it DigiLife, but it could be one of many former telephone companies providing Internet services in 2025). During the previous few years, DigiLife had transformed itself into a connectivity hub: a platform that made it easier for customers to join, manage, and track interactions with media and software entities across the online world. Thanks to recently passed laws regarding digital identity and data management, including the “right to be forgotten,” the DigiLife data manager was more than window dressing. It laid out easy-to-follow choices that all Web-based service providers were required by law to honor….

Today, in 2019, personal data management applications like the one Alex used exist only in nascent form, and consumers have yet to demonstrate that they trust these services. Nor can they yet profit by selling their data. But the need is great, and so is the opportunity for companies that fulfill it. By 2025, the total value of the data economy as currently structured will rise to more than US$400 billion, and by monetizing the vast amounts of data they produce, consumers can potentially recapture as much as a quarter of that total.

Given the critical role of telecom operating companies within the digital economy — the central position of their data networks, their networking capabilities, their customer relationships, and their experience in government affairs — they are in a good position to seize this business opportunity. They might not do it alone; they are likely to form consortia with software companies or other digital partners. Nonetheless, for legacy connectivity companies, providing this type of service may be the most sustainable business option. It may also be the best option for the rest of us, as we try to maintain control in a digital world flooded with our personal data….(More)”.

Responsible AI for conservation

Curated on Posted on by Stefaan Verhulst

Oliver Wearn, RobinFreeman and David Jacoby in Nature: “Machine learning (ML) is revolutionizing efforts to conserve nature. ML algorithms are being applied to predict the extinction risk of thousands of species, assess the global footprint of fisheries, and identify animals and humans in wildlife sensor data recorded in the field. These efforts have recently been given a huge boost with support from the commercial sector. New initiatives, such as Microsoft’s AI for Earth and Google’s AI for Social Good, are bringing new resources and new ML tools to bear on some of the biggest challenges in conservation. In parallel to this, the open data revolution means that global-scale, conservation-relevant datasets can be fed directly to ML algorithms from open data repositories, such as Google Earth Engine for satellite data or Movebank for animal tracking data. Added to these will be Wildlife Insights, a Google-supported platform for hosting and analysing wildlife sensor data that launches this year. With new tools and a proliferation of data comes a bounty of new opportunities, but also new responsibilities….(More)”

Weather Service prepares to launch prediction model many forecasters don’t trust

Curated on Posted on by Stefaan Verhulst

Jason Samenow in the Washington Post: “In a month, the National Weather Service plans to launch its “next generation” weather prediction model with the aim of “better, more timely forecasts.” But many meteorologists familiar with the model fear it is unreliable.

The introduction of a model that forecasters lack confidence in matters, considering the enormous impact that weather has on the economy, valued at around $485 billion annually.

The Weather Service announced Wednesday that the model, known as the GFS-FV3 (FV3 stands for Finite­ Volume Cubed-Sphere dynamical core), is “tentatively” set to become the United States’ primary forecast model on March 20, pending tests. It is an update to the current version of the GFS (Global Forecast System), popularly known as the American model, which has existed in various forms for more than 30 years….

A concern is that if forecasters cannot rely on the FV3, they will be left to rely only on the European model for their predictions without a credible alternative for comparisons. And they’ll also have to pay large fees for the European model data. Whereas model data from the Weather Service is free, the European Center for Medium-Range Weather Forecasts, which produces the European model, charges for access.

But there is an alternative perspective, which is that forecasters will just need to adjust to the new model and learn to account for its biases. That is, a little short-term pain is worth the long-term potential benefits as the model improves….

The Weather Service’s parent agency, the National Oceanic and Atmospheric Administration, recently entered an agreement with the National Center for Atmospheric Research to increase collaboration between forecasters and researchers in improving forecast modeling.

In addition, President Trump recently signed into law the Weather Research and Forecast Innovation Act Reauthorization, which establishes the NOAA Earth Prediction Innovation Center, aimed at further enhancing prediction capabilities. But even while NOAA develops relationships and infrastructure to improve the Weather Service’s modeling, the question remains whether the FV3 can meet the forecasting needs of the moment. Until the problems identified are addressed, its introduction could represent a step back in U.S. weather prediction despite a well-intended effort to leap forward….(More).

Should Libraries Be the Keepers of Their Cities’ Public Data?

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Linda Poon at CityLab: “In recent years, dozens of U.S. cities have released pools of public data. It’s an effort to improve transparency and drive innovation, and done well, it can succeed at both: Governments, nonprofits, and app developers alike have eagerly gobbled up that data, hoping to improve everything from road conditions to air quality to food delivery.

But what often gets lost in the conversation is the idea of how public data should be collected, managed, and disseminated so that it serves everyone—rather than just a few residents—and so that people’s privacy and data rights are protected. That’s where librarians come in.

“As far as how private and public data should be handled, there isn’t really a strong model out there,” says Curtis Rogers, communications director for the Urban Library Council (ULC), an association of leading libraries across North America. “So to have the library as the local institution that is the most trusted, and to give them that responsibility, is a whole new paradigm for how data could be handled in a local government.”

In fact, librarians have long been advocates of digital inclusion and literacy. That’s why, last month, ULC launched a new initiative to give public libraries a leading role in a future with artificial intelligence. They kicked it off with a working group meeting in Washington, D.C., where representatives from libraries in cities like Baltimore, Toronto, Toledo, and Milwaukee met to exchange ideas on how to achieve that through education and by taking on a larger role in data governance.

It’s a broad initiative, and Rogers says they are still in the beginning stages of determining what that role will ultimately look like. But the group will discuss how data should be organized and managed, hash out the potential risks of artificial intelligence, and eventually develop a field-wide framework for how libraries can help drive equitable public data policies in cities.

Already, individual libraries are involved with their city’s data. Chattanooga Public Library (which wasn’t part of the working group, but is a member of ULC) began hosting the city’s open data portal in 2014, turning a traditionally print-centered institution into a community data hub. Since then, the portal has added more than 280 data sets and garnered hundreds of thousands of page views, according to a report for the 2018 fiscal year….

The Toronto Public Library is also in a unique position because it may soon sit inside one of North America’s “smartest” cities. Last month, the city’s board of trade published a 17-page report titled “BiblioTech,” calling for the library to oversee data governance for all smart city projects.

It’s a grand example of just how big the potential is for public libraries. Ryan says the proposal remains just that at the moment, and there are no details yet on what such a model would even look like. She adds that they were not involved in drafting the proposal, and were only asked to provide feedback. But the library is willing to entertain the idea.

Such ambitions would be a large undertaking in the U.S., however, especially for smaller libraries that are already understaffed and under-resourced. According to ULC’s survey of its members, only 23 percent of respondents said they have a staff person designated as the AI lead. A little over a quarter said they even have AI-related educational programming, and just 15 percent report being part of any local or national initiative.

Debbie Rabina, a professor of library science at Pratt Institute in New York, also cautions that putting libraries in charge of data governance has to be carefully thought out. It’s one thing for libraries to teach data literacy and privacy, and to help cities disseminate data. But to go further than that—to have libraries collecting and owning data and to have them assessing who can and can’t use the data—can lead to ethical conflicts and unintended consequences that could erode the public’s trust….(More)”.