business

JPMorgan is quietly building an IBM Watson-like platform

Curated on August 26, 2018 by Stefaan Verhulst

Frank Chaparro at BusinessInsider: “JPMorgan’s corporate and investment bank is best known for advising businesses on billion-dollar acquisitions, helping private unicorns tap into the public markets, and managing the cash of Fortune 500 companies.

But now it is quietly working on a new platform that would go far beyond anything the firm has previously done, using crowdsourcing to accumulate massive amounts of data intended to one day help its clients make complex decisions about how to run their businesses, according to people familiar with the project.

For JPMorgan’s clients like asset-management firms and hedge funds, it could provide new data sets to help investors squeeze out more alpha from their models or better price assets. But JPMorgan is looking to go beyond the buy side to help its large corporate clients as well. The platform could, for example, help retailers figure out where to build their next store, inform manufacturers about how to revamp systems in their factories, and improve logistics management for delivery services companies, the people said.

The platform, called Roar by JPMorgan, would store sensitive private data, such as hospital records or satellite imagery, that’s not in the public domain. Typically, this type of information is exchanged between firms on a bilateral arrangement so it is not improperly used. But Roar would allow clients to tap into this data, which they could then use in a secure fashion to make forecasts and gain business insights….

Right now, the platform is being tested internally with public data and JPMorgan is collaborating with academics to answer questions such as predicting traffic patterns or future air pollution….(More)”.

Data Science Thinking: The Next Scientific, Technological and Economic Revolution

Curated on August 20, 2018 by Stefaan Verhulst

Book by Longbing Cao: “This book explores answers to the fundamental questions driving the research, innovation and practices of the latest revolution in scientific, technological and economic development: how does data science transform existing science, technology, industry, economy, profession and education? How does one remain competitive in the data science field? What is responsible for shaping the mindset and skillset of data scientists?

Data Science Thinking paints a comprehensive picture of data science as a new scientific paradigm from the scientific evolution perspective, as data science thinking from the scientific-thinking perspective, as a trans-disciplinary science from the disciplinary perspective, and as a new profession and economy from the business perspective.

The topics cover an extremely wide spectrum of essential and relevant aspects of data science, spanning its evolution, concepts, thinking, challenges, discipline, and foundation, all the way to industrialization, profession, education, and the vast array of opportunities that data science offers. The book’s three parts each detail layers of these different aspects….(More)”.

Searching for the Smart City’s Democratic Future

Curated on August 16, 2018 by Stefaan Verhulst

Article by Bianca Wylie at the Center for International Governance Innovation: “There is a striking blue building on Toronto’s eastern waterfront. Wrapped top to bottom in bright, beautiful artwork by Montreal illustrator Cecile Gariepy, the building — a former fish-processing plant — stands out alongside the neighbouring parking lots and a congested highway. It’s been given a second life as an office for Sidewalk Labs — a sister company to Google that is proposing a smart city development in Toronto. Perhaps ironically, the office is like the smart city itself: something old repackaged to be light, fresh and novel.

“Our mission is really to use technology to redefine urban life in the twenty-first century.”

Dan Doctoroff, CEO of Sidewalk Labs, shared this mission in an interview with Freakonomics Radio. The phrase is a variant of the marketing language used by the smart city industry at large. Put more simply, the term “smart city” is usually used to describe the use of technology and data in cities.

No matter the words chosen to describe it, the smart city model has a flaw at its core: corporations are seeking to exert influence on urban spaces and democratic governance. And because most governments don’t have the policy in place to regulate smart city development — in particular, projects driven by the fast-paced technology sector — this presents a growing global governance concern.

This is where the story usually descends into warnings of smart city dystopia or failure. Loads of recent articles have detailed the science fiction-style city-of-the-future and speculated about the perils of mass data collection, and for good reason — these are important concepts that warrant discussion. It’s time, however, to push past dystopian narratives and explore solutions for the challenges that smart cities present in Toronto and globally…(More)”.

Countries Can Learn from France’s Plan for Public Interest Data and AI

Curated on August 15, 2018 by Stefaan Verhulst

Nick Wallace at the Center for Data Innovation: “French President Emmanuel Macron recently endorsed a national AI strategy that includes plans for the French state to make public and private sector datasets available for reuse by others in applications of artificial intelligence (AI) that serve the public interest, such as for healthcare or environmental protection. Although this strategy fails to set out how the French government should promote widespread use of AI throughout the economy, it will nevertheless give a boost to AI in some areas, particularly public services. Furthermore, the plan for promoting the wider reuse of datasets, particularly in areas where the government already calls most of the shots, is a practical idea that other countries should consider as they develop their own comprehensive AI strategies.

The French strategy, drafted by mathematician and Member of Parliament Cédric Villani, calls for legislation to mandate repurposing both public and private sector data, including personal data, to enable public-interest uses of AI by government or others, depending on the sensitivity of the data. For example, public health services could use data generated by Internet of Things (IoT) devices to help doctors better treat and diagnose patients. Researchers could use data captured by motorway CCTV to train driverless cars. Energy distributors could manage peaks and troughs in demand using data from smart meters.

Repurposed data held by private companies could be made publicly available, shared with other companies, or processed securely by the public sector, depending on the extent to which sharing the data presents privacy risks or undermines competition. The report suggests that the government would not require companies to share data publicly when doing so would impact legitimate business interests, nor would it require that any personal data be made public. Instead, Dr. Villani argues that, if wider data sharing would do unreasonable damage to a company’s commercial interests, it may be appropriate to only give public authorities access to the data. But where the stakes are lower, companies could be required to share the data more widely, to maximize reuse. Villani rightly argues that it is virtually impossible to come up with generalizable rules for how data should be shared that would work across all sectors. Instead, he argues for a sector-specific approach to determining how and when data should be shared.

After making the case for state-mandated repurposing of data, the report goes on to highlight four key sectors as priorities: health, transport, the environment, and defense. Since these all have clear implications for the public interest, France can create national laws authorizing extensive repurposing of personal data without violating the General Data Protection Regulation (GDPR) which allows national laws that permit the repurposing of personal data where it serves the public interest. The French strategy is the first clear effort by an EU member state to proactively use this clause in aid of national efforts to bolster AI….(More)”.

Programmers need ethics when designing the technologies that influence people’s lives

Curated on August 14, 2018October 9, 2018 by Stefaan Verhulst

Cherri M. Pancake at The Conversation: “Computing professionals are on the front lines of almost every aspect of the modern world. They’re involved in the response when hackers steal the personal information of hundreds of thousands of people from a large corporation. Their work can protect – or jeopardize – critical infrastructure like electrical grids and transportation lines. And the algorithms they write may determine who gets a job, who is approved for a bank loan or who gets released on bail.

Technological professionals are the first, and last, lines of defense against the misuse of technology. Nobody else understands the systems as well, and nobody else is in a position to protect specific data elements or ensure the connections between one component and another are appropriate, safe and reliable. As the role of computing continues its decades-long expansion in society, computer scientists are central to what happens next.

That’s why the world’s largest organization of computer scientists and engineers, the Association for Computing Machinery, of which I am president, has issued a new code of ethics for computing professionals. And it’s why ACM is taking other steps to help technologists engage with ethical questions….

ACM’s new ethics code has several important differences from the 1992 version. One has to do with unintended consequences. In the 1970s and 1980s, technologists built software or systems whose effects were limited to specific locations or circumstances. But over the past two decades, it has become clear that as technologies evolve, they can be applied in contexts very different from the original intent.

For example, computer vision research has led to ways of creating 3D models of objects – and people – based on 2D images, but it was never intended to be used in conjunction with machine learning in surveillance or drone applications. The old ethics code asked software developers to be sure a program would actually do what they said it would. The new version also exhorts developers to explicitly evaluate their work to identify potentially harmful side effects or potential for misuse.

Another example has to do with human interaction. In 1992, most software was being developed by trained programmers to run operating systems, databases and other basic computing functions. Today, many applications rely on user interfaces to interact directly with a potentially vast number of people. The updated code of ethics includes more detailed considerations about the needs and sensitivities of very diverse potential users – including discussing discrimination, exclusion and harassment….(More)”.

Introducing the (World’s First) Ethical Operating System

Curated on August 9, 2018September 24, 2018 by Stefaan Verhulst

Article by Paula Goldman and Raina Kumra: “Is it possible for tech developers to anticipate future risks? Or are these future risks so unknowable to us here in the present that, try as we might to make our tech safe, continued exposure to risks is simply the cost of engagement?

The Ethical OS is already being piloted by nearly 20 tech companies, schools, and startups, including Mozilla and Techstars. We believe it can better equip technologists to grapple with three of the most pressing issues facing our community today:

- If the technology you’re building right now will someday be used in unexpected ways, how can you hope to be prepared?

- What new categories of risk should you pay special attention to right now?

Which design, team, or business model choices can actively safeguard users, communities, society, and your company from future risk?

As large sections of the public grow weary of a seemingly constant stream of data safety and security issues, and with growing calls for heightened government intervention and oversight, the time is now for the tech community to get this right.

We created the Ethical OS as a pilot to help make ethical thinking and future risk mitigation integral components of all design and development processes. It’s not going to be easy. The industry has far more work to do, both inside individual companies and collectively. But with our toolkit as a guide, developers will have a practical means of helping to begin working to ensure their tech is as good as their intentions…(More)”.

Civil Society as Public Conscience

Curated on August 8, 2018 by Stefaan Verhulst

Larry Kramer at the Stanford Social Innovation Review: “Does civil society address questions of values in ways that government and business cannot? This question makes sense if we presuppose limits on the values government and business can express. However, there are no such limits, as evidenced by the way both sectors have, throughout US history, taken positions and played roles on all sides of our nation’s great moral and political debates. This is hardly surprising inasmuch as “government” and “business,” no less than “civil society,” comprise a multiplicity of actors with widely divergent interests, passions, and beliefs. The principle of federalism is built on the idea (well-established empirically) that different governments, operating at different levels and in different places, will respond to problems differently, creating multiple channels for competitive democratic action. Likewise, the competitiveness of the marketplace ensures that, with rare exceptions, there are business interests on different sides of most questions.

Yet while government and business may not be monoliths, their decisions and actions are subject to predictable, systematic forms of distortion….

What sets civil society organizations apart is that they are free from precisely the forces that limit actors in government and business; they are neither responsible to voters nor (usually) restricted by market discipline. They can be entirely mission driven, which gives them the freedom to test controversial ideas, develop challenging positions, and advocate for change based wholly on the magnitude and meaning of an issue or objective. As important, they can use this freedom to intervene with government or business in ways that overcome or circumvent the obstacles that bias these sectors’ decisions and activities. Short-term pressures may make it difficult for government agencies to invest in experiments, for example, but they can take up proven concepts. Civil society organizations can establish the necessary proof and, within legal limits, help overcome political barriers that may block adoption. Nonprofit activity may likewise be able to correct market defects or foster conditions that encourage deeper business investment. Nonprofit leaders can take risks that government agents and business managers dependably shy away from, and they can stay with efforts that take time to show results.

More profoundly, nonprofits have the freedom to play the role of “prodder,” of idea advocate, of irritant to systems that need to be irritated. Civil society can be our public conscience, helping make sure that we do not turn our back on fundamental values, or forget about those who lack market and political power.

There is a rub, of course (there’s always a rub). Civil society organizations may be free from political and market discipline, but only by subjecting themselves to the whims and caprice of philanthropic funders. This alternative distortion is to some extent blunted by the pluralistic, decentralized nature of the funder community; there are a great many funders out there, and they represent a broad range of ideologies, interests, and viewpoints. But the flaws in this system are many and well known. Scrambling for dollars is time consuming and difficult, and most funders restrict their support while failing to cover a grantees’ full costs. Awkward differences between how funders and grantees understand a problem or think it should be addressed are common. Nonprofits understandably feel that funders sometimes undervalue their expertise and front-line experience, while funders just as understandably feel responsible for making independent judgments about how nonprofits should use their resources. And while the funder community is more pluralistic than its critics allow, many viewpoints and approaches indubitably fail to find support—sometimes for worse, as well as for better…(More)”.

The Democratization of Data Science

Curated on August 6, 2018September 26, 2018 by Stefaan Verhulst

Jonathan Cornelissen at Harvard Business School: “Want to catch tax cheats? The government of Rwanda does — and it’s finding them by studying anomalies in revenue-collection data.

Want to understand how American culture is changing? So does a budding sociologist in Indiana. He’s using data science to find patterns in the massive amounts of text people use each day to express their worldviews — patterns that no individual reader would be able to recognize.

Intelligent people find new uses for data science every day. Still, despite the explosion of interest in the data collected by just about every sector of American business — from financial companies and health care firms to management consultancies and the government — many organizations continue to relegate data-science knowledge to a small number of employees.

That’s a mistake — and in the long run, it’s unsustainable. Think of it this way: Very few companies expect only professional writers to know how to write. So why ask onlyprofessional data scientists to understand and analyze data, at least at a basic level?

Relegating all data knowledge to a handful of people within a company is problematic on many levels. Data scientists find it frustrating because it’s hard for them to communicate their findings to colleagues who lack basic data literacy. Business stakeholders are unhappy because data requests take too long to fulfill and often fail to answer the original questions. In some cases, that’s because the questioner failed to explain the question properly to the data scientist.

Why would non–data scientists need to learn data science? That’s like asking why non-accountants should be expected to stay within budget.

These days every industry is drenched in data, and the organizations that succeed are those that most quickly make sense of their data in order to adapt to what’s coming. The best way to enable fast discovery and deeper insights is to disperse data science expertise across an organization.

Companies that want to compete in the age of data need to do three things: share data tools, spread data skills, and spread data responsibility…(More)”.

A rationale for data governance as an approach to tackle recurrent drawbacks in open data portals

Curated on August 4, 2018 by Stefaan Verhulst

Conference paper by Juan Ribeiro Reis et al: “Citizens and developers are gaining broad access to public data sources, made available in open data portals. These machine-readable datasets enable the creation of applications that help the population in several ways, giving them the opportunity to actively participate in governance processes, such as decision taking and policy-making.

While the number of open data portals grows over the years, researchers have been able to identify recurrent problems with the data they provide, such as lack of data standards, difficulty in data access and poor understandability. Such issues make difficult the effective use of data. Several works in literature propose different approaches to mitigate these issues, based on novel or well-known data management techniques.

However, there is a lack of general frameworks for tackling these problems. On the other hand, data governance has been applied in large companies to manage data problems, ensuring that data meets business needs and become organizational assets. In this paper, firstly, we highlight the main drawbacks pointed out in literature for government open data portals. Eventually, we bring around how data governance can tackle much of the issues identified…(More)”.

Regulatory Technology – Replacing Law with Computer Code

Curated on August 1, 2018 by Stefaan Verhulst

LSE Legal Studies Working Paper by Eva Micheler and Anna Whaley: “Recently both the Bank of England and the Financial Conduct Authority have carried out experiments using new digital technology for regulatory purposes. The idea is to replace rules written in natural legal language with computer code and to use artificial intelligence for regulatory purposes.

This new way of designing public law is in line with the government’s vision for the UK to become a global leader in digital technology. It is also reflected in the FCA’s business plan.

The article reviews the technology and the advantages and disadvantages of combining the technology with regulatory law. It then informs the discussion from a broader public law perspective. It analyses regulatory technology through criteria developed in the mainstream regulatory discourse. It contributes to that discourse by anticipating problems that will arise as the technology evolves. In addition, the hope is to assist the government in avoiding mistakes that have occurred in the past and creating a better system from the start…(More)”.