Technology & the Law of Corporate Responsibility – The Impact of Blockchain


Blogpost by Elizabeth Boomer: “Blockchain, a technology regularly associated with digital currency, is increasingly being utilized as a corporate social responsibility tool in major international corporations. This intersection of law, technology, and corporate responsibility was addressed earlier this month at the World Bank Law, Justice, and Development Week 2019, where the theme was Rights, Technology and Development. The law related to corporate responsibility for sustainable development is increasingly visible due in part to several lawsuits against large international corporations, alleging the use of child and forced labor. In addition, the United Nations has been working for some time on a treaty on business and human rights to encourage corporations to avoid “causing or contributing to adverse human rights impacts through their own activities and [to] address such impacts when they occur.”

DeBeersVolvo, and Coca-Cola, among other industry leaders, are using blockchain, a technology that allows digital information to be distributed and analyzed, but not copied or manipulated, to trace the source of materials and better manage their supply chains. These initiatives have come as welcome news in industries where child or forced labor in the supply chain can be hard to detect, e.g. conflict minerals, sugar, tobacco, and cacao. The issue is especially difficult when trying to trace the mining of cobalt for lithium ion batteries, increasingly used in electric cars, because the final product is not directly traceable to a single source.

While non governmental organizations (NGOs) have been advocating for improved corporate performance in supply chains regarding labor and environmental standards for years, blockchain may be a technological tool that could reliably trace information regarding various products – from food to minerals – that go through several layers of suppliers before being certified as slave- or child labor- free.

Child labor and forced labor are still common in some countries. The majority of countries worldwide have ratified International Labour Organization (ILO) Convention No. 182, prohibiting the worst forms of child labor (186 ratifications), as well as the ILO Convention prohibiting forced labor (No. 29, with 178 ratifications), and the abolition of forced labor (Convention No. 105, with 175 ratifications). However, the ILO estimates that approximately 40 million men and women are engaged in modern day slavery and 152 million children are subject to child labor, 38% of whom are working in hazardous conditions. The enduring existence of forced labor and child labor raises difficult ethical questions, because in many contexts, the victim does not have a viable alternative livelihood….(More)”.

Artificial intelligence: From expert-only to everywhere


Deloitte: “…AI consists of multiple technologies. At its foundation are machine learning and its more complex offspring, deep-learning neural networks. These technologies animate AI applications such as computer vision, natural language processing, and the ability to harness huge troves of data to make accurate predictions and to unearth hidden insights (see sidebar, “The parlance of AI technologies”). The recent excitement around AI stems from advances in machine learning and deep-learning neural networks—and the myriad ways these technologies can help companies improve their operations, develop new offerings, and provide better customer service at a lower cost.

The trouble with AI, however, is that to date, many companies have lacked the expertise and resources to take full advantage of it. Machine learning and deep learning typically require teams of AI experts, access to large data sets, and specialized infrastructure and processing power. Companies that can bring these assets to bear then need to find the right use cases for applying AI, create customized solutions, and scale them throughout the company. All of this requires a level of investment and sophistication that takes time to develop, and is out of reach for many….

These tech giants are using AI to create billion-dollar services and to transform their operations. To develop their AI services, they’re following a familiar playbook: (1) find a solution to an internal challenge or opportunity; (2) perfect the solution at scale within the company; and (3) launch a service that quickly attracts mass adoption. Hence, we see Amazon, Google, Microsoft, and China’s BATs launching AI development platforms and stand-alone applications to the wider market based on their own experience using them.

Joining them are big enterprise software companies that are integrating AI capabilities into cloud-based enterprise software and bringing them to the mass market. Salesforce, for instance, integrated its AI-enabled business intelligence tool, Einstein, into its CRM software in September 2016; the company claims to deliver 1 billion predictions per day to users. SAP integrated AI into its cloud-based ERP system, S4/HANA, to support specific business processes such as sales, finance, procurement, and the supply chain. S4/HANA has around 8,000 enterprise users, and SAP is driving its adoption by announcing that the company will not support legacy SAP ERP systems past 2025.

A host of startups is also sprinting into this market with cloud-based development tools and applications. These startups include at least six AI “unicorns,” two of which are based in China. Some of these companies target a specific industry or use case. For example, Crowdstrike, a US-based AI unicorn, focuses on cybersecurity, while Benevolent.ai uses AI to improve drug discovery.

The upshot is that these innovators are making it easier for more companies to benefit from AI technology even if they lack top technical talent, access to huge data sets, and their own massive computing power. Through the cloud, they can access services that address these shortfalls—without having to make big upfront investments. In short, the cloud is democratizing access to AI by giving companies the ability to use it now….(More)”.

Beyond the Valley


Book by Ramesh Srinivasan: “How to repair the disconnect between designers and users, producers and consumers, and tech elites and the rest of us: toward a more democratic internet.

In this provocative book, Ramesh Srinivasan describes the internet as both an enabler of frictionless efficiency and a dirty tangle of politics, economics, and other inefficient, inharmonious human activities. We may love the immediacy of Google search results, the convenience of buying from Amazon, and the elegance and power of our Apple devices, but it’s a one-way, top-down process. We’re not asked for our input, or our opinions—only for our data. The internet is brought to us by wealthy technologists in Silicon Valley and China. It’s time, Srinivasan argues, that we think in terms beyond the Valley.

Srinivasan focuses on the disconnection he sees between designers and users, producers and consumers, and tech elites and the rest of us. The recent Cambridge Analytica and Russian misinformation scandals exemplify the imbalance of a digital world that puts profits before inclusivity and democracy. In search of a more democratic internet, Srinivasan takes us to the mountains of Oaxaca, East and West Africa, China, Scandinavia, North America, and elsewhere, visiting the “design labs” of rural, low-income, and indigenous people around the world. He talks to a range of high-profile public figures—including Elizabeth Warren, David Axelrod, Eric Holder, Noam Chomsky, Lawrence Lessig, and the founders of Reddit, as well as community organizers, labor leaders, and human rights activists. To make a better internet, Srinivasan says, we need a new ethic of diversity, openness, and inclusivity, empowering those now excluded from decisions about how technologies are designed, who profits from them, and who are surveilled and exploited by them….(More)”

Data Ownership: Exploring Implications for Data Privacy Rights and Data Valuation


Hearing by the Senate Committee on Banking, Housing and Urban Affairs:”…As a result of an increasingly digital economy, more personal information is available to companies than ever before.
Private companies are collecting, processing, analyzing and sharing considerable data on individuals for all kinds of purposes.

There have been many questions about what personal data is being collected, how it is being collected, with whom it is being shared and how it is being used, including in ways that affect individuals’ financial lives.

Given the vast amount of personal information flowing through the economy, individuals need real control over their personal data. This Committee has held a series of data privacy hearings exploring possible
frameworks for facilitating privacy rights to consumers. Nearly all have included references to data as a new currency or commodity.

The next question, then, is who owns it? There has been much debate about the concept of data ownership, the monetary value of personal information and its potential role in data privacy…..The witnesses will be: 

  1. Mr. Jeffrey Ritter Founding Chair, American Bar Association Committee on Cyberspace Law, External Lecturer
  2. Mr. Chad Marlow Senior Advocacy And Policy Counsel American Civil Liberties Union
  3. Mr. Will Rinehart Director Of Technology And Innovation Policy American Action Forum
  4. Ms. Michelle Dennedy Chief Executive Officer DrumWave Inc.

Should Consumers Be Able to Sell Their Own Personal Data?


The Wall Street Journal: “People around the world are confused and concerned about what companies do with the data they collect from their interactions with consumers.

A global survey conducted last fall by the research firm Ipsos gives a sense of the scale of people’s worries and uncertainty. Roughly two-thirds of those surveyed said they knew little or nothing about how much data companies held about them or what companies did with that data. And only about a third of respondents on average said they had at least a fair amount of trust that a variety of corporate and government organizations would use the information they had about them in the right way….

Christopher Tonetti, an associate professor of economics at Stanford Graduate School of Business, says consumers should own and be able to sell their personal data. Cameron F. Kerry, a visiting fellow at the Brookings Institution and former general counsel and acting secretary of the U.S. Department of Commerce, opposes the idea….

YES: It Would Encourage Sharing of Data—a Plus for Consumers and Society…Data isn’t like other commodities in one fundamental way—it doesn’t diminish with use. And that difference is the key to why consumers should own the data that’s created when they interact with companies, and have the right to sell it.YES: It Would Encourage Sharing of Data—a Plus for Consumers and Society…

NO: It Would Do Little to Help Consumers, and Could Leave Them Worse Off Than Now…

But owning data will do little to help consumers’ privacy—and may well leave them worse off. Meanwhile, consumer property rights would create enormous friction for valid business uses of personal information and for the free flow of information we value as a society.

In our current system, consumers reflexively click away rights to data in exchange for convenience, free services, connection, endorphins or other motivations. In a market where consumers could sell or license personal information they generate from web browsing, ride-sharing apps and other digital activities, is there any reason to expect that they would be less motivated to share their information? …(More)”.

The Ethics of Big Data Applications in the Consumer Sector


Paper by Markus Christen et al : “Business applications relying on processing of large amounts of heterogeneous data (Big Data) are considered to be key drivers of innovation in the digital economy. However, these applications also pose ethical issues that may undermine the credibility of data-driven businesses. In our contribution, we discuss ethical problems that are associated with Big Data such as: How are core values like autonomy, privacy, and solidarity affected in a Big Data world? Are some data a public good? Or: Are we obliged to divulge personal data to a certain degree in order to make the society more secure or more efficient?

We answer those questions by first outlining the ethical topics that are discussed in the scientific literature and the lay media using a bibliometric approach. Second, referring to the results of expert interviews and workshops with practitioners, we identify core norms and values affected by Big Data applications—autonomy, equality, fairness, freedom, privacy, property-rights, solidarity, and transparency—and outline how they are exemplified in examples of Big Data consumer applications, for example, in terms of informational self-determination, non-discrimination, or free opinion formation. Based on use cases such as personalized advertising, individual pricing, or credit risk management we discuss the process of balancing such values in order to identify legitimate, questionable, and unacceptable Big Data applications from an ethics point of view. We close with recommendations on how practitioners working in applied data science can deal with ethical issues of Big Data….(More)”.

Study finds that a GPS outage would cost $1 billion per day


Eric Berger at Ars Technica: “….one of the most comprehensive studies on the subject has assessed the value of this GPS technology to the US economy and examined what effect a 30-day outage would have—whether it’s due to a severe space weather event or “nefarious activity by a bad actor.” The study was sponsored by the US government’s National Institutes of Standards and Technology and performed by a North Carolina-based research organization named RTI International.

Economic effect

As part of the analysis, researchers spoke to more than 200 experts in the use of GPS technology for various services, from agriculture to the positioning of offshore drilling rigs to location services for delivery drivers. (If they’d spoken to me, I’d have said the value of using GPS to navigate Los Angeles freeways and side streets was incalculable). The study covered a period from 1984, when the nascent GPS network was first opened to commercial use, through 2017. It found that GPS has generated an estimated $1.4 trillion in economic benefits during that time period.

The researchers found that the largest benefit, valued at $685.9 billion, came in the “telecommunications” category,  including improved reliability and bandwidth utilization for wireless networks. Telematics (efficiency gains, cost reductions, and environmental benefits through improved vehicle dispatch and navigation) ranked as the second most valuable category at $325 billion. Location-based services on smartphones was third, valued at $215 billion.

Notably, the value of GPS technology to the US economy is growing. According to the study, 90 percent of the technology’s financial impact has come since just 2010, or just 20 percent of the study period. Some sectors of the economy are only beginning to realize the value of GPS technology, or are identifying new uses for it, the report says, indicating that its value as a platform for innovation will continue to grow.

Outage impact

In the case of some adverse event leading to a widespread outage, the study estimates that the loss of GPS service would have a $1 billion per-day impact, although the authors acknowledge this is at best a rough estimate. It would likely be higher during the planting season of April and May, when farmers are highly reliant on GPS technology for information about their fields.

To assess the effect of an outage, the study looked at several different variables. Among them was “precision timing” that enables a number of wireless services, including the synchronization of traffic between carrier networks, wireless handoff between base stations, and billing management. Moreover, higher levels of precision timing enable higher bandwidth and provide access to more devices. (For example, the implementation of 4G LTE technology would have been impossible without GPS technology)….(More)”

Commission publishes guidance on free flow of non-personal data


European Commission: “The guidance fulfils an obligation in the Regulation on the free flow of non-personal data (FFD Regulation), which requires the Commission to publish a guidance on the interaction between this Regulation and the General Data Protection Regulation (GDPR), especially as regards datasets composed of both personal and non-personal data. It aims to help users – in particular small and medium-sized enterprises – understand the interaction between the two regulations.

In line with the existing GDPR documents, prepared by the European Data Protection Board, this guidance document aims to clarify which rules apply when processing personal and non-personal data. It gives a useful overview of the central concepts of the free flow of personal and non-personal data within the EU, while explaining the relation between the two Regulations in practical terms and with concrete examples….

Non-personal data are distinct from personal data, as laid down in the GDPR Regulation. The non-personal data can be categorised in terms of origin, namely:

  • data which originally did not relate to an identified or identifiable natural person, such as data on weather conditions generated by sensors installed on wind turbines, or data on maintenance needs for industrial machines; or
  • data which was initially personal data, but later made anonymous.

While the guidance refers to more examples of non-personal data, it also explains the concept of personal data, anonymised and pseudonymised, to provide a better understanding as well describes the limitations between personal and non-personal data.

What are mixed datasets?

In most real-life situations, a dataset is very likely to be composed of both personal and non-personal data. This is often referred to as a “mixed dataset”. Mixed datasets represent the majority of datasets used in the data economy and commonly gathered thanks to technological developments such as the Internet of Things (i.e. digitally connecting objects), artificial intelligence and technologies enabling big data analytics.

Examples of mixed datasets include a company’s tax records, mentioning the name and telephone number of the managing director of the company. This can also include a company’s knowledge of IT problems and solutions based on individual incident reports, or a research institution’s anonymised statistical data and the raw data initially collected, such as the replies of individual respondents to statistical survey questions….(More)”.

Open Data and the Private Sector


Chapter by Joel Gurin, Carla Bonini and Stefaan Verhulst in State of Open Data: “The open data movement launched a decade ago with a focus on transparency, good governance, and citizen participation. As other chapters in this collection have documented in detail, those critical uses of open data have remained paramount and are continuing to grow in importance at a time of fake news and increased secrecy. But the value of open data extends beyond transparency and accountability – open data is also an important resource for business and economic growth.

The past several years have seen an increased focus on the value of open data to the private sector. In 2012, the Open Data Institute (ODI) was founded in the United Kingdom (UK) and backed with GBP 10 million by the UK government to maximise the value of open data in business and government. A year later, McKinsey released a report suggesting open data could help unlock USD 3 to 5 trillion in economic value annually. At around the same time, Monsanto acquired the Climate Corporation, a digital agriculture company that leverages open data to inform farmers for approximately USD 1.1 billion. In 2014, the GovLab launched the Open Data 500,2the first national study of businesses using open government data (now in six countries), and, in 2015, Open Data for Development (OD4D) launched the Open Data Impact Map, which today contains more than 1 100 examples of private sector companies using open data. The potential business applications of open data continue to be a priority for many governments around the world as they plan and develop their data programmes.

The use of open data has become part of the broader business practice of using data and data science to inform business decisions, ranging from launching new products and services to optimising processes and outsmarting the competition. In this chapter, we take stock of the state of open data and the private sector by analysing how the private sector both leverages and contributes to the open data ecosystem….(More)”.

Companies That Rely On Census Data Worry Citizenship Question Will Hurt


Hansi Lo Wang at NPR: “Some critics of the citizenship question the Trump administration wants to add to the 2020 census are coming from a group that tends to stay away from politically heated issues — business leaders.

From longtime corporations like Levi Strauss & Co. to upstarts like Warby Parker, some companies say that including the question — “Is this person a citizen of the United States?” — could harm not only next year’s national head count, but also their bottom line.

How governments use census data is a common refrain in the lead-up to a constitutionally mandated head count of every person living in the U.S. The new population counts, gathered once a decade, are used to determine how congressional seats and Electoral College votes are distributed among the states. They also guide how hundreds of billions in federal tax dollars are spread around the country to fund public services.

What is often less visible is how the census data undergird decisions made by large and small businesses across the country. The demographic information the census collects — including the age, sex, race, ethnicity and housing status of all U.S. residents — informs business owners about who their existing and future customers are, which new products and services those markets may want and where to build new locations.

Weeks before the Supreme Court heard oral arguments over the citizenship question last month, more than two dozen companies and business groups filed a friend-of-the-court brief against the question. Its potential impact on the accuracy of census data, especially about immigrants and people of color, is drawing concern from both Lyft and Uber, as well as Levi Strauss, Warby Parker and Univision.

“We don’t view this as a political situation at all,” says Christine Pierce, the senior vice president of data science at Nielsen — a major data analytics company in the business world that filed its own brief with the high court. “We see this as one that is around sound research and good science.”…(More)”.