Paper by Takahiro Yabe et al: “Disruptions, such as closures of businesses during pandemics, not only affect businesses and amenities directly but also influence how people move, spreading the impact to other businesses and increasing the overall economic shock. However, it is unclear how much businesses depend on each other during disruptions. Leveraging human mobility data and same-day visits in five US cities, we quantify dependencies between points of interest encompassing businesses, stores and amenities. We find that dependency networks computed from human mobility exhibit significantly higher rates of long-distance connections and biases towards specific pairs of point-of-interest categories. We show that using behaviour-based dependency relationships improves the predictability of business resilience during shocks by around 40% compared with distance-based models, and that neglecting behaviour-based dependencies can lead to underestimation of the spatial cascades of disruptions. Our findings underscore the importance of measuring complex relationships in patterns of human mobility to foster urban economic resilience to shocks…(More)”.
Use GenAI to Improve Scenario Planning
Article by Daniel J. Finkenstadt et al: “Businesses are increasingly leveraging strategic foresight and scenario planning to navigate uncertainties stemming from climate change, global conflicts, and technological advancements. Traditional methods, however, struggle with identifying key trends, exploring multiple scenarios, and providing actionable guidance. Generative AI offers a robust alternative, enabling rapid, cost-effective, and comprehensive contingency planning. This AI-driven approach enhances scenario creation, narrative exploration, and strategy generation, providing detailed, adaptable strategies rather than conclusive solutions. This approach demands accurate, relevant data and encourages iterative refinement, transforming how organizations forecast and strategize for the future…(More)”.
A Brief History of Automations That Were Actually People
Article by Brian Contreras: “If you’ve ever asked a chatbot a question and received nonsensical gibberish in reply, you already know that “artificial intelligence” isn’t always very intelligent.
And sometimes it isn’t all that artificial either. That’s one of the lessons from Amazon’s recent decision to dial back its much-ballyhooed “Just Walk Out” shopping technology, a seemingly science-fiction-esque software that actually functioned, in no small part, thanks to behind-the-scenes human labor.
This phenomenon is nicknamed “fauxtomation” because it “hides the human work and also falsely inflates the value of the ‘automated’ solution,” says Irina Raicu, director of the Internet Ethics program at Santa Clara University’s Markkula Center for Applied Ethics.
Take Just Walk Out: It promises a seamless retail experience in which customers at Amazon Fresh groceries or third-party stores can grab items from the shelf, get billed automatically and leave without ever needing to check out. But Amazon at one point had more than 1,000 workers in India who trained the Just Walk Out AI model—and manually reviewed some of its sales—according to an article published last year on the Information, a technology business website.
An anonymous source who’d worked on the Just Walk Out technology told the outlet that as many as 700 human reviews were needed for every 1,000 customer transactions. Amazon has disputed the Information’s characterization of its process. A company representative told Scientific American that while Amazon “can’t disclose numbers,” Just Walk Out has “far fewer” workers annotating shopping data than has been reported. In an April 17 blog post, Dilip Kumar, vice president of Amazon Web Services applications, wrote that “this is no different than any other AI system that places a high value on accuracy, where human reviewers are common.”…(More)”
Data Rules: Reinventing the Market Economy
Book by Cristina Alaimo and Jannis Kallinikos: “Digital data have become the critical frontier where emerging economic practices and organizational forms confront the traditional economic order and its institutions. In Data Rules, Cristina Alaimo and Jannis Kallinikos establish a social science framework for analyzing the unprecedented social and economic restructuring brought about by data. Working at the intersection of information systems and organizational studies, they draw extensively on intellectual currents in sociology, semiotics, cognitive science and technology, and social theory. Making the case for turning “data-making” into an area of inquiry of its own, the authors uncover how data are deeply implicated in rewiring the institutions of the market economy.
The authors associate digital data with the decentering of organizations. As they point out, centered systems make sense only when firms (and formal organizations more broadly) can keep the external world at arm’s length and maintain a relative operation independence from it. These patterns no longer hold. Data transform the production of goods and services to an endless series of exchanges and interactions that defeat the functional logics of markets and organizations. The diffusion of platforms and ecosystems is indicative of these broader transformations. Rather than viewing data as simply a force of surveillance and control, the authors place the transformative potential of data at the center of an emerging socioeconomic order that restructures society and its institutions…(More)”.
Governing the use of big data and digital twin technology for sustainable tourism
Report by Eko Rahmadian: “The tourism industry is increasingly utilizing big data to gain valuable insights and enhance decision-making processes. The advantages of big data, such as real-time information, robust data processing capabilities, and improved stakeholder decision-making, make it a promising tool for analyzing various aspects of tourism, including sustainability. Moreover, integrating big data with prominent technologies like machine learning, artificial intelligence (AI), and the Internet of Things (IoT) has the potential to revolutionize smart and sustainable tourism.
Despite the potential benefits, the use of big data for sustainable tourism remains limited, and its implementation poses challenges related to governance, data privacy, ethics, stakeholder communication, and regulatory compliance. Addressing these challenges is crucial to ensure the responsible and sustainable use of these technologies. Therefore, strategies must be developed to navigate these issues through a proper governing system.
To bridge the existing gap, this dissertation focuses on the current research on big data for sustainable tourism and strategies for governing its use and implementation in conjunction with emerging technologies. Specifically, this PhD dissertation centers on mobile positioning data (MPD) as a case due to its unique benefits, challenges, and complexity. Also, this project introduces three frameworks, namely: 1) a conceptual framework for digital twins (DT) for smart and sustainable tourism, 2) a documentation framework for architectural decisions (DFAD) to ensure the successful implementation of the DT technology as a governance mechanism, and 3) a big data governance framework for official statistics (BDGF). This dissertation not only presents these frameworks and their benefits but also investigates the issues and challenges related to big data governance while empirically validating the applicability of the proposed frameworks…(More)”.
Air Canada chatbot promised a discount. Now the airline has to pay it
Article by Kyle Melnick: “After his grandmother died in Ontario a few years ago, British Columbia resident Jake Moffatt visited Air Canada’s website to book a flight for the funeral. He received assistance from a chatbot, which told him the airline offered reduced rates for passengers booking last-minute travel due to tragedies.
Moffatt bought a nearly $600 ticket for a next-day flight after the chatbot said he would get some of his money back under the airline’s bereavement policy as long as he applied within 90 days, according to a recent civil-resolutions tribunal decision.
But when Moffatt later attempted to receive the discount, he learned that the chatbot had been wrong. Air Canada only awarded bereavement fees if the request had been submitted before a flight. The airline later argued the chatbot wasa separate legal entity “responsible for its own actions,” the decision said.
Moffatt filed a claim with the Canadian tribunal, which ruled Wednesday that Air Canada owed Moffatt more than $600 in damages and tribunal fees after failing to provide “reasonable care.”
As companies have added artificial intelligence-powered chatbots to their websites in hopes of providing faster service, the Air Canada dispute sheds light on issues associated with the growing technology and how courts could approach questions of accountability. The Canadian tribunal in this case came down on the side of the customer, ruling that Air Canada did not ensure its chatbot was accurate…(More)”
Policy primer on non-personal data
Primer by the International Chamber of Commerce: “Non-personal data plays a critical role in providing solutions to global challenges. Unlocking its full potential requires policymakers, businesses, and all other stakeholders to collaborate to construct policy environments that can capitalise on its benefits.
This report gives insights into the different ways that non-personal data has a positive impact on society, with benefits including, but not limited to:
- Tracking disease outbreaks;
- Facilitating international scientific cooperation;
- Understanding climate-related trends;
- Improving agricultural practices for increased efficiency;
- Optimising energy consumption;
- Developing evidence-based policy;
- Enhancing cross-border cybersecurity cooperation.
In addition, businesses of all sizes benefit from the transfer of data across borders, allowing companies to establish and maintain international supply chains and smaller businesses to enter new markets or reduce operating costs.
Despite these benefits, international flows of non-personal data are frequently limited by restrictions and data localisation measures. A growing patchwork of regulations can also create barriers to realising the potential of non-personal data. This report explores the impact of data flow restrictions including:
- Hindering global supply chains;
- Limiting the use of AI reliant on large datasets;
- Disincentivising data sharing amongst companies;
- Preventing companies from analysing the data they hold…(More)”.
EU leadership in trustworthy AI: Guardrails, Innovation & Governance
Article by Thierry Breton: “As mentioned in President von der Leyen’s State of the Union letter of intent, Europe should lead global efforts on artificial intelligence, guiding innovation, setting guardrails and developing global governance.
First, on innovation: we will launch the EU AI Start-Up Initiative, leveraging one of Europe’s biggest assets: its public high-performance computing infrastructure. We will identify the most promising European start-ups in AI and give them access to our supercomputing capacity.
I have said it before: AI is a combination of data, computing and algorithms. To train and finetune the most advanced foundation models, developers need large amounts of computing power.
Europe is a world leader in supercomputing through its European High-Performance Computing Joint Undertaking (EuroHPC). Soon, Europe will have its first exascale supercomputers, JUPITER in Germany and JULES VERNE in France (able to perform a quintillion -that means a billion billion- calculations per second), in addition to various existing supercomputers (such as LEONARDO in Italy and LUMI in Finland).
Access to Europe’s supercomputing infrastructure will help start-ups bring down the training time for their newest AI models from months or years to days or weeks. And it will help them lead the development and scale-up of AI responsibly and in line with European values.
This goes together with our broader efforts to support AI innovation across the value chain – from AI start-ups to all those businesses using AI technologies in their industrial ecosystems. This includes our Testing and Experimentation Facilities for AI (launched in January 2023), our Digital Innovation Hubs, the development of regulatory sandboxes under the AI Act, our support for the European Partnership on AI, Data and Robotics and the cutting-edge research supported by HorizonEurope.
Second, guardrails for AI: Europe has pioneered clear rules for AI systems through the EU AI Act, the world’s first comprehensive regulatory framework for AI. My teams are working closely with the Parliament and Council to support the swift adoption of the EU AI Act. This will give citizens and businesses confidence in AI developed in Europe, knowing that it is safe and respects fundamental rights and European values. And it serves as an inspiration for global rules and principles for trustworthy AI.
As reiterated by President von der Leyen, we are developing an AI Pact that will convene AI companies, help them prepare for the implementation of the EU AI Act and encourage them to commit voluntarily to applying the principles of the Act before its date of applicability.
Third, governance: with the AI Act and the Coordinated Plan on AI, we are working towards a governance framework for AI, which can be a centre of expertise, in particular on large foundation models, and promote cooperation, not only between Member States, but also internationally…(More)”
Data Is Everybody’s Business
Book by Barbara H. Wixom, Cynthia M. Beath and Leslie Owens: “Most organizations view data monetization—converting data into money—too narrowly: as merely selling data sets. But data monetization is a core business activity for both commercial and noncommercial organizations, and, within organizations, it’s critical to have wide-ranging support for this pursuit. In Data Is Everybody’s Business, the authors offer a clear and engaging way for people across the entire organization to understand data monetization and make it happen. The authors identify three viable ways to convert data into money—improving work with data, wrapping products with data, and selling information offerings—and explain when to pursue each and how to succeed…(More)”.
Data portability and interoperability: A primer on two policy tools for regulation of digitized industries
Article by Sukhi Gulati-Gilbert and Robert Seamans: “…In this article we describe two other tools, data portability and interoperability, that may be particularly useful in technology-enabled sectors. Data portability allows users to move data from one company to another, helping to reduce switching costs and providing rival firms with access to valuable customer data. Interoperability allows two or more technical systems to exchange data interactively. Due to its interactive nature, interoperability can help prevent lock-in to a specific platform by allowing users to connect across platforms. Data portability and interoperability share some similarities; in addition to potential pro-competitive benefits, the tools promote values of openness, transparency, and consumer choice.
After providing an overview of these topics, we describe the tradeoffs involved with implementing data portability and interoperability. While these policy tools offer lots of promise, in practice there can be many challenges involved when determining how to fund and design an implementation that is secure and intuitive and accomplishes the intended result. These challenges require that policymakers think carefully about the initial implementation of data portability and interoperability. Finally, to better show how data portability and interoperability can increase competition in an industry, we discuss how they could be applied in the banking and social media sectors. These are just two examples of how data portability and interoperability policy could be applied to many different industries facing increased digitization. Our definitions and examples should be helpful to those interested in understanding the tradeoffs involved in using these tools to promote competition and innovation in the U.S. economy…(More)” See also: Data to Go: The Value of Data Portability as a Means to Data Liquidity.