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: 

  1. Tracking disease outbreaks; 
  2. Facilitating international scientific cooperation; 
  3. Understanding climate-related trends; 
  4.  Improving agricultural practices for increased efficiency; 
  5. Optimising energy consumption; 
  6. Developing evidence-based policy; 
  7. 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 Hubsthe 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.

Data Sharing Between Public and Private Sectors: When Local Governments Seek Information from the Sharing Economy.


Paper by the Centre for Information Policy Leadership: “…addresses the growing trend of localities requesting (and sometimes mandating) that data collected by the private sector be shared with the localities themselves. Such requests are generally not in the context of law enforcement or national security matters, but rather are part of an effort to further the public interest or promote a public good.

To the extent such requests are overly broad or not specifically tailored to the stated public interest, CIPL believes that the public sector’s adoption of accountability measures—which CIPL has repeatedly promoted for the private sector—can advance responsible data sharing practices between the two sectors. It can also strengthen the public’s confidence in data-driven initiatives that seek to improve their communities…(More)”.

From Fragmentation to Coordination: The Case for an Institutional Mechanism for Cross-Border Data Flows


Report by the World Economic Forum: “Digital transformation of the global economy is bringing markets and people closer. Few conveniences of modern life – from international travel to online shopping to cross-border payments – would exist without the free flow of data.

Yet, impediments to free-flowing data are growing. The “Data Free Flow with Trust (DFFT)” concept is based on the idea that responsible data concerns, such as privacy and security, can be addressed without obstructing international data transfers. Policy-makers, trade negotiators and regulators are actively working on this, and while important progress has been made, an effective and trusted international cooperation mechanism would amplify their progress.

This white paper makes the case for establishing such a mechanism with a permanent secretariat, starting with the Group of Seven (G7) member-countries, and ensuring participation of high-level representatives of multiple stakeholder groups, including the private sector, academia and civil society.

This new institution would go beyond short-term fixes and catalyse long-term thinking to operationalize DFFT…(More)”.

DMA: rules for digital gatekeepers to ensure open markets start to apply


Press Release: “The EU Digital Markets Act (DMA) applies from today. Now that the DMA applies, potential gatekeepers that meet the quantitative thresholds established have until 3 July to notify their core platform services to the Commission. ..

The DMA aims to ensure contestable and fair markets in the digital sector. It defines gatekeepers as those large online platforms that provide an important gateway between business users and consumers, whose position can grant them the power to act as a private rule maker, and thus create a bottleneck in the digital economy. To address these issues, the DMA defines a series of specific obligations that gatekeepers will need to respect, including prohibiting them from engaging in certain behaviours in a list of do’s and don’ts. More information is available in the dedicated Q&A…(More)”.

Networks: An Economics Approach


Book by Sanjeev Goyal: “Networks are everywhere: the infrastructure that brings water into our homes, the social networks made up of our friends and families, the supply chains connecting cities, people, and goods. These interconnections contain economic trade-offs: for example, should an airline operate direct flights between cities or route all its flights through a hub? Viewing networks through an economics lens, this textbook considers the costs and benefits that govern their formation and functioning.

Networks are central to an understanding of the production, consumption, and information that lie at the heart of economic activity. Sanjeev Goyal provides advanced undergraduate and graduate students with an accessible and comprehensive introduction to the economics research on networks of the past twenty-five years. Each chapter introduces a theoretical model illustrated with the help of case studies and formal proofs. After introducing the theoretical concepts, Goyal examines economic networks, including infrastructure, security, market power, and financial networks. He then covers social networks, with chapters on coordinating activity, communication and learning, information networks, epidemics, and impersonal markets. Finally, Goyal locates social and economic networks in a broader context covering networked markets, economic development, trust, and group networks in their relation to markets and the state…(More)”.

Valuing the U.S. Data Economy Using Machine Learning and Online Job Postings


Paper by J Bayoán Santiago Calderón and Dylan Rassier: “With the recent proliferation of data collection and uses in the digital economy, the understanding and statistical treatment of data stocks and flows is of interest among compilers and users of national economic accounts. In this paper, we measure the value of own-account data stocks and flows for the U.S. business sector by summing the production costs of data-related activities implicit in occupations. Our method augments the traditional sum-of-costs methodology for measuring other own-account intellectual property products in national economic accounts by proxying occupation-level time-use factors using a machine learning model and the text of online job advertisements (Blackburn 2021). In our experimental estimates, we find that annual current-dollar investment in own-account data assets for the U.S. business sector grew from $84 billion in 2002 to $186 billion in 2021, with an average annual growth rate of 4.2 percent. Cumulative current-dollar investment for the period 2002–2021 was $2.6 trillion. In addition to the annual current-dollar investment, we present historical-cost net stocks, real growth rates, and effects on value-added by the industrial sector…(More)”.