Workforce ecosystems and AI

Curated on Posted on by Stefaan Verhulst

Report by David Kiron, Elizabeth J. Altman, and Christoph Riedl: “Companies increasingly rely on an extended workforce (e.g., contractors, gig workers, professional service firms, complementor organizations, and technologies such as algorithmic management and artificial intelligence) to achieve strategic goals and objectives. When we ask leaders to describe how they define their workforce today, they mention a diverse array of participants, beyond just full- and part-time employees, all contributing in various ways. Many of these leaders observe that their extended workforce now comprises 30-50% of their entire workforce. For example, Novartis has approximately 100,000 employees and counts more than 50,000 other workers as external contributors. Businesses are also increasingly using crowdsourcing platforms to engage external participants in the development of products and services. Managers are thinking about their workforce in terms of who contributes to outcomes, not just by workers’ employment arrangements.

Our ongoing research on workforce ecosystems demonstrates that managing work across organizational boundaries with groups of interdependent actors in a variety of employment relationships creates new opportunities and risks for both workers and businesses. These are not subtle shifts. We define a workforce ecosystem as:

A structure that encompasses actors, from within the organization and beyond, working to create value for an organization. Within the ecosystem, actors work toward individual and collective goals with interdependencies and complementarities among the participants.

The emergence of workforce ecosystems has implications for management theory, organizational behavior, social welfare, and policymakers. In particular, issues surrounding work and worker flexibility, equity, and data governance and transparency pose substantial opportunities for policymaking.

At the same time, artificial intelligence (AI)—which we define broadly to include machine learning and algorithmic management—is playing an increasingly large role within the corporate context. The widespread use of AI is already displacing workers through automation, augmenting human performance at work, and creating new job categories…(More)”.

The Surveillance Ad Model Is Toxic — Let’s Not Install Something Worse

Curated on Posted on by Stefaan Verhulst

Article by Elizabeth M. Renieris: “At this stage, law and policy makers, civil society and academic researchers largely agree that the existing business model of the Web — algorithmically targeted behavioural advertising based on personal data, sometimes also referred to as surveillance advertising — is toxic. They blame it for everything from the erosion of individual privacy to the breakdown of democracy. Efforts to address this toxicity have largely focused on a flurry of new laws (and legislative proposals) requiring enhanced notice to, and consent from, users and limiting the sharing or sale of personal data by third parties and data brokers, as well as the application of existing laws to challenge ad-targeting practices.

In response to the changing regulatory landscape and zeitgeist, industry is also adjusting its practices. For example, Google has introduced its Privacy Sandbox, a project that includes a planned phaseout of third-party cookies from its Chrome browser — a move that, although lagging behind other browsers, is nonetheless significant given Google’s market share. And Apple has arguably dealt one of the biggest blows to the existing paradigm with the introduction of its AppTrackingTransparency (ATT) tool, which requires apps to obtain specific, opt-in consent from iPhone users before collecting and sharing their data for tracking purposes. The ATT effectively prevents apps from collecting a user’s Identifier for Advertisers, or IDFA, which is a unique Apple identifier that allows companies to recognize a user’s device and track its activity across apps and websites.

But the shift away from third-party cookies on the Web and third-party tracking of mobile device identifiers does not equate to the end of tracking or even targeted ads; it just changes who is doing the tracking or targeting and how they go about it. Specifically, it doesn’t provide any privacy protections from first parties, who are more likely to be hegemonic platforms with the most user data. The large walled gardens of Apple, Google and Meta will be less impacted than smaller players with limited first-party data at their disposal…(More)”.

Innovating Democracy? The Means and Ends of Citizen Participation in Latin America

Curated on Posted on by Stefaan Verhulst

Book by Thamy Pogrebinschi: “Since democratization, Latin America has experienced a surge in new forms of citizen participation. Yet there is still little comparative knowledge on these so-called democratic innovations. This Element seeks to fill this gap. Drawing on a new dataset with 3,744 cases from 18 countries between 1990 and 2020, it presents the first large-N cross-country study of democratic innovations to date. It also introduces a typology of twenty kinds of democratic innovations, which are based on four means of participation, namely deliberation, citizen representation, digital engagement, and direct voting. Adopting a pragmatist, problem-driven approach, this Element claims that democratic innovations seek to enhance democracy by addressing public problems through combinations of those four means of participation in pursuit of one or more of five ends of innovations, namely accountability, responsiveness, rule of law, social equality, and political inclusion…(More)”.

MAPLE: The Massachusetts Platform for Legislative Engagement

Curated on Posted on by Stefaan Verhulst

About: “MAPLE seeks to better connect its constituents to one another, and to our legislators. We hope to create a space for you to meaningfully engage in state government, learn about proposed legislation that impacts our lives in the Commonwealth, and share your expertise and stories. MAPLE aims to meaningfully channel and focus your civic energy towards productive actions for our state and local communities.

Today, there is no legal obligation for the MA legislature (formally known as “The General Court”) to disclose what written testimony they receive and, in practice, such disclosure very rarely happens. As a result, it can be difficult to understand what communications and perspectives are informing our legislators’ decisions. Often, even members of the legislature cannot easily access the public testimony given on a bill.

When you submit testimony via the MAPLE platform, you can publish it in a freely accessible online database (this website) so that all other stakeholders can read your perspective. We also help you find the right recipients in the legislature for your testimony, and prepare the email for you to send.

We hope this will help foster a greater capacity and means for self-governance and lead to better policy outcomes, with greater alignment to the needs, values, and objectives of the population of Massachusetts. While you certainly do not have to submit testimony via this website, we hope you will. Every piece of testimony published , and allows more people to gain from your knowledge and experience…(More)”.

Slow-governance in smart cities: An empirical study of smart intersection implementation in four US college towns

Curated on Posted on by Stefaan Verhulst

Paper by Madelyn Rose Sanfilippo and Brett Frischmann: “Cities cannot adopt supposedly smart technological systems and protect human rights without developing appropriate data governance, because technologies are not value-neutral. This paper proposes a deliberative, slow-governance approach to smart tech in cities. Inspired by the Governing Knowledge Commons (GKC) framework and past case studies, we empirically analyse the adoption of smart intersection technologies in four US college towns to evaluate and extend knowledge commons governance approaches to address human rights concerns. Our proposal consists of a set of questions that should guide community decision-making, extending the GKC framework via an incorporation of human-rights impact assessments and a consideration of capabilities approaches to human rights. We argue that such a deliberative, slow-governance approach enables adaptation to local norms and more appropriate community governance of smart tech in cities. By asking and answering key questions throughout smart city planning, procurement, implementation and management processes, cities can respect human rights, interests and expectations…(More)”.

Institutional review boards need new skills to review data sharing and management plans

Curated on Posted on by Stefaan Verhulst

Article by Vasiliki Rahimzadeh, Kimberley Serpico & Luke Gelinas: “New federal rules require researchers to submit plans for how to manage and share their scientific data, but institutional ethics boards may be underprepared to review them.

Data sharing is widely considered a conduit to scientific progress, the benefits of which should return to individuals and communities who invested in that science. This is the central premise underpinning changes recently announcement by the US Office of Science Technology and Policy (OSTP)1 on sharing and managing data generated from federally funded research. Researchers will now be required to make publicly accessible any scholarly publications stemming from their federally funded research, as well as supporting data, according to the OSTP announcement. However, the attendant risks to individuals’ privacy-related interests and the increasing threat of community-based harms remain barriers to fostering a trustworthy ecosystem of biomedical data science.

Institutional review boards (IRBs) are responsible for ensuring protections for all human participants engaged in research, but they rarely include members with specialized expertise needed to effectively minimize data privacy and security risks. IRBs must be prepared to meet these review demands given the new data sharing policy changes. They will need additional resources to conduct quality and effective reviews of data management and sharing (DMS) plans. Practical ways forward include expanding IRB membership, proactively consulting with researchers, and creating new research compliance resources. This Comment will focus on data management and sharing oversight by IRBs in the US, but the globalization of data science research underscores the need for enhancing similar review capacities in data privacy, management and security worldwide…(More)”.

The Rule of Law

Curated on Posted on by Stefaan Verhulst

Paper by Cass R. Sunstein: “The concept of the rule of law is invoked for purposes that are both numerous and diverse, and that concept is often said to overlap with, or to require, an assortment of other practices and ideals, including democracy, free elections, free markets, property rights, and freedom of speech. It is best to understand the concept in a more specific way, with a commitment to seven principles: (1) clear, general, publicly accessible rules laid down in advance; (2) prospectivity rather than retroactivity; (3) conformity between law on the books and law in the world; (4) hearing rights; (5) some degree of separation between (a) law-making and law enforcement and (b) interpretation of law; (6) no unduly rapid changes in the law; and (7) no contradictions or palpable inconsistency in the law. This account of the rule of law conflicts with those offered by (among many others) Friedrich Hayek and Morton Horwitz, who conflate the idea with other, quite different ideas and practices. Of course it is true that the seven principles can be specified in different ways, broadly compatible with the goal of describing the rule of law as a distinct concept, and some of the seven principles might be understood to be more fundamental than others…(More)”.

No Ground Truth? No Problem: Improving Administrative Data Linking Using Active Learning and a Little Bit of Guile

Curated on Posted on by Stefaan Verhulst

Paper by Sarah Tahamont et al: “While linking records across large administrative datasets [“big data”] has the potential to revolutionize empirical social science research, many administrative data files do not have common identifiers and are thus not designed to be linked to others. To address this problem, researchers have developed probabilistic record linkage algorithms which use statistical patterns in identifying characteristics to perform linking tasks. Naturally, the accuracy of a candidate linking algorithm can be substantially improved when an algorithm has access to “ground-truth” examples — matches which can be validated using institutional knowledge or auxiliary data. Unfortunately, the cost of obtaining these examples is typically high, often requiring a researcher to manually review pairs of records in order to make an informed judgement about whether they are a match. When a pool of ground-truth information is unavailable, researchers can use “active learning” algorithms for linking, which ask the user to provide ground-truth information for select candidate pairs. In this paper, we investigate the value of providing ground-truth examples via active learning for linking performance. We confirm popular intuition that data linking can be dramatically improved with the availability of ground truth examples. But critically, in many real-world applications, only a relatively small number of tactically-selected ground-truth examples are needed to obtain most of the achievable gains. With a modest investment in ground truth, researchers can approximate the performance of a supervised learning algorithm that has access to a large database of ground truth examples using a readily available off-the-shelf tool…(More)”.

The NIST Trustworthy and Responsible Artificial Intelligence Resource Center

Curated on Posted on by Stefaan Verhulst

About: “The NIST Trustworthy and Responsible Artificial Intelligence Resource Center (AIRC) is a platform to support people and organizations in government, industry, and academia—both in the U.S. and internationally—driving technical and scientific innovation in AI. It serves as a one-stop-shop for foundational content, technical documents, and AI toolkits such as repository hub for standards, measurement methods and metrics, and data sets. It also provides a common forum for all AI actors to engage and collaborate in the development and deployment of trustworthy and responsible AI technologies that benefit all people in a fair and equitable manner.

The NIST AIRC is developed to support and operationalize the NIST AI Risk Management Framework (AI RMF 1.0) and its accompanying playbook. To match the complexity of AI technology, the AIRC will grow over time to provide an engaging interactive space that enables stakeholders to share AI RMF case studies and profiles, educational materials and technical guidance related to AI risk management.

The initial release of the AIRC (airc.nist.gov) provides access to the foundational content, including the AI RMF 1.0, the playbook, and a trustworthy and responsible AI glossary. It is anticipated that in the coming months enhancements to the AIRC will include structured access to relevant technical and policy documents; access to a standards hub that connects various standards promoted around the globe; a metrics hub to assist in test, evaluation, verification, and validation of AI; as well as software tools, resources and guidance that promote trustworthy and responsible AI development and use. Visitors to the AIRC will be able to tailor the above content they see based on their requirements (organizational role, area of expertise, etc.).

Over time the Trustworthy and Responsible AI Resource Center will enable distribution of stakeholder produced content, case studies, and educational materials…(More)”.

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

Curated on Posted on by Stefaan Verhulst

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)”.