AI in Hiring and Evaluating Workers: What Americans Think

Curated on Posted on by Stefaan Verhulst

Pew Research Center survey: “… finds crosscurrents in the public’s opinions as they look at the possible uses of AI in workplaces. Americans are wary and sometimes worried. For instance, they oppose AI use in making final hiring decisions by a 71%-7% margin, and a majority also opposes AI analysis being used in making firing decisions. Pluralities oppose AI use in reviewing job applications and in determining whether a worker should be promoted. Beyond that, majorities do not support the idea of AI systems being used to track workers’ movements while they are at work or keeping track of when office workers are at their desks.

Yet there are instances where people think AI in workplaces would do better than humans. For example, 47% think AI would do better than humans at evaluating all job applicants in the same way, while a much smaller share – 15% – believe AI would be worse than humans in doing that. And among those who believe that bias along racial and ethnic lines is a problem in performance evaluations generally, more believe that greater use of AI by employers would make things better rather than worse in the hiring and worker-evaluation process. 

Overall, larger shares of Americans than not believe AI use in workplaces will significantly affect workers in general, but far fewer believe the use of AI in those places will have a major impact on them personally. Some 62% think the use of AI in the workplace will have a major impact on workers generally over the next 20 years. On the other hand, just 28% believe the use of AI will have a major impact on them personally, while roughly half believe there will be no impact on them or that the impact will be minor…(More)”.

End of data sharing could make Covid-19 harder to control, experts and high-risk patients warn

Curated on Posted on by Stefaan Verhulst

Article by Sam Whitehead: “…The federal government’s public health emergency that’s been in effect since January 2020 expires May 11. The emergency declaration allowed for sweeping changes in the U.S. health care system, like requiring state and local health departments, hospitals, and commercial labs to regularly share data with federal officials.

But some shared data requirements will come to an end and the federal government will lose access to key metrics as a skeptical Congress seems unlikely to grant agencies additional powers. And private projects, like those from The New York Times and Johns Hopkins University, which made covid data understandable and useful for everyday people, stopped collecting data in March.

Public health legal scholars, data experts, former and current federal officials, and patients at high risk of severe covid outcomes worry the scaling back of data access could make it harder to control covid.

There have been improvements in recent years, such as major investments in public health infrastructure and updated data reporting requirements in some states. But concerns remain that the overall shambolic state of U.S. public health data infrastructure could hobble the response to any future threats.

“We’re all less safe when there’s not the national amassing of this information in a timely and coherent way,” said Anne Schuchat, former principal deputy director of the Centers for Disease Control and Prevention.

A lack of data in the early days of the pandemic left federal officials, like Schuchat, with an unclear picture of the rapidly spreading coronavirus. And even as the public health emergency opened the door for data-sharing, the CDC labored for months to expand its authority.

Eventually, more than a year into the pandemic, the CDC gained access to data from private health care settings, such as hospitals and nursing homes, commercial labs, and state and local health departments…(More)”. See also: Why we still need data to understand the COVID-19 pandemic

Why we need applied humanities approaches

Curated on Posted on by Stefaan Verhulst

Article by Kathryn Strong Hansen: “Since the term “applied humanities” is not especially common, some explanation may be helpful. Applied humanities education prepares students to use humanities knowledge and methods in practice rather than only in theory. As the University of Arizona’s Department of Public and Applied Humanities puts it, the goal is “public enrichment and the direct and tangible improvement of the human condition.” While this goal undoubtedly involves “intrahumanities” outputs like museum and exhibit curation or textual editing, public enrichment through the humanities can also be pursued through science and engineering curricula.

The direct goal of much science education is improving the human condition, such as CRISPR developments opening up possibilities for gene therapies. Similarly, good engineering seeks to improve the human condition, like the LEED-certified building methods that minimize negative impacts on the environment.

Since the humanities concern themselves with the human experience in all its facets, they can offer much to STEM endeavors, and applied humanities approaches have been implemented for many decades. One of the most established applied humanities pursuits is applied linguistics, which has existed as a field of study since about 1948. Another useful and growing example is that of the medical humanities, which provide medical practitioners with training that can help them interact more effectively with patients and navigate the emotional impact of their profession.

While applied approaches might be less widespread or established in other humanities fields, they are just as needed. In part, they are needed because the skills and knowledge of humanities scholars can help students in a multiplicity of fields, including STEM disciplines, to improve their understanding of their subject matter and how it connects to society at large…(More)”.

Behavioral Economics: Policy Impact and Future Directions

Curated on Posted on by Stefaan Verhulst

Report from the National Academies of Sciences, Engineering, and Medicine: “Behavioral economics – a field based in collaborations among economists and psychologists – focuses on integrating a nuanced understanding of behavior into models of decision-making. Since the mid-20th century, this growing field has produced research in numerous domains and has influenced policymaking, research, and marketing. However, little has been done to assess these contributions and review evidence of their use in the policy arena.

Behavioral Economics: Policy Impact and Future Directions examines the evidence for behavioral economics and its application in six public policy domains: health, retirement benefits, climate change, social safety net benefits, climate change, education, and criminal justice. The report concludes that the principles of behavioral economics are indispensable for the design of policy and recommends integrating behavioral specialists into policy development within government units. In addition, the report calls for strengthening research methodology and identifies research priorities for building on the accomplishments of the field to date…(More)”.

A Guide to Adaptive Government: Preparing for Disruption

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Report by Nicholas D. Evans: “With disruption now the norm rather than the exception, governments need to rethink business as usual and prepare for business as disrupted.

Government executives and managers should plan for continuous disruption and for how their agencies and departments will operate under continuous turbulence and change. In 2022 alone, the world witnessed war in Ukraine, the continuing effects of the COVID-19 pandemic, and natural disasters such as Hurricane Ian—not to mention energy scarcity, supply chain shortages, the start of a global recession, record highs for inflation, and rising interest rates.

Traditional business continuity and disaster recovery playbooks and many other such earlier approaches—born when disruption was the exception—are no longer sufficient. Rather than operating “business as usual,” government agencies and departments now must plan and operate for “business as disrupted.” One other major pivot point: when these disruptions happen, such as COVID, they bring an opportunity to drive a long awaited or postponed transformation. It is about leveraging that opportunity for change and not simply returning to the status quo The impact to supply chains during the COVID-19 pandemic and recovery illustrates this insight…

Evans recognizes the importance of pursuing agile principles as foundational in realizing the vision of adaptive government described in this report. Agile government principles serve as a powerful foundation for building “intrinsic agility,” since they encourage key cultural, behavioral, and growth mindset approaches to embed agility and adaptability into organizational norms and processes. Many of the insights, guidance, and recommendations offered in this report complement work pursued by the Agile Government Center (AGC), led by the National Academy of Public Administration in collaboration with our Center, and spearheaded by NAPA Fellow and Center Executive Fellow Ed DeSeve.

This report illustrates the strategic significance of adaptability to government organizations today. The author offers new strategies, techniques, and tools to accelerate digital transformation, and better position government agencies to respond to the next wave of both opportunities and disruptive threats—similar to what our Center, NAPA, and partner organizations refer to as “future shocks.” Adaptability as a core competency can support both innovation and risk management, helping governments to optimize for ever-changing mission needs and ambient conditions Adaptability represents a powerful enabler for modern government and enterprise organizations.

We hope that this report helps government leaders, academic experts, and other stakeholders to infuse adaptive thinking throughout the public sector, leading to more effective operations, better outcomes, and improved performance in a world where the only constant seems to be the inevitability of change and disruption…(More)”.

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)”.

MAPLE: The Massachusetts Platform for Legislative Engagement

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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)”.