Laurin B. Weissinger’s research while affiliated with Tufts University and other places

What is this page?


This page lists works of an author who doesn't have a ResearchGate profile or hasn't added the works to their profile yet. It is automatically generated from public (personal) data to further our legitimate goal of comprehensive and accurate scientific recordkeeping. If you are this author and want this page removed, please let us know.

Publications (6)


2022 Viehoff_AI and Political Community_OUP_SUBMITTED.pdf
  • Data
  • File available

April 2023

·

111 Reads

·

·

·

[...]

·

Roel I. J. Dobbe
Download

The Oxford Handbook of AI Governance

February 2022

·

1,279 Reads

·

125 Citations

This handbook is currently in development, with individual articles publishing online in advance of print publication. At this time, we cannot add information about unpublished articles in this handbook, however the table of contents will continue to grow as additional articles pass through the review process and are added to the site. Please note that the online publication date for this handbook is the date that the first article in the title was published online. For more information, please read the site FAQs.


AI, Complexity, and Regulation

February 2022

·

26 Reads

·

3 Citations

Regulating and governing AI will remain a challenge due to the inherent intricacy of how AI is deployed and used in practice. Regulation effectiveness and efficiency are inversely proportional to system complexity and the clarity of objectives: the more complicated an area is and the harder objectives are to operationalize, the more difficult it is to regulate and govern. Safety regulations, while often concerned with complex systems like airplanes, benefit from measurable, clear objectives and uniform subsystems. AI has emergent properties and is not just “a technology.” It is interwoven with organizations, people, and the wider social context. Furthermore, objectives like “fairness” are not only difficult to grasp and classify, but they will change their meaning case-by-case. The inherent complexity of AI systems will continue to complicate regulation and governance; however, with appropriate investment, monetary and otherwise, complexity can be tackled successfully. Due to the considerable power imbalance between users of AI in comparison to those AI systems are used on, successful regulation might be difficult to create and enforce. As such, AI regulation is more of a political and socio-economic problem than a technical one.


Trusted sources and institutions, broken down by gender
Figure ED1 shows histograms of sources and institutions that respondents say they would trust most to help them decide whether or not to take the COVID-19 vaccine. Respondents were only permitted to select one most trusted source or institution. Responses are broken down by gender of respondent.
Average vaccine acceptance across all LMIC countries leaving one or two study samples out
Figure ED2 shows distribution of estimates of average acceptance for all studies in LMICs (excluding USA and Russia) leaving one and two study samples out at a time. Figure also shows distributions of subgroup averages by gender, education and age leaving one and two study samples out at a time. To directly compare the resulting distributions to the estimates reported in Fig. 1, we plot point estimates reported in Fig. 1 for all LMIC studies, Russia and the US.
Acceptance rates, overall and by respondent characteristics
Average acceptance of the COVID-19 vaccine across studies and subgroups within studies. For each study, we summarize sampling information in parentheses in the following way: (1) we indicate whether the geographic coverage of the sample is national or subnational. If the coverage is subnational we provide further details; (2) we list the number of observations included in the study. In the plot, points represent the estimated percentage of individuals who would take the vaccine. ‘No’, ‘Don’t know’ and ‘Refuse’ are taken as a single reference category. Bars around each point indicate a 95% confidence interval for the estimate. The ‘All LMICs (national samples)’ row reports averages for just the LMIC samples with national-level geographic coverage. An estimate of average acceptance for all studies in LMICs (excluding the United States and Russia) is also shown in the ‘All LMICs’ row.
Reasons not to take the vaccine
The percentage of respondents mentioning reasons why they would not take the COVID-19 vaccine. In the plot, points represent the estimated percentage of individuals that would not take the vaccine or do not know if they would take the vaccine for each possible response option. Bars around each point indicate the 95% CI for the estimate. An estimated average for all studies in LMICs is also shown. The size of the points illustrates the number of observations in each response option. The India and Pakistan survey 2 studies are not included because they either did not include the question or were not properly harmonized with the other studies.
Trusted sources respondents say they would trust most to help them decide whether to take the COVID-19 vaccine
Histograms of sources respondents say they would trust most to help them decide whether to take the COVID-19 vaccine. Respondents were only permitted to select one most trusted actor or institution. The India, Mozambique, Pakistan survey 1, Pakistan survey 2 and Uganda survey 1 studies are not included because they either did not include the question or were not properly harmonized with the other studies.
COVID-19 vaccine acceptance and hesitancy in low- and middle-income countries

August 2021

·

704 Reads

·

1,005 Citations

Nature Medicine

Widespread acceptance of COVID-19 vaccines is crucial for achieving sufficient immunization coverage to end the global pandemic, yet few studies have investigated COVID-19 vaccination attitudes in lower-income countries, where large-scale vaccination is just beginning. We analyze COVID-19 vaccine acceptance across 15 survey samples covering 10 low- and middle-income countries (LMICs) in Asia, Africa and South America, Russia (an upper-middle-income country) and the United States, including a total of 44,260 individuals. We find considerably higher willingness to take a COVID-19 vaccine in our LMIC samples (mean 80.3%; median 78%; range 30.1 percentage points) compared with the United States (mean 64.6%) and Russia (mean 30.4%). Vaccine acceptance in LMICs is primarily explained by an interest in personal protection against COVID-19, while concern about side effects is the most common reason for hesitancy. Health workers are the most trusted sources of guidance about COVID-19 vaccines. Evidence from this sample of LMICs suggests that prioritizing vaccine distribution to the Global South should yield high returns in advancing global immunization coverage. Vaccination campaigns should focus on translating the high levels of stated acceptance into actual uptake. Messages highlighting vaccine efficacy and safety, delivered by healthcare workers, could be effective for addressing any remaining hesitancy in the analyzed LMICs. Survey data collected across ten low-income and middle-income countries (LMICs) in Asia, Africa and South America compared with surveys from Russia and the United States reveal heterogeneity in vaccine confidence in LMICs, with healthcare providers being trusted sources of information, as well as greater levels of vaccine acceptance in these countries than in Russia and the United States.


COVID-19 Vaccine Acceptance and Hesitancy in Low and Middle Income Countries, and Implications for Messaging

March 2021

·

615 Reads

·

53 Citations

Background As vaccination campaigns are deployed worldwide, addressing vaccine hesitancy is of critical importance to ensure sufficient immunization coverage. We analyzed COVID-19 vaccine acceptance across 15 samples covering ten low- and middle- income countries (LMICs) in Asia, Africa, and South America, and two higher income countries (Russia and the United States). Methods Standardized survey responses were collected from 45,928 individuals between June 2020 and January 2021. We estimate vaccine acceptance with robust standard errors clustered at the study level. We analyze stated reasons for vaccine acceptance and hesitancy, and the most trusted sources for advice on vaccination, and we disaggregate acceptance rates by gender, age, and education level. Findings We document willingness to take a COVID-19 vaccine across LMIC samples, ranging from 67% (Burkina Faso) to 97% (Nepal). Willingness was considerably higher in LMICs (80%) than in the United States (65%) and Russia (30%). Vaccine acceptance was primarily explained by an interest in personal protection against the disease (91%). Concern about side effects (40%) was the most common reason for reluctance. Health workers were considered the most trusted sources of information about COVID-19 vaccines. Interpretation Given high levels of stated willingness to accept a COVID-19 vaccine across LMIC samples, our study suggests that prioritizing efficient and equitable vaccine distribution to LMICs will yield high returns in promoting immunization on a global scale. Messaging and other community-level interventions in these contexts should be designed to help translate intentions into uptake, and emphasize safety and efficacy. Trusted health workers are ideally positioned to deliver these messages.


Building robust and ethical vaccination verification systems

December 2020

·

17 Reads

·

3 Citations

As countries begin to vaccinate their populations against COVID-19, creating systems to verify vaccine records will be vital to reopening businesses, educational institutions, and travel. We consider the challenges of building vaccine record verification (VRV) systems that involve data sharing by health care providers, methods for verifying vaccine records, and regulation of how entities (e.g., workplaces, schools, businesses, and airlines) may request proof of vaccination. In particular, we focus on the opportunities and risks associated with digital vaccine passport apps. We propose three ethical principles to guide the building of VRV systems: 1) aligning systems with vaccine prioritization, 2) upholding fairness and equity, and 3) building trustworthy technology that protects the public's health data.

Citations (5)


... This concept of provisioning a text-based set of rules to be narrowly interpreted by an AI agent for training and selfevaluation emerged as an AI safety approach in relation to LLMs; which are a subset of AI agent created from Natural Language Processing techniques (Bai et al., 2022;Durmus et al., 2023). AI governance refers to the ecosystem of norms, markets, and institutions that shape how AI is built and deployed, as well as the policy and research required to maintain it, in line with human interests (Bullock et al., 2024;Dafoe, 2024). However, the constitutional approach to AI governance is limiting, as it draws on understandings of a constitution as a closed system of norms or unifying ideals that are expressed in plain language and employed as a governance mechanism. ...

Reference:

AI as a constituted system: accountability lessons from an LLM experiment
The Oxford Handbook of AI Governance
  • Citing Article
  • February 2022

... This makes AI a multifaceted sociotechnical process, with humans, machines, algorithms, and data playing integral roles as fundamental components (Peng et al., 2021). The sociotechnical attributes of AI systems introduce complexities with AI development and implementation for sustainability advances in nations involving an interplay between technological developments, individuals, government responses, and environmental dynamics (Cesareo & White, 2023;Vinuesa et al., 2020;Weissinger, 2021). ...

AI, Complexity, and Regulation
  • Citing Article
  • February 2022

... Additionally, the present study found that participants with a probability of having a previous COVID-19 infection had lower confidence in the COVID-19 vaccine than those who had not been previously infected. After studying many low-and middle-income countries, Solís Arce et al. [34] revealed that the main cause of vaccine acceptance was the hope of people to protect themselves from COVID-19 infection. In contrast, the fear of side effects of vaccines was the major cause of vaccine rejection. ...

COVID-19 vaccine acceptance and hesitancy in low- and middle-income countries

Nature Medicine

... 52 Interestingly, research involving 10 LMICs has shown that individuals in these countries are more likely to accept COVID-19 vaccines (on average 80%) than those in the United States (65%) or Russia (30%). 53 Also, based on the findings from a study that evaluates the willingness to be vaccinated in all the Eight regions in Bangladesh among 3646 participants based on a household survey revealed that 74.6% of respondents indicated their acceptance to be vaccinated, 54 but vaccine hesitancy was higher among the rural, semiurban, slum, elderly, and low-educated populations. 54 In another study conducted in Ghana among the 2734 people in all the 16 regions of the country found that 82.8% of respondents were willing to take a COVID-19 vaccine, while 9.7% were hesitant. ...

COVID-19 Vaccine Acceptance and Hesitancy in Low and Middle Income Countries, and Implications for Messaging

... Particularly with the wider acceptance and use of vaccination passports in the COVID-19 pandemic and post-pandemic, gaining consumers' approval and trust in THOs business practices becomes paramount. These organisations should consider travellers' concerns regarding privacy violations, government/private surveillance, the potential abuse of data collected and consumers' distrust in vaccination passport apps (Zhang et al., 2021). To increase consumers' trust THOs should implement strong internal controls and safeguards to prevent staff from accessing any personal data without ethical process (e.g. ...

Building robust and ethical vaccination verification systems
  • Citing Preprint
  • December 2020