158 research outputs found
Analytical reasoning task reveals limits of social learning in networks
Social learning -by observing and copying others- is a highly successful
cultural mechanism for adaptation, outperforming individual information
acquisition and experience. Here, we investigate social learning in the context
of the uniquely human capacity for reflective, analytical reasoning. A hallmark
of the human mind is our ability to engage analytical reasoning, and suppress
false associative intuitions. Through a set of lab-based network experiments,
we find that social learning fails to propagate this cognitive strategy. When
people make false intuitive conclusions, and are exposed to the analytic output
of their peers, they recognize and adopt this correct output. But they fail to
engage analytical reasoning in similar subsequent tasks. Thus, humans exhibit
an 'unreflective copying bias,' which limits their social learning to the
output, rather than the process, of their peers' reasoning -even when doing so
requires minimal effort and no technical skill. In contrast to much recent work
on observation-based social learning, which emphasizes the propagation of
successful behavior through copying, our findings identify a limit on the power
of social networks in situations that require analytical reasoning
Test Bench Of The Barrel Calorimeter Modules
A systematic procedure to qualify the barrel calorimeter modules is an essential step to guarantee a 0.7% constant term, which is the collaboration objective. The procedure detailed in this note consists of quality monitoring during mechanical assembling and of a set of electrical tests such as electrical continuity, cell and cross-talk capacitance measurement, and high-voltage behaviour. For the whole test, it has been necessary to develop dedicated electronic boards, to develop measurement methods, and the whole operation software. Making the procedure automatic will guarantee the quality of each module during assembling, cabling, and test in liquid argon
Polarized citizen preferences for the ethical allocation of scarce medical resources in 20 countries.
This is the final version. Available from SAGE Publications via the DOI in this record. Data Availability Statement:
Data and code are open and available at https://github.com/
bencebago/ventilators.Objective. When medical resources are scarce, clinicians must make difficult triage decisions. When these decisions affect public trust and morale, as was the case during the COVID-19 pandemic, experts will benefit from knowing which triage metrics have citizen support. Design. We conducted an online survey in 20 countries, comparing support for 5 common metrics (prognosis, age, quality of life, past and future contribution as a health care worker) to a benchmark consisting of support for 2 no-triage mechanisms (first-come-first-served and random allocation). Results. We surveyed nationally representative samples of 1000 citizens in each of Brazil, France, Japan, and the United States and also self-selected samples from 20 countries (total N = 7599) obtained through a citizen science website (the Moral Machine). We computed the support for each metric by comparing its usability to the usability of the 2 no-triage mechanisms. We further analyzed the polarizing nature of each metric by considering its usability among participants who had a preference for no triage. In all countries, preferences were polarized, with the 2 largest groups preferring either no triage or extensive triage using all metrics. Prognosis was the least controversial metric. There was little support for giving priority to healthcare workers. Conclusions. It will be difficult to define triage guidelines that elicit public trust and approval. Given the importance of prognosis in triage protocols, it is reassuring that it is the least controversial metric. Experts will need to prepare strong arguments for other metrics if they wish to preserve public trust and morale during health crises. Highlights: We collected citizen preferences regarding triage decisions about scarce medical resources from 20 countries.We find that citizen preferences are universally polarized.Citizens either prefer no triage (random allocation or first-come-first served) or extensive triage using all common triage metrics, with "prognosis" being the least controversial.Experts will need to prepare strong arguments to preserve or elicit public trust in triage decisions
Slippery slope arguments imply opposition to change
Slippery slope arguments (SSAs) of the form if A, then C describe an initial proposal (A) and a predicted, undesirable consequence of this proposal (C) (e.g., “If cannabis is ever legalized, then eventually cocaine will be legalized, too”). Despite SSAs being a common rhetorical device, there has been surprisingly little empirical research into their subjective evaluation and perception. Here, we present evidence that SSAs are interpreted as a form of consequentialist argument, inviting inferences about the speaker’s (or writer’s) attitudes. Study 1 confirmed the common intuition that a SSA is perceived to be an argument against the initial proposal (A), whereas Study 2 showed that the subjective strength of this inference relates to the subjective undesirability of the predicted consequences (C). Because arguments are rarely made out of context, in Studies 3 and 4 we examined how one important contextual factor, the speaker’s known beliefs, influences the perceived coherence, strength, and persuasiveness of a SSA. Using an unobtrusive dependent variable (eye movements during reading), in Study 3 we showed that readers are sensitive to the internal coherence between a speaker’s beliefs and the implied meaning of the argument. Finally, Study 4 revealed that this degree of internal coherence influences the perceived strength and persuasiveness of the argument. Together, these data indicate that SSAs are treated as a form of negative consequentialist argument. People infer that the speaker of a SSA opposes the initial proposal; therefore, SSAs are only perceived to be persuasive and conversationally relevant when the speaker’s attitudes match this inference
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Ethical principles in machine learning and artificial intelligence: cases from the field and possible ways forward
Decision-making on numerous aspects of our daily lives is being outsourced to machine-learning algorithms and artificial intelligence (AI), motivated by speed and efficiency in the decision process. Machine learning (ML) approaches - one of the typologies of algorithms underpinning artificial intelligence - are typically developed as black boxes. The implication is that ML code scripts are rarely scrutinised; interpretability is usually sacrificed in favour of usability and effectiveness. Room for improvement in practices associated with programme development have also been flagged along other dimensions, including inter alia fairness, accuracy, accountability, and transparency. In this contribution, the production of guidelines and dedicated documents around these themes is discussed. The following applications of AI-driven decision making are outlined: a) Risk assessment in the criminal justice system, and b) autonomous vehicles, highlighting points of friction across ethical principles. Possible ways forward towards the implementation of governance on AI are finally examined
The impact of generative artificial intelligence on socioeconomic inequalities and policy making
Generative artificial intelligence has the potential to both exacerbate and
ameliorate existing socioeconomic inequalities. In this article, we provide a
state-of-the-art interdisciplinary overview of the potential impacts of
generative AI on (mis)information and three information-intensive domains:
work, education, and healthcare. Our goal is to highlight how generative AI
could worsen existing inequalities while illuminating how AI may help mitigate
pervasive social problems. In the information domain, generative AI can
democratize content creation and access, but may dramatically expand the
production and proliferation of misinformation. In the workplace, it can boost
productivity and create new jobs, but the benefits will likely be distributed
unevenly. In education, it offers personalized learning, but may widen the
digital divide. In healthcare, it might improve diagnostics and accessibility,
but could deepen pre-existing inequalities. In each section we cover a specific
topic, evaluate existing research, identify critical gaps, and recommend
research directions, including explicit trade-offs that complicate the
derivation of a priori hypotheses. We conclude with a section highlighting the
role of policymaking to maximize generative AI's potential to reduce
inequalities while mitigating its harmful effects. We discuss strengths and
weaknesses of existing policy frameworks in the European Union, the United
States, and the United Kingdom, observing that each fails to fully confront the
socioeconomic challenges we have identified. We propose several concrete
policies that could promote shared prosperity through the advancement of
generative AI. This article emphasizes the need for interdisciplinary
collaborations to understand and address the complex challenges of generative
AI.Comment: PNAS Nexus, in pres
Changing the culture of assessment: the dominance of the summative assessment paradigm
Background
Despite growing evidence of the benefits of including assessment for learning strategies within programmes of assessment, practical implementation of these approaches is often problematical. Organisational culture change is often hindered by personal and collective beliefs which encourage adherence to the existing organisational paradigm. We aimed to explore how these beliefs influenced proposals to redesign a summative assessment culture in order to improve students’ use of assessment-related feedback.
Methods
Using the principles of participatory design, a mixed group comprising medical students, clinical teachers and senior faculty members was challenged to develop radical solutions to improve the use of post-assessment feedback. Follow-up interviews were conducted with individual members of the group to explore their personal beliefs about the proposed redesign. Data were analysed using a socio-cultural lens.
Results
Proposed changes were dominated by a shared belief in the primacy of the summative assessment paradigm, which prevented radical redesign solutions from being accepted by group members. Participants’ prior assessment experiences strongly influenced proposals for change. As participants had largely only experienced a summative assessment culture, they found it difficult to conceptualise radical change in the assessment culture. Although all group members participated, students were less successful at persuading the group to adopt their ideas. Faculty members and clinical teachers often used indirect techniques to close down discussions. The strength of individual beliefs became more apparent in the follow-up interviews.
Conclusions
Naïve epistemologies and prior personal experiences were influential in the assessment redesign but were usually not expressed explicitly in a group setting, perhaps because of cultural conventions of politeness. In order to successfully implement a change in assessment culture, firmly-held intuitive beliefs about summative assessment will need to be clearly understood as a first step
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