Hasil untuk "Labor policy. Labor and the state"

Theresa Züger, Laura State, Lena Winter

The overall rapid increase of artificial intelligence (AI) use is linked to various initiatives that propose AI 'for good'. However, there is a lack of transparency in the goals of such projects, as well as a missing evaluation of their actual impacts on society and the planet. We close this gap by proposing public interest and sustainability as a regulatory dual-concept, together creating the necessary framework for a just and sustainable development that can be operationalized and utilized for the assessment of AI systems. Based on this framework, and building on existing work in auditing, we introduce the Impact-AI-method, a qualitative audit method to evaluate concrete AI projects with respect to public interest and sustainability. The interview-based method captures a project's governance structure, its theory of change, AI model and data characteristics, and social, environmental, and economic impacts. We also propose a catalog of assessment criteria to rate the outcome of the audit as well as to create an accessible output that can be debated broadly by civil society. The Impact-AI-method, developed in a transdisciplinary research setting together with NGOs and a multi-stakeholder research council, is intended as a reusable blueprint that both informs public debate about AI 'for good' claims and supports the creation of transparency of AI systems that purport to contribute to a just and sustainable development.

Muhammad Alif RIfky , Anggia Utami Dewi , Wawan Budi Darmawan

This article examines Indonesia's One Channel System (OCS) as a strategic labor migration policy. Grounded in a qualitative content analysis of policy documents and bilateral agreements, the study moves beyond a descriptive account to offer a critical evaluation of the OCS. It utilizes Michael G. Roskin’s theory of National Interest as a foundational framework for understanding the state’s motivations. Still, it enriches this with complementary lenses from the International Relations literature, including migration governance, labor diplomacy, and human security. The findings affirm that the OCS serves as a key instrument of Indonesian statecraft, advancing national security, securing economic interests through remittance formalization, and enhancing international prestige. However, the analysis reveals that significant challenges severely constrain the policy’s effectiveness. These include a persistent lack of bilateral cooperation from Malaysia, critical on-the-ground implementation gaps, and the unintended risk of state overreach. A fundamental disconnect between the policy’s top-down objectives and the lived realities and agency of migrant workers. The study concludes that while the OCS is a vital assertion of regulatory sovereignty, its success is contingent on bridging the gap between state-centric interests and worker-centric protection. It suggests that future policy must prioritize legally binding bilateral enforcement and address deep-rooted implementation failures.

Arno Geimer, Beltran Fiz Pontiveros, Radu State

Federated Learning (FL) is a collaborative machine learning paradigm which allows participants to collectively train a model while training data remains private. This paradigm is especially beneficial for sectors like finance, where data privacy, security and model performance are paramount. FL has been extensively studied in the years following its introduction, leading to, among others, better performing collaboration techniques, ways to defend against other clients trying to attack the model, and contribution assessment methods. An important element in for-profit Federated Learning is the development of incentive methods to determine the allocation and distribution of rewards for participants. While numerous methods for allocation have been proposed and thoroughly explored, distribution frameworks remain relatively understudied. In this paper, we propose a novel framework which introduces client-specific tokens as investment vehicles within the FL ecosystem. Our framework aims to address the limitations of existing incentive schemes by leveraging a decentralized finance (DeFi) platform and automated market makers (AMMs) to create a more flexible and scalable reward distribution system for participants, and a mechanism for third parties to invest in the federation learning process.

Dongdong Li, Jiuxiang Dong

This note proposes a data-driven output-feedback stabilizing policy iteration for unknown linear discrete-time systems with unmeasurable states. Existing policy iteration methods for optimal control must start from a stabilizing control policy, which is particularly challenging to obtain for unknown systems, especially when states are unavailable. In such cases, it is more difficult to guarantee stability and convergence performance. To address this problem, an output-feedback stabilizing policy iteration framework is developed to learn closed-loop stabilizing control policies while ensuring convergence performance. Specifically, cumulative scalar parameters are introduced to compress the original system to a stable scale. Then, by integrating modified policy iteration with parameter update rules, the system is gradually amplified/restored to the original system while preserving stability such that the stabilizing control policy is obtained. The entire process is driven solely by input-output data. Moreover, a stability analysis is provided for output-feedback. The proposed approach is validated by simulations.

Kubilay Çakıcı, Enes Atay

The population of Türkiye and the world is getting older every day. In recent years, policies have been developed to support healthy and harmonious living in urbanizing and ageing areas. This study focuses on age-friendly practices in Turkish municipalities that are part of the World Health Organization Global Network of Age-Friendly Cities and Communities. The mission of the Network is to ensure the sharing and dissemination of best practices and experiences. In line with this mission, it aims to support cities in becoming age-friendly. Six municipalities from Türkiye, including one metropolitan municipality and five metropolitan district municipalities, are part of the Network. These six municipalities constitute the sample of the study. A qualitative method was used in the study. We accessed activity reports from 2020-2023 and strategic plans for 2020-2024 from the corporate websites of the municipalities. We analyzed examples of age-friendly city practices of the municipalities included in the network using the content analysis method. The study concluded that municipalities have started to prioritize age-friendly practices, especially in recent years, and have diversified their practices in this direction. However, activities related to the sub-themes of the age-friendly city concept primarily focus on social support and health services.

Jane Hsieh, Angie Zhang, Mialy Rasetarinera et al.

The proliferating adoption of platform-based gig work increasingly raises concerns for worker conditions. Past studies documented how platforms leveraged design to exploit labor, withheld information to generate power asymmetries, and left workers alone to manage logistical overheads as well as social isolation. However, researchers also called attention to the potential of helping workers overcome such costs via worker-led datasharing, which can enable collective actions and mutual aid among workers, while offering advocates, lawmakers and regulatory bodies insights for improving work conditions. To understand stakeholders' desiderata for a data-sharing system (i.e. functionality and policy initiatives that it can serve), we interviewed 11 policy domain experts in the U.S. and conducted co-design workshops with 14 active gig workers across four domains. Our results outline policymakers' prioritized initiatives, information needs, and (mis)alignments with workers' concerns and desires around data collectives. We offer design recommendations for data-sharing systems that support worker needs while bringing us closer to legislation that promote more thriving and equitable gig work futures.

Linh Le Pham Van, Hung The Tran, Sunil Gupta

Reinforcement Learning (RL) can effectively learn complex policies. However, learning these policies often demands extensive trial-and-error interactions with the environment. In many real-world scenarios, this approach is not practical due to the high costs of data collection and safety concerns. As a result, a common strategy is to transfer a policy trained in a low-cost, rapid source simulator to a real-world target environment. However, this process poses challenges. Simulators, no matter how advanced, cannot perfectly replicate the intricacies of the real world, leading to dynamics discrepancies between the source and target environments. Past research posited that the source domain must encompass all possible target transitions, a condition we term full support. However, expecting full support is often unrealistic, especially in scenarios where significant dynamics discrepancies arise. In this paper, our emphasis shifts to addressing large dynamics mismatch adaptation. We move away from the stringent full support condition of earlier research, focusing instead on crafting an effective policy for the target domain. Our proposed approach is simple but effective. It is anchored in the central concepts of the skewing and extension of source support towards target support to mitigate support deficiencies. Through comprehensive testing on a varied set of benchmarks, our method's efficacy stands out, showcasing notable improvements over previous techniques.

Allen Z. Ren, Justin Lidard, Lars L. Ankile et al.

We introduce Diffusion Policy Policy Optimization, DPPO, an algorithmic framework including best practices for fine-tuning diffusion-based policies (e.g. Diffusion Policy) in continuous control and robot learning tasks using the policy gradient (PG) method from reinforcement learning (RL). PG methods are ubiquitous in training RL policies with other policy parameterizations; nevertheless, they had been conjectured to be less efficient for diffusion-based policies. Surprisingly, we show that DPPO achieves the strongest overall performance and efficiency for fine-tuning in common benchmarks compared to other RL methods for diffusion-based policies and also compared to PG fine-tuning of other policy parameterizations. Through experimental investigation, we find that DPPO takes advantage of unique synergies between RL fine-tuning and the diffusion parameterization, leading to structured and on-manifold exploration, stable training, and strong policy robustness. We further demonstrate the strengths of DPPO in a range of realistic settings, including simulated robotic tasks with pixel observations, and via zero-shot deployment of simulation-trained policies on robot hardware in a long-horizon, multi-stage manipulation task. Website with code: diffusion-ppo.github.io

Acácia Kuenzer

O estudo das políticas públicas para o ensino médio e educação profissional tem sido um dos objetos privilegiados pelo GT 9 da Anped, ao longo da sua história. Nesse momento histórico que estamos vivendo, em que políticas de ensino médio e educação profissional e tecnológica têm sido formuladas sem o necessário debate com as entidades representativas dos trabalhadores, professores, pesquisadores e estudantes, o resgate de outros momentos em que essa mesma estratégia foi utilizada para impor diretrizes curriculares que aprofundam a desigualdade da oferta em prejuízo da classe trabalhadora, é um movimento necessário. Isso porque o passado nos ajuda a compreender o presente, que por sua vez traz anúncios do futuro, reforçando a necessidade do enfrentamento das políticas estabelecidas de forma autoritária que desqualificam a educação disponibilizada para os que vivem do trabalho, com o que se aprofundam as diferenças de classe.

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Nicholas E. Corrado, Josiah P. Hanna

On-policy reinforcement learning (RL) algorithms are typically characterized as algorithms that perform policy updates using i.i.d. trajectories collected by the agent's current policy. However, after observing only a finite number of trajectories, such on-policy sampling may produce data that fails to match the expected on-policy data distribution. This sampling error leads to high-variance gradient estimates that yield data-inefficient on-policy learning. Recent work in the policy evaluation setting has shown that non-i.i.d., off-policy sampling can produce data with lower sampling error w.r.t. the expected on-policy distribution than on-policy sampling can produce (Zhong et. al, 2022). Motivated by this observation, we introduce an adaptive, off-policy sampling method to reduce sampling error during on-policy policy gradient RL training. Our method, Proximal Robust On-Policy Sampling (PROPS), reduces sampling error by collecting data with a behavior policy that increases the probability of sampling actions that are under-sampled w.r.t. the current policy. We empirically evaluate PROPS on continuous-action MuJoCo benchmark tasks as well as discrete-action tasks and demonstrate that (1) PROPS decreases sampling error throughout training and (2) increases the data efficiency of on-policy policy gradient algorithms.

Laura State, Salvatore Ruggieri, Franco Turini

Explaining opaque Machine Learning (ML) models is an increasingly relevant problem. Current explanation in AI (XAI) methods suffer several shortcomings, among others an insufficient incorporation of background knowledge, and a lack of abstraction and interactivity with the user. We propose REASONX, an explanation method based on Constraint Logic Programming (CLP). REASONX can provide declarative, interactive explanations for decision trees, which can be the ML models under analysis or global/local surrogate models of any black-box model. Users can express background or common sense knowledge using linear constraints and MILP optimization over features of factual and contrastive instances, and interact with the answer constraints at different levels of abstraction through constraint projection. We present here the architecture of REASONX, which consists of a Python layer, closer to the user, and a CLP layer. REASONX's core execution engine is a Prolog meta-program with declarative semantics in terms of logic theories.

Laura State, Salvatore Ruggieri, Franco Turini

Many high-performing machine learning models are not interpretable. As they are increasingly used in decision scenarios that can critically affect individuals, it is necessary to develop tools to better understand their outputs. Popular explanation methods include contrastive explanations. However, they suffer several shortcomings, among others an insufficient incorporation of background knowledge, and a lack of interactivity. While (dialogue-like) interactivity is important to better communicate an explanation, background knowledge has the potential to significantly improve their quality, e.g., by adapting the explanation to the needs of the end-user. To close this gap, we present REASONX, an explanation tool based on Constraint Logic Programming (CLP). REASONX provides interactive contrastive explanations that can be augmented by background knowledge, and allows to operate under a setting of under-specified information, leading to increased flexibility in the provided explanations. REASONX computes factual and constrative decision rules, as well as closest constrative examples. It provides explanations for decision trees, which can be the ML models under analysis, or global/local surrogate models of any ML model. While the core part of REASONX is built on CLP, we also provide a program layer that allows to compute the explanations via Python, making the tool accessible to a wider audience. We illustrate the capability of REASONX on a synthetic data set, and on a a well-developed example in the credit domain. In both cases, we can show how REASONX can be flexibly used and tailored to the needs of the user.

Fernanda Franz Willers

Este artigo discute a relação entre o projeto educacional proposto pela Confederação Nacional da Indústria (CNI) e a contrarreforma do ensino médio e tem como fio condutor a análise dos seguintes pontos: 1) a contrarreforma do ensino médio; 2) a disputa pelos sentidos discursivos da BNCC; 3) as estratégias para a implementação da BNCC como programa da CNI. Os resultados revelam que o conteúdo da contrarreforma do ensino médio possui forte vinculação com as reivindicações da CNI para a política educacional brasileira. Palavras-chave: Confederação Nacional da Indústria (CNI); Base comum curricular (BNCC); Reforma do Ensino Médio.  

Furkan Düzenli, Mehtap Demir

The foundations of unionization are known to have been laid down by blue-collar workers. However, with the inclusion of technology in work life these days in particular, things have not progressed as expected for unions. Blue-collar workers have typical characteristics, and when evaluated generationally, saying that blue-collar workers represent Generation X would not be wrong. With regard to work life, on the other hand, different generations are seen to have taken over the jobs that are considered to be for the new generation. When looking at Generation X, the unions that have known how to organize workers are have certain problems getting the new generation of employees to unite under the umbrella of an organization. This study aims to measure the perceptions toward unions of workers who are actively employed in the labor market in order to measure these perceptions in terms of generations so that unions can create their own strategies in the face of the changing perceptions arising from generational differences, with the aim being to fill the gap in the literature in this regard. For this purpose, the study uses quantitative analysis methods and analyzes the obtained data using the package program SPSS 22.00 in order to perform explanatory factor analysis, reliability analysis, the independent samples t-test, and one-way analysis of variance (ANOVA) in accordance with the hypotheses. Using the data obtained from 248 participants, the research has reveals the perceptions toward unions of employees from different generations to vary.

Colin Vendromin, Marc M. Dignam

We prove that the density operator for the nonlinearly-generated quantum state of light in the $M$ lossy nonorthogonal quasimodes of a nanocavity system has the analytic form of a multimode squeezed thermal state, where the time-dependence of the squeezing and thermal photon parameters are given by a set of $3M$ coupled differential equations. We apply our approach to a system with two highly nonorthogonal quasimodes and obtain good agreement with simulations using a basis of Fock states. Our approach provides an efficient way to model and optimize the generation of mixed Gaussian cluster states.

C. Le-Van, Nguyen To‐The

PurposeTotal factor productivity (TFP), for a country and for a firm as well, is a crucial element for economic growth by inducing high output. Actually, workers' effort is among the important factors that positively influence the TFP.Design/methodology/approachIn this paper, the authors assume that the wage bonus enhances the worker's effort. Wage bonus is an incentive mechanism and plays a role in the TFP as is shown in a recent paper by Le Van and Pham (2021). The firm will maximize its profits. The supplies of capital and workers are exogenous. At equilibrium, the authors obtain that wage bonus has positive effects on output, labor productivity and price of the output.FindingsThe wage bonus system can make the optimal sequence of outputs grow without bounds. And if the optimal sequence converges to a steady state, this one can be characterized by higher output per capita than that in the steady state without the bonus.Originality/valueIn particular, the result show if, thanks to the wage bonus externality effect, the production may become of increasing returns and if the incentive mechanism is very strong, any optimal path of physical capitals will converge to infinity.

Ana Paula Felix, Eliza Bartolozzi Ferreira, Kefren Calegari dos Santos

Resumo O objetivo é analisar a forma de implantação do “novo ensino médio” no Espírito Santo. O estudo é desenvolvido a partir de uma breve revisão bibliográfica e análise dos instrumentos normativos que orientam a organização do “novo ensino médio”. As percepções dos trabalhadores docentes sobre a reforma foram analisadas com base nos dados coletados a partir de uma enquete aplicada com 263 docentes. A forma de implantação da reforma é singular e a maioria dos docentes desconhecem ou conhecem parcialmente a organização do “novo ensino médio”. Palavras-chave: reforma; ensino médio; ação pública.

Alexander Yu. Andryukhin

Purpose of the study. Analysis of inflationary factors associated with the labor market and employment is usually limited to the study of the relationship between the consumer price index and the Phillips curve. Therefore, the study examines the potential impact of a wider range of labor and employment market indicators on inflationary processes in the Russian economy. The purpose of the paper is to identify and assess the links between unemployment, on the one hand, and indicators of the labor market, employment, and incomes of the population in the national economy of Russia.Materials and methods. The study used the author’s hypothesis about the possibility of influ-encing inflation not only by unemployment, but also by other indicators of the labor market, such as the share of informal employment or the average working hours per week. The research also studied the impact of the labor market not only on the consumer price index, but also on the basic consumer price index (cleared of the influence of seasonal and administrative factors). The monthly data of the Federal State Statistics Service of the Russian Federation for 2016-2020 were used in Russia as a whole. We useda standard apparatus for searching and measuring cause-and-effect relationships (ma-trices of paired correlation coefficients, regression analysis).Results. In the short term, the level of labor force participation and economic activity have a positive relationship with inflation, as they are even lower than the level that could cause inflationary pressure (according to the second order polynomial). In 2017-2018 inflation was positively influ-enced by the size of the nominal accrued wages and the average number of hours worked per week. The traditional impact of the population income and aggregate demand on inflation has manifested itself. But it was insignificant (up to 10 % of inflation variance). This effect occurs only in those years when there are no more powerful inflationary factors. Consequently, cost inflation was fairly limited. In the short term, in some years, there is also a certain positive relationship between the share of people employed in the informal sector and the consumer price index. The rise in the infla-tionary tax on businesses without market power is forcing the majority of workers to be hired infor-mally. In the long term, an increase in the level of labor force participation explains part of the vari-ance of the basic consumer price index (but not related to the general consumer price index). With an increase in economic activity and income, the population acquires a wider range of goods, prices for which are not seasonal and are not administratively regulated.Conclusion. In general, the factors of the labor market and the population’s income are not de-cisive for inflation in the Russian economy, but they explain some of the changes. In the future, it is possible to build more accurate models in which indicators such as the level of labor supply can take a certain place next to the main inflationary factors. The findings of the study can be used when mak-ing decisions in the field of labor market regulation in conjunction with monetary policy.

Enio José Serra dos Santos

O presente texto é resultado de um honroso desafio a mim dirigido: homenagear Osmar Fávero, uma das principais referências sobre a memória e a história da Educação de Jovens e Adultos e da Educação Popular no Brasil. O agradecimento pelo convite é imenso, assim como é profunda minha gratidão ao homenageado pelos ensinamentos, conversas, histórias compartilhadas e pelas orientações de Mestrado e Doutorado, ambos realizados no Programa de Pós-Graduação em Educação da Universidade Federal Fluminense (UFF).