Hasil untuk "Human ecology. Anthropogeography"

Nick von Felten

Isolated perspectives have often paved the way for great scientific discoveries. However, many breakthroughs only emerged when moving away from singular views towards interactions. Discussions on Artificial Intelligence (AI) typically treat human and AI bias as distinct challenges, leaving their dynamic interplay and compounding potential largely unexplored. Recent research suggests that biased AI can amplify human cognitive biases, while well-calibrated systems might help mitigate them. In this position paper, I advocate for transcending beyond separate treatment of human and AI biases and instead focus on their interaction effects. I argue that a comprehensive framework, one that maps (compound human-AI) biases to mitigation strategies, is essential for understanding and protecting human cognition, and I outline concrete steps for its development.

Tailia Malloy, Maria Jose Ferreira, Fei Fang et al.

In real-world decision making, outcomes are often delayed, meaning individuals must make multiple decisions before receiving any feedback. Moreover, feedback can be presented in different ways: it may summarize the overall results of multiple decisions (aggregated feedback) or report the outcome of individual decisions after some delay (clustered feedback). Despite its importance, the timing and presentation of delayed feedback has received little attention in cognitive modeling of decision-making, which typically focuses on immediate feedback. To address this, we conducted an experiment to compare the effect of delayed vs. immediate feedback and aggregated vs. clustered feedback. We also propose a Hierarchical Instance-Based Learning (HIBL) model that captures how people make decisions in delayed feedback settings. HIBL uses a super-model that chooses between sub-models to perform the decision-making task until an outcome is observed. Simulations show that HIBL best predicts human behavior and specific patterns, demonstrating the flexibility of IBL models.

Swati Sachan, Theo Miller, Mai Phuong Nguyen

High-stakes decision domains are increasingly exploring the potential of Large Language Models (LLMs) for complex decision-making tasks. However, LLM deployment in real-world settings presents challenges in data security, evaluation of its capabilities outside controlled environments, and accountability attribution in the event of adversarial decisions. This paper proposes a framework for responsible deployment of LLM-based decision-support systems through active human involvement. It integrates interactive collaboration between human experts and developers through multiple iterations at the pre-deployment stage to assess the uncertain samples and judge the stability of the explanation provided by post-hoc XAI techniques. Local LLM deployment within organizations and decentralized technologies, such as Blockchain and IPFS, are proposed to create immutable records of LLM activities for automated auditing to enhance security and trace back accountability. It was tested on Bert-large-uncased, Mistral, and LLaMA 2 and 3 models to assess the capability to support responsible financial decisions on business lending.

Eleni Straitouri, Stratis Tsirtsis, Ander Artola Velasco et al.

Recent work has shown that, in classification tasks, it is possible to design decision support systems that do not require human experts to understand when to cede agency to a classifier or when to exercise their own agency to achieve complementarity$\unicode{x2014}$experts using these systems make more accurate predictions than those made by the experts or the classifier alone. The key principle underpinning these systems reduces to adaptively controlling the level of human agency, by design. Can we use the same principle to achieve complementarity in sequential decision making tasks? In this paper, we answer this question affirmatively. We develop a decision support system that uses a pre-trained AI agent to narrow down the set of actions a human can take to a subset, and then asks the human to take an action from this action set. Along the way, we also introduce a bandit algorithm that leverages the smoothness properties of the action sets provided by our system to efficiently optimize the level of human agency. To evaluate our decision support system, we conduct a large-scale human subject study ($n = 1{,}600$) where participants play a wildfire mitigation game. We find that participants who play the game supported by our system outperform those who play on their own by $\sim$$30$% and the AI agent used by our system by $>$$2$%, even though the AI agent largely outperforms participants playing without support. We have made available the data gathered in our human subject study as well as an open source implementation of our system at https://github.com/Networks-Learning/narrowing-action-choices .

He Zhang, Xinyi Fu, John M. Carroll

Traditional image annotation tasks rely heavily on human effort for object selection and label assignment, making the process time-consuming and prone to decreased efficiency as annotators experience fatigue after extensive work. This paper introduces a novel framework that leverages the visual understanding capabilities of large multimodal models (LMMs), particularly GPT, to assist annotation workflows. In our proposed approach, human annotators focus on selecting objects via bounding boxes, while the LMM autonomously generates relevant labels. This human-AI collaborative framework enhances annotation efficiency by reducing the cognitive and time burden on human annotators. By analyzing the system's performance across various types of annotation tasks, we demonstrate its ability to generalize to tasks such as object recognition, scene description, and fine-grained categorization. Our proposed framework highlights the potential of this approach to redefine annotation workflows, offering a scalable and efficient solution for large-scale data labeling in computer vision. Finally, we discuss how integrating LMMs into the annotation pipeline can advance bidirectional human-AI alignment, as well as the challenges of alleviating the "endless annotation" burden in the face of information overload by shifting some of the work to AI.

Isac Toaya Mussama

Este estudo avalia a dinâmica de uso e cobertura da terra em Dianópolis (TO), Brasil, utilizando dados da Coleção 9.0 do Projeto MapBiomas para os anos de 1985 e 2023. As informações foram derivadas de imagens Landsat classificadas com algoritmos de aprendizado de máquina no Google Earth Engine e analisadas no ArcGIS®. Observou-se redução da cobertura florestal de 68,35% para 54,40% e expansão agropecuária de 8,63% para 44,18%. A vegetação herbácea e arbustiva foi praticamente suprimida, indicando conversão de ecossistemas nativos. Houve ainda aumento de áreas urbanas, não vegetadas e de corpos hídricos, refletindo intensificação antrópica. Os resultados confirmam padrões nacionais e globais de conversão da vegetação natural, destacando a urgência de estratégias de manejo sustentável e conservação ambiental.

Ratanond Koonchanok, Khairi Reda

People often use visualizations not only to explore a dataset but also to draw generalizable conclusions about underlying models or phenomena. While previous research has viewed deviations from rational analysis as problematic, we hypothesize that human reliance on non-normative heuristics may be advantageous in certain situations. In this study, we investigate scenarios where human intuition might outperform idealized statistical rationality. Our experiment assesses participants' accuracy in characterizing the parameters of known data-generating models from bivariate visualizations. Our findings show that, while participants generally demonstrated lower accuracy than statistical models, they often outperformed Bayesian agents, particularly when dealing with extreme samples. These results suggest that, even when deviating from rationality, human gut reactions to visualizations can provide an advantage. Our findings offer insights into how analyst intuition and statistical models can be integrated to improve inference and decision-making, with important implications for the design of visual analytics tools.

Paula Akemi Aoyagui, Sharon Ferguson, Anastasia Kuzminykh

An essential aspect of evaluating Large Language Models (LLMs) is identifying potential biases. This is especially relevant considering the substantial evidence that LLMs can replicate human social biases in their text outputs and further influence stakeholders, potentially amplifying harm to already marginalized individuals and communities. Therefore, recent efforts in bias detection invested in automated benchmarks and objective metrics such as accuracy (i.e., an LLMs output is compared against a predefined ground truth). Nonetheless, social biases can be nuanced, oftentimes subjective and context-dependent, where a situation is open to interpretation and there is no ground truth. While these situations can be difficult for automated evaluation systems to identify, human evaluators could potentially pick up on these nuances. In this paper, we discuss the role of human evaluation and subjective interpretation to augment automated processes when identifying biases in LLMs as part of a human-centred approach to evaluate these models.

David Helmer, Michael Boardman, S. Kate Conroy et al.

The REAIM 2024 Blueprint for Action states that AI applications in the military domain should be ethical and human-centric and that humans must remain responsible and accountable for their use and effects. Developing rigorous test and evaluation, verification and validation (TEVV) frameworks will contribute to robust oversight mechanisms. TEVV in the development and deployment of AI systems needs to involve human users throughout the lifecycle. Traditional human-centred test and evaluation methods from human factors need to be adapted for deployed AI systems that require ongoing monitoring and evaluation. The language around AI-enabled systems should be shifted to inclusion of the human(s) as a component of the system. Standards and requirements supporting this adjusted definition are needed, as are metrics and means to evaluate them. The need for dialogue between technologists and policymakers on human-centred TEVV will be evergreen, but dialogue needs to be initiated with an objective in mind for it to be productive. Development of TEVV throughout system lifecycle is critical to support this evolution including the issue of human scalability and impact on scale of achievable testing. Communication between technical and non technical communities must be improved to ensure operators and policy-makers understand risk assumed by system use and to better inform research and development. Test and evaluation in support of responsible AI deployment must include the effect of the human to reflect operationally realised system performance. Means of communicating the results of TEVV to those using and making decisions regarding the use of AI based systems will be key in informing risk based decisions regarding use.

Jiajun Wang, Xiangzhe Yuan, Siying Hu et al.

With the development of generative AI technology, a vast array of AI-generated paintings (AIGP) have gone viral on social media like TikTok. However, some negative news about AIGP has also emerged. For example, in 2022, numerous painters worldwide organized a large-scale anti-AI movement because of the infringement in generative AI model training. This event reflected a social issue that, with the development and application of generative AI, public feedback and feelings towards it may have been overlooked. Therefore, to investigate public interactions and perceptions towards AIGP on social media, we analyzed user engagement level and comment sentiment scores of AIGP using human painting videos as a baseline. In analyzing user engagement, we also considered the possible moderating effect of the aesthetic quality of Paintings. Utilizing topic modeling, we identified seven reasons, including hyperrealistic quality, ambivalent reactions, perceived theft of art, etc., leading to negative public perceptions of AIGP. Our work may provide instructive suggestions for future generative AI technology development and avoid potential crises in human-AI collaboration.

Malka Liaquat, Ghina Ahmed, Hina Ismail et al.

With the emergence of a green environment and green business, the banking sector has also enforced green practices. This study aims to explore the impact of motivational factors and green behaviors on the environmental performance of banking sector employees. This is a quantitative study and data has been collected through a cross-sectional survey of the questionnaire in the banking sector. 300 questionnaires were distributed to the bank employees. PLS-SEM was used to find the statistical results. The study finds a positive impact of Extrinsic motivation and Intrinsic motivation on Employee Environmental Performance, the mediating effect of Task-related Green Behaviors was also found to be positive. The study does not support the effect of Voluntary Green Behaviors on Employee Environment Performance and its mediating effect was also not supported. The study findings and deep knowledge of the impact of motivational and behavioral employee environmental performance on banking sector employees have provided new directions for researchers and policymakers. This study will help the policymakers in strategically developing rewarding policies for the employees that would definitely create a positive impact on performance. The results of the study have provided empirical confirmation of employees’ motivational needs and their impact on green behaviors that collectively impact employee environmental performance.

Angela Carrera-Rivera, Daniel Reguera-Bakhache, Felix Larrinaga et al.

Human-machine interfaces (HMI) facilitate communication between humans and machines, and their importance has increased in modern technology. However, traditional HMIs are often static and do not adapt to individual user preferences or behavior. Adaptive User Interfaces (AUIs) have become increasingly important in providing personalized user experiences. Machine learning techniques have gained traction in User Experience (UX) research to provide smart adaptations that can reduce user cognitive load. This paper presents an ongoing exploration of a method for generating adaptive user interfaces by analyzing user interactions and contextual data. It also provides an illustrative example using Markov chains to predict the next step for users interacting with an app for an industrial mixing machine. Furthermore, the paper conducts an offline evaluation of the approach, focusing on the precision of the recommendations. The study emphasizes the importance of incorporating user interactions and contextual data into the design of adaptive HMIs, while acknowledging the existing challenges and potential benefits.

Celal Savur, Jamison Heard, Ferat Sahin

Fluent human-robot collaboration requires a robot teammate to understand, learn, and adapt to the human's psycho-physiological state. Such collaborations require a computing system that monitors human physiological signals during human-robot collaboration (HRC) to quantitatively estimate a human's level of comfort, which we have termed in this research as comfortability index (CI) and uncomfortability index (unCI). Subjective metrics (surprise, anxiety, boredom, calmness, and comfortability) and physiological signals were collected during a human-robot collaboration experiment that varied robot behavior. The emotion circumplex model is adapted to calculate the CI from the participant's quantitative data as well as physiological data. To estimate CI/unCI from physiological signals, time features were extracted from electrocardiogram (ECG), galvanic skin response (GSR), and pupillometry signals. In this research, we successfully adapt the circumplex model to find the location (axis) of 'comfortability' and 'uncomfortability' on the circumplex model, and its location match with the closest emotions on the circumplex model. Finally, the study showed that the proposed approach can estimate human comfortability/uncomfortability from physiological signals.

Parag Khanna, Mårten Björkman, Christian Smith

Handovers frequently occur in our social environments, making it imperative for a collaborative robotic system to master the skill of handover. In this work, we aim to investigate the relationship between the grip force variation for a human giver and the sensed interaction force-torque in human-human handovers, utilizing a data-driven approach. A Long-Short Term Memory (LSTM) network was trained to use the interaction force-torque in a handover to predict the human grip force variation in advance. Further, we propose to utilize the trained network to cause human-like grip force variation for a robotic giver.

Romy Müller, Marius Thoß, Julian Ullrich et al.

The evaluation of explainable artificial intelligence is challenging, because automated and human-centred metrics of explanation quality may diverge. To clarify their relationship, we investigated whether human and artificial image classification will benefit from the same visual explanations. In three experiments, we analysed human reaction times, errors, and subjective ratings while participants classified image segments. These segments either reflected human attention (eye movements, manual selections) or the outputs of two attribution methods explaining a ResNet (Grad-CAM, XRAI). We also had this model classify the same segments. Humans and the model largely agreed on the interpretability of attribution methods: Grad-CAM was easily interpretable for indoor scenes and landscapes, but not for objects, while the reverse pattern was observed for XRAI. Conversely, human and model performance diverged for human-generated segments. Our results caution against general statements about interpretability, as it varies with the explanation method, the explained images, and the agent interpreting them.

Loesje Verhoeven, Jo Ritzen

Are institutional trust and interpersonal trust threatened by globalisation? For nineteen countries in Europe, using a fixed effects model for a panel data set relating globalisation to several economic and social macro variables, like income inequality and diversity, to average institutional and interpersonal trust derived from responses in European Social Surveys, we do not find any significant relation between the relatively moderate globalisation of the first two decades of the 21st century on average interpersonal and institutional trust. At the same time, occurrences of economic decline in a country are negatively related to institutional trust. GDP has a positive effect on both institutional and interpersonal.Combining the macro factors with the individual traits of respondents using pooled repeated cross-sectional data demonstrate the dominance of personal characteristics in individual levels of trust, with only institutional quality emerging as a macro variable which is significantly and positively related to trust, especially for the Socio-Economic Groups 3 to 7 (of the eight groups distinguished). Those who are born in the country exhibit higher levels of interpersonal trust, in particular in the higher SES groups 4–7, but show significantly lower institutional trust for the SES groups 0–2. Age is negatively related to institutional trust for all SES groups, but positively related to interpersonal trust for SES groups 4–7.These findings appear to imply that those who are concerned with the level of institutional trust in the population as a basic requirement for democracy in Europe should focus on the quality of institutions and not on globalisation.

Adelar Fochezatto, Carlos Henán Rodas Céspedes

Para promover o desenvolvimento regional, é importante conhecer como se comportam os indicadores demográficos empresariais nas diferentes regiões. Este estudo calcula indicadores anuais de nascimento, mortalidade, rotatividade e de sobrevivência de empresas nas mesorregiões do Rio Grande do Sul (RS) no período de 2006 a 2013. Para isso, é utilizada uma base de dados identificada que possibilita o acompanhamento longitudinal de todas as empresas formais. Os resultados mostram que as diferenças entre as mesorregiões são pequenas, indicando pouca influência da localização espacial sobre a demografia de empresas no curto prazo. Os resultados mostram, também, que as maiores taxas de sobrevivência de empresas estão na mesorregião Nordeste enquanto que as maiores taxas de nascimento e de rotatividade estão na mesorregião Sudeste do RS.

Kate Donahue, Alexandra Chouldechova, Krishnaram Kenthapadi

Much of machine learning research focuses on predictive accuracy: given a task, create a machine learning model (or algorithm) that maximizes accuracy. In many settings, however, the final prediction or decision of a system is under the control of a human, who uses an algorithm's output along with their own personal expertise in order to produce a combined prediction. One ultimate goal of such collaborative systems is "complementarity": that is, to produce lower loss (equivalently, greater payoff or utility) than either the human or algorithm alone. However, experimental results have shown that even in carefully-designed systems, complementary performance can be elusive. Our work provides three key contributions. First, we provide a theoretical framework for modeling simple human-algorithm systems and demonstrate that multiple prior analyses can be expressed within it. Next, we use this model to prove conditions where complementarity is impossible, and give constructive examples of where complementarity is achievable. Finally, we discuss the implications of our findings, especially with respect to the fairness of a classifier. In sum, these results deepen our understanding of key factors influencing the combined performance of human-algorithm systems, giving insight into how algorithmic tools can best be designed for collaborative environments.

FengHua Wen, Yating Jiang, Ling Jiang

Abstract Floating population is an important group in the emerging urbanization process. This group promotes long-term settlement, which is a significant driving force increasing the urbanization level of countries. This study analyzed the changes in population mobility between Chinese cities and the willingness of the floating population to settle down. The analyses were based on data obtained from the China Migrants Dynamic Survey (CMDS) in 2017, and the China Seventh Census 2020. Spatial econometric models were constructed for in-depth research. The result showed that: ① the floating population migrated mainly from the central region to the surrounding cities, and their long-term settlement intention presented a spatial pattern of "high in the east, low in the west, and local concentration." ②the long-term settlement intention significantly negatively affected the urban floating population. City economic level, public service capacity, and environmental quality significantly positively or negatively influence the number of the floating population. For promoting more floating population to become urban residents, management of the group should be strengthened, construction level of the urban economy, society, and ecology improved, and the willingness of the group to settle for an extended time encouraged.