The paper argues for a social-economic approach to AI development.
problem The current AI development overlooks social and cultural aspects of intelligence.
method A blend of economic and social concepts with computational and inferential concepts in algorithm design.
result A social-economic approach is necessary for proper AI development.
Introduces a network-based framework to analyze swarm intelligence.
problem Lack of a common framework to compare swarm-based algorithms.
method Introduces an interaction network to examine swarm-based systems.
result Shows how swarm-based algorithms can be studied as systems.
Framework detects cyber threats from Twitter tweets.
problem Time-consuming manual extraction of cyber threat intelligence.
method Novelty detection model trained on CVE data.
result F1-score of 0.643 for classifying cyber threat tweets.
This study shows how social insects and machine learning methods share a common mathematical framework.
problem Understanding how decentralized systems achieve optimal decision-making.
method Developed a rigorous mathematical framework to show isomorphism between ant colonies and ensemble machine learning.
result Demonstrated that ant colony decision-making and random forest learning implement identical variance reduction strategies through decorrelation of identical units.
Deciding effective and timely preventive measures against complex social problems affecting relatively low income geographies is a difficult challenge. There is a strong need to adopt intelligent automation based solutions with low cost imprints to tackle these problems at larger scales. Starting with the hypothesis th…
The ABC algorithm's social interactions are characterized through a novel interaction network.
problem Understanding complex behavior in swarm intelligence systems.
method Definition and analysis of the interaction network for the ABC algorithm.
result ABC exhibits dynamic information flow but lacks continuous coordination.
Robot learns human interaction skills through multimodal deep reinforcement learning.
problem Developing robots with human-like social interaction skills.
method Multimodal Deep Q-Network (MDQN) for end-to-end reinforcement learning.
result Robot successfully learned basic interaction skills after 14 days of interaction.
We obtain the conditions for the emergence of the swarm intelligence effect in an interactive game of restless multi-armed bandit (rMAB). A player competes with multiple agents. Each bandit has a payoff that changes with a probability pc per round. The agents and player choose one of three options: (1) Exploit (a …
Highly precise globalness detection on social network pages.
problem Differentiating local from global nodes in online social networks.
method Four-stage operational flow for globalness detection.
result High precision (89%) and recall (88%) of local pages predictions.
New analysis shows rational actors will deploy AGI despite negative social value due to catastrophic risk.
problem Rational actors will deploy AGI despite negative social value due to shared catastrophic risk.
method Continuous-time preemption game with shared catastrophic externalities, showing suicide region and welfare distortion.
result The suicide region widens as catastrophic risk grows, and two mechanisms can close it.
Algorithmic collusion outperforms humans in a duopoly market, reducing social welfare.
problem The threat of algorithmic collusion in competitive markets.
method Proposed and tested an algorithm to extort human competitors in a Cournot duopoly market.
result Algorithmic collusion leads to higher profits for the algorithm and reduced social welfare.
Computational Intelligence (CI) is a sub-branch of Artificial Intelligence paradigm focusing on the study of adaptive mechanisms to enable or facilitate intelligent behavior in complex and changing environments. There are several paradigms of CI [like artificial neural networks, evolutionary computations, swarm intelli…
AI framework for automated policy-making connects with econometrics and social choice.
problem Improving government and economic policy-making through AI.
method Social Environment Design framework integrating Reinforcement Learning, EconCS, and Computational Social Choice.
result Promotes ethical and responsible decision making by solving open problems.
A new method uses CML to speed up social signal annotation.
problem Manual annotation of social signals in large multi-modal corpora is time-consuming and exhausting.
method CML techniques to predict local parts first, followed by a session-independent classification model.
result The method has the potential to significantly reduce human labelling efforts.
MGpi model predicts social actions in group interactions.
problem Social interaction among multiple agents and groups.
method Deep neural network with Kinesic-Proxemic-Message gate for social signal gating.
result Achieves state-of-the-art performance in group identification.
PHASE dataset simulates complex social interactions in physical environments.
problem Lack of datasets for evaluating physically grounded perception of complex social interactions.
method Created PHASE dataset of 2D animations with procedural generation and physics engine.
result SIMPLE model outperforms neural networks in recognizing complex social interactions.
Agents learn social skills from each other, improving performance.
problem Independent RL agents fail to use social learning.
method Imposed constraints, model-based auxiliary loss, mixed training.
result Agents trained with social learning outperform solo-trained agents.
This paper uses decolonial theory to improve AI's ethical development.
problem AI's risks to vulnerable peoples and negative impacts of innovation.
method Embedding decolonial critical approach in AI technical practice.
result Developing tactics to align AI with ethical principles.
Deep neural networks detect cyberthreats from Twitter.
problem Scalable processing of cybersecurity information from social media.
method Convolutional and bidirectional LSTM networks for tweet classification and named entity recognition.
result Pipeline achieves high true positive and F1-score rates for threat detection.
Crowd wisdom improves decision-making by integrating multiple responses.
problem Improving collective decision-making accuracy from multiple responses.
method Analogous to unsupervised dimension reduction, using techniques like PCA and Isomap.
result Crowd wisdom methods outperform existing solutions, including supervised learning.
Defines explainability as reasoning under background knowledge.
problem Lack of agreed definitions in explainable AI.
method Reviews philosophical and social foundations, translates to tech realm.
result Defines explainability as logical reasoning under background knowledge.
AI investors signal higher debt in ESG firms, boosting portfolio management.
problem Determining the value of ESG investing amid AI investment trends.
method Cross-sectional regressions of ESG scores and debt ratios of S&P 500 firms.
result ESG scores signal higher debt in firms, supporting ESG investing.
Paper investigates if RL can learn from observing others without explicit modeling.
problem Can RL learn from observing others without explicit modeling?
method Simple scenarios with RL agents observing and learning from another agent's actions.
result RL agents can learn from observing others, leveraging environmental effects.
Research tackles alliance formation in many-player zero-sum games, showing reinforcement learning fails but a contract mechanism can help.
problem Tackles the challenge of alliance formation in many-player zero-sum games.
method Demonstrates the social dilemma aspect of alliance formation, introduces a contract mechanism to augment reinforcement learning.
result Naïve reinforcement learning fails to form alliances, but a contract mechanism can help.
Model detects insurance fraud using social network analysis.
problem Fraudulent insurance claims by exaggeration or intentional damage.
method Network construction linking claims and parties, BiRank algorithm for fraud score computation, feature extraction from network and claims, supervised model building.
result Network features improve fraud detection performance.
AI identifies patient clusters for diabetes case management.
problem Diabetes complications and mental health comorbidities drive high healthcare costs.
method Combined AI techniques with diverse data sources for prediction and clustering.
result 83.5% accuracy in predicting diabetes complications and meaningful patient clusters.
Provides a compendium of data sources for various applications.
problem Lack of comprehensive data sources for data science, machine learning, and AI.
method Compilation of diverse data sources across multiple application areas.
result A comprehensive list of data sources for data scientists and machine learning experts.
New approach simulates reputation dynamics using information compression.
problem Malicious communication strategies in reputation networks.
method Uses information compression techniques to simulate social phenomena.
result Emergent phenomena like echo chambers and deception are observed.
Twitter system detects unexpected epidemics.
problem Detecting sudden, unexpected epidemic outbreaks in Twitter.
method Dynamic classification, alert generation, and ranking/recommendation.
result Empirical evaluation and validation with domain experts.
Paper proposes fairgroup construction to improve fairness in Medicaid eligibility decisions.
problem Improper decisions in Medicaid eligibility allocation due to ML/DL model limitations.
method Fairgroup construction based on legal doctrine of disparate impact.
result Demonstrates improved fairness in regressive classifiers for Medicaid eligibility decisions.
AI models predict stock trends using historical data and public sentiment.
problem Improving stock market prediction accuracy using AI.
method Employed regression and classification ML algorithms for technical and fundamental analysis respectively.
result Median performance suggests AI is not yet superior to stock markets.
Researchers propose a method to quantify explainability in AI systems.
problem Lack of consensus and quantification of explainability in AI systems.
method Analyzed definitions from different disciplines, proposed a quantification approach.
result Proposed a reasonable and model-agnostic way to quantify explainability.
Graphs represent natural and artificial systems; ML can learn from them.
problem Representing and analyzing complex systems using graphs.
method Graph Neural Networks (GNNs) for learning from graph data.
result GNNs enable learning from diverse graph-based data.
Unity offers a flexible platform for AI research with rich simulations.
problem Limited configurability and complexity in existing simulation environments.
method Proposed a taxonomy of simulation platforms and demonstrated Unity's suitability.
result Unity enables rich, complex simulations for AI research.
Large language models struggle with causal relationships, leading to biases and hallucinations.
problem LLMs struggle with true causal relationships, leading to biases and hallucinations.
method Embed causality into LLMs training process at every stage.
result LLMs need to be trained to understand and apply causal knowledge, not just recite it.
AI model predicts stock prices using social media data and hybrid neural networks.
problem Predicting stock price movements during the COVID-19 pandemic.
method Integrates social media trends and historical stock data using a hybrid CNN-BLSTM framework.
result The proposed framework outperforms traditional models in predicting stock price movements.
Study on decision-making cascades with agents having varying beliefs and noise levels.
problem Optimizing decision-making in a cascade of agents with heterogeneous beliefs and noise.
method Recursive belief update and analysis of optimal decision rules, predecessor selection problem characterization.
result Optimal decisions can deviate from true prior beliefs in certain conditions, highlighting the importance of social learning.
Kernel method estimates long-term effects from short-term data.
problem Estimating long-term effects from short-term data in continuous actions.
method Kernel ridge regression to embed and extrapolate long-term effects.
result Uniform consistency and nonasymptotic error bounds for the estimator.
This paper evaluates debiasing methods on word embeddings to reduce religious bias.
problem Social biases persist in word embeddings, potentially amplifying them in AI applications.
method Investigates and evaluates three multiclass debiasing techniques on three word embeddings.
result ConceptorDebiasing is the most effective method, reducing religious bias by 82-96%.
AI predicts dementia onset from emotional face evaluations.
problem Early detection of dementia in aging societies.
method Behavioral responses analysis and AI regression.
result Encouraging AI-based prediction results for MoCA scores.
Neural networks predict scientific correlations from text descriptions.
problem Predicting untested scientific correlations to guide research and resource allocation.
method Trained neural network on 170k correlational findings from social science journals.
result Neural network can accurately predict reported correlations from text descriptions.
Paper tackles biases in AI embeddings from wild data.
problem Biases in AI embeddings from uncontrolled data.
method Measures biases using social psychology word lists, observes gender bias in occupations, demonstrates simple projection to reduce bias.
result Simple projection significantly reduces embedding biases.
The paper evaluates the importance of monotonicity in AI fairness across various fields.
problem Ensuring fairness in AI applications across criminology, education, health care, and finance.
method Theoretical reasoning, simulation, and extensive empirical analysis of monotonic neural additive models (MNAMs).
result Monotonicity is essential for fairness in AI ethics and society, especially in criminology, education, health care, and finance.
AI learns market manipulation through simulation, suggesting regulation.
problem Regulating AI to prevent market manipulation.
method Used a genetic algorithm in an artificial market simulation.
result AI discovered market manipulation as an optimal strategy.
Mathematical framework using Riemannian geometry for intelligence and consciousness.
problem Lack of a unified mathematical framework for intelligence and consciousness.
method Conceptualizes intelligence as tokens in a high-dimensional space, using Riemannian geometry to describe structure and dynamics.
result Integrates geometric concepts to offer a unified framework for intelligence and consciousness.
Blockchain aims to improve trust in AI systems, but lacks systematic studies.
problem Lack of systematic studies on blockchain design principles for AI trust.
method Hybrid qualitative and quantitative studies.
result Vast opportunities for future research and practice in blockchain design.
Bayesian method predicts social unrest from social media data.
problem Uncertainty in predicting events from diverse, unstructured social media data.
method Machine learning for classification and empirical Bayesian approach for event probability calculation.
result Predicted social unrest events in Australian cities with high accuracy.
Artificial intelligence has impacted many aspects of human life. This paper studies the impact of artificial intelligence on economic theory. In particular we study the impact of artificial intelligence on the theory of bounded rationality, efficient market hypothesis and prospect theory.