Tag: ai-society (56 references)
Trust and Friction: Negotiating How Information Flows Through Decentralized Social Media
Cybernetics
Cybernetics is the transdisciplinary study of circular causal processes such as feedback and recursion, where the effects of a system's actions (its outputs) return as inputs to that system, influencing subsequent action. It is concerned with general principles that are relevant across multiple contexts, including in engineering, ecological, economic, biological, cognitive and social systems and also in practical activities such as designing, learning, and managing. Cybernetics' transdisciplinary character has meant that it intersects with a number of other fields, leading to it having both wide influence and diverse interpretations. The field is named after an example of circular causal feedback—that of steering a ship (the ancient Greek κυβερνήτης (kybernḗtēs) refers to the person who steers a ship). In steering a ship, the position of the rudder is adjusted in continual response to the effect it is observed as having, forming a feedback loop through which a steady course can be maintained in a changing environment, responding to disturbances from cross winds and tide. Cybernetics has its origins in exchanges between numerous disciplines during the 1940s. Initial developments were consolidated through meetings such as the Macy Conferences and the Ratio Club. Early focuses included purposeful behaviour, neural networks, heterarchy, information theory, and self-organising systems. As cybernetics developed, it became broader in scope to include work in design, family therapy, management and organisation, pedagogy, sociology, the creative arts and the counterculture.
If open source is to win, it must go public
Canada as a Champion for Public AI: Data, Compute and Open Source Infrastructure for Economic Growth and Inclusive Innovation
The Illusion of Artificial Inclusion
Machines of Loving Grace: How AI Could Transform the World for the Better
The Rise of AI-Generated Content in Wikipedia
Large language models reduce public knowledge sharing on online Q&A platforms
GPTs are GPTs: Labor Market Impact Potential of LLMs
Public {AI}: {Infrastructure} for the {Common} {Good}
Wikimedia data for AI: a review of Wikimedia datasets for NLP tasks and AI-assisted editing
Public AI: Making AI Work for Everyone, by Everyone
Push and Pull: A Framework for Measuring Attentional Agency
Wikipedia's value in the age of generative {AI}
If there was a generative artificial intelligence system that could, on its own, write all the information contained in Wikipedia, would it be the same as Wikipedia today?
Power and Progress: Our Thousand-Year Struggle Over Technology and Prosperity| Winners of the 2024 Nobel Prize for Economics
Terms-we-serve-with: Five dimensions for anticipating and repairing algorithmic harm
Sociotechnical Harms of Algorithmic Systems: Scoping a Taxonomy for Harm Reduction
Understanding the landscape of potential harms from algorithmic systems enables practitioners to better anticipate consequences of the systems they build. It also supports the prospect of incorporating controls to help minimize harms that emerge from the interplay of technologies and social and cultural dynamics. A growing body of scholarship has identified a wide range of harms across different algorithmic technologies. However, computing research and practitioners lack a high level and synthesized overview of harms from algorithmic systems. Based on a scoping review of computing research (n=172), we present an applied taxonomy of sociotechnical harms to support a more systematic surfacing of potential harms in algorithmic systems. The final taxonomy builds on and refers to existing taxonomies, classifications, and terminologies. Five major themes related to sociotechnical harms — representational, allocative, quality-of-service, interpersonal harms, and social system/societal harms — and sub-themes are presented along with a description of these categories. We conclude with a discussion of challenges and opportunities for future research.
An Alternative to Regulation: The Case for Public AI
Datamodels: Predicting Predictions from Training Data
Proposes datamodels that predict model outputs as a function of training data subsets, providing a framework for understanding data attribution through retraining experiments.
The Fallacy of AI Functionality
On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?
What are you optimizing for? Aligning Recommender Systems with Human Values
A Deeper Investigation of the Importance of Wikipedia Links to Search Engine Results
Language (Technology) is Power: A Critical Survey of “Bias” in NLP
Lessons from Archives: Strategies for Collecting Sociocultural Data in Machine Learning
Exploring Research Interest in Stack Overflow -- A Systematic Mapping Study and Quality Evaluation
The Economics of Maps
Are anonymity-seekers just like everybody else? An analysis of contributions to Wikipedia from Tor
In Pursuit of Interpretable, Fair and Accurate Machine Learning for Criminal Recidivism Prediction
Reconciling modern machine-learning practice and the classical bias–variance trade-off
Excavating AI: The Politics of Images in Machine Learning Training Sets
Ecosystem Tipping Points in an Evolving World
Face Recognition Vendor Test (FRVT) Part 3: Demographic Effects
Privacy, anonymity, and perceived risk in open collaboration: A study of service providers
Dissecting Racial Bias in an Algorithm Used to Manage the Health of Populations
Fairness and Abstraction in Sociotechnical Systems
"Data Strikes": Evaluating the Effectiveness of a New Form of Collective Action Against Technology Companies
Simulates data strikes against recommender systems, showing that collective withholding of training data can create leverage for users against technology platforms.
A Reductions Approach to Fair Classification
Data Statements for Natural Language Processing: Toward Mitigating System Bias and Enabling Better Science
Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification
Troubling Trends in Machine Learning Scholarship
Artificial Intelligence and Its Implications for Income Distribution and Unemployment
A Blueprint for a Better Digital Society
Big Data's Disparate Impact
Causal Inference in Statistics, Social, and Biomedical Sciences
Comprehensive treatment of causal inference methods for observational and experimental data. Covers randomized experiments, matching, propensity scores, instrumental variables, and regression discontinuity designs.
Causality: Models, Reasoning, and Inference
Foundational book on causal inference introducing structural causal models, do-calculus, and counterfactual reasoning. Unifies graphical models with potential outcomes framework. Second edition with expanded coverage.
Causal Inference Using Potential Outcomes: Design, Modeling, Decisions
Comprehensive overview of the potential outcomes framework for causal inference. Covers experimental design, observational studies, propensity scores, and the fundamental problem of causal inference.
Modeling Complexity : The Limits to Prediction
Simple Demographics Often Identify People Uniquely
Social {Dilemmas}: {The} {Anatomy} of {Cooperation}
The study of social dilemmas is the study of the tension between individual and collective rationality. In a social dilemma, individually reasonable behavior leads to a situation in which everyone is worse off. The first part of this review is a discussion of categories of social dilemmas and how they are modeled. The key two-person social dilemmas (Prisoner’s Dilemma, Assurance, Chicken) and multiple-person social dilemmas (public goods dilemmas and commons dilemmas) are examined. The second part is an extended treatment of possible solutions for social dilemmas. These solutions are organized into three broad categories based on whether the solutions assume egoistic actors and whether the structure of the situation can be changed: Motivational solutions assume actors are not completely egoistic and so give some weight to the outcomes of their partners. Strategic solutions assume egoistic actors, and neither of these categories of solutions involve changing the fundamental structure of the situation. Solutions that do involve changing the rules of the game are considered in the section on structural solutions. I conclude the review with a discussion of current research and directions for future work.