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Found 21 papers, 2 papers with code
AI Fairness in Practice
In this workbook, we tackle this challenge by exploring how a context-based and society-centred approach to understanding AI Fairness can help project teams better identify, mitigate, and manage the many ways that unfair bias and discrimination can crop up across the AI project workflow.
AI Sustainability in Practice Part One: Foundations for Sustainable AI Projects
Sustainable AI projects are continuously responsive to the transformative effects as well as short-, medium-, and long-term impacts on individuals and society that the design, development, and deployment of AI technologies may have.
AI Ethics and Governance in Practice: An Introduction
AI systems may have transformative and long-term effects on individuals and society.
AI Sustainability in Practice Part Two: Sustainability Throughout the AI Workflow
The sustainability of AI systems depends on the capacity of project teams to proceed with a continuous sensitivity to their potential real-world impacts and transformative effects.
Learning from data with structured missingness
Missing data are an unavoidable complication in many machine learning tasks.
Don't "research fast and break things": On the ethics of Computational Social Science
This article is concerned with setting up practical guardrails within the research activities and environments of CSS.
Data Justice in Practice: A Guide for Developers
The Advancing Data Justice Research and Practice project aims to broaden understanding of the social, historical, cultural, political, and economic forces that contribute to discrimination and inequity in contemporary ecologies of data collection, governance, and use.
Advancing Data Justice Research and Practice: An Integrated Literature Review
The Advancing Data Justice Research and Practice (ADJRP) project aims to widen the lens of current thinking around data justice and to provide actionable resources that will help policymakers, practitioners, and impacted communities gain a broader understanding of what equitable, freedom-promoting, and rights-sustaining data collection, governance, and use should look like in increasingly dynamic and global data innovation ecosystems.
Data Justice Stories: A Repository of Case Studies
The idea of "data justice" is of recent academic vintage.
Human rights, democracy, and the rule of law assurance framework for AI systems: A proposal
The HUDERAF combines the procedural requirements for principles-based human rights due diligence with the governance mechanisms needed to set up technical and socio-technical guardrails for responsible and trustworthy AI innovation practices.
Gaussian Processes on Hypergraphs
We derive a Matern Gaussian process (GP) on the vertices of a hypergraph.
Does "AI" stand for augmenting inequality in the era of covid-19 healthcare?
Among the most damaging characteristics of the covid-19 pandemic has been its disproportionate effect on disadvantaged communities.
Artificial intelligence, human rights, democracy, and the rule of law: a primer
In September 2019, the Council of Europe's Committee of Ministers adopted the terms of reference for the Ad Hoc Committee on Artificial Intelligence (CAHAI).
Explaining decisions made with AI: A workbook (Use case 1: AI-assisted recruitment tool)
The goal of the workbook is to summarise some of main themes from Explaining decisions made with AI and then to provide the materials for a workshop exercise that has been built around a use case created to help you gain a flavour of how to put the guidance into practice.
The Arc of the Data Scientific Universe
In this paper I explore the scaffolding of normative assumptions that supports Sabina Leonelli's implicit appeal to the values of epistemic integrity and the global public good that conjointly animate the ethos of responsible and sustainable data work in the context of COVID-19.
Understanding bias in facial recognition technologies
Over the past couple of years, the growing debate around automated facial recognition has reached a boiling point.
Stein Variational Gaussian Processes
We show how to use Stein variational gradient descent (SVGD) to carry out inference in Gaussian process (GP) models with non-Gaussian likelihoods and large data volumes.
Tackling COVID-19 through Responsible AI Innovation: Five Steps in the Right Direction
Innovations in data science and AI/ML have a central role to play in supporting global efforts to combat COVID-19.
Understanding artificial intelligence ethics and safety
A remarkable time of human promise has been ushered in by the convergence of the ever-expanding availability of big data, the soaring speed and stretch of cloud computing platforms, and the advancement of increasingly sophisticated machine learning algorithms.
Cloud Computing
Cultural Vocal Bursts Intensity Prediction
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Bandit Learning in Concave N-Person Games
This paper examines the long-run behavior of learning with bandit feedback in non-cooperative concave games.
Using J-K-fold Cross Validation To Reduce Variance When Tuning NLP Models
K-fold cross validation (CV) is a popular method for estimating the true performance of machine learning models, allowing model selection and parameter tuning.
