Search Results for author: David Leslie

Found 21 papers, 2 papers with code

AI Sustainability in Practice Part Two: Sustainability Throughout the AI Workflow

no code implementations19 Feb 2024 David Leslie, Cami Rincon, Morgan Briggs, Antonella Perini, Smera Jayadeva, Ann Borda, SJ Bennett, Christopher Burr, Mhairi Aitken, Michael Katell, Claudia Fischer, Janis Wong, Ismael Kherroubi Garcia

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.

AI Sustainability in Practice Part One: Foundations for Sustainable AI Projects

no code implementations19 Feb 2024 David Leslie, Cami Rincon, Morgan Briggs, Antonella Perini, Smera Jayadeva, Ann Borda, SJ Bennett, Christopher Burr, Mhairi Aitken, Michael Katell, Claudia Fischer, Janis Wong, Ismael Kherroubi Garcia

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 Fairness in Practice

no code implementations19 Feb 2024 David Leslie, Cami Rincon, Morgan Briggs, Antonella Perini, Smera Jayadeva, Ann Borda, SJ Bennett, Christopher Burr, Mhairi Aitken, Michael Katell, Claudia Fischer, Janis Wong, Ismael Kherroubi Garcia

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.

Ethics Fairness +1

Don't "research fast and break things": On the ethics of Computational Social Science

no code implementations12 Jun 2022 David Leslie

This article is concerned with setting up practical guardrails within the research activities and environments of CSS.

Ethics

Data Justice in Practice: A Guide for Developers

no code implementations12 Apr 2022 David Leslie, Michael Katell, Mhairi Aitken, Jatinder Singh, Morgan Briggs, Rosamund Powell, Cami Rincón, Antonella Perini, Smera Jayadeva, Christopher Burr

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.

Fairness

Advancing Data Justice Research and Practice: An Integrated Literature Review

no code implementations6 Apr 2022 David Leslie, Michael Katell, Mhairi Aitken, Jatinder Singh, Morgan Briggs, Rosamund Powell, Cami Rincón, Thompson Chengeta, Abeba Birhane, Antonella Perini, Smera Jayadeva, Anjali Mazumder

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.

Human rights, democracy, and the rule of law assurance framework for AI systems: A proposal

no code implementations6 Feb 2022 David Leslie, Christopher Burr, Mhairi Aitken, Michael Katell, Morgan Briggs, Cami Rincon

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.

Management

Does "AI" stand for augmenting inequality in the era of covid-19 healthcare?

no code implementations30 Apr 2021 David Leslie, Anjali Mazumder, Aidan Peppin, Maria Wolters, Alexa Hagerty

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

no code implementations2 Apr 2021 David Leslie, Christopher Burr, Mhairi Aitken, Josh Cowls, Michael Katell, Morgan Briggs

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)

no code implementations20 Mar 2021 David Leslie, Morgan Briggs

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

no code implementations6 Feb 2021 David Leslie

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.

ARC

Understanding bias in facial recognition technologies

no code implementations5 Oct 2020 David Leslie

Over the past couple of years, the growing debate around automated facial recognition has reached a boiling point.

Stein Variational Gaussian Processes

1 code implementation25 Sep 2020 Thomas Pinder, Christopher Nemeth, David Leslie

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.

Gaussian Processes Variational Inference

Tackling COVID-19 through Responsible AI Innovation: Five Steps in the Right Direction

no code implementations15 Aug 2020 David Leslie

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

no code implementations11 Jun 2019 David Leslie

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 +2

Bandit Learning in Concave N-Person Games

no code implementations NeurIPS 2018 Mario Bravo, David Leslie, Panayotis Mertikopoulos

This paper examines the long-run behavior of learning with bandit feedback in non-cooperative concave games.

Stochastic Optimization

Using J-K-fold Cross Validation To Reduce Variance When Tuning NLP Models

1 code implementation COLING 2018 Henry Moss, David Leslie, Paul Rayson

K-fold cross validation (CV) is a popular method for estimating the true performance of machine learning models, allowing model selection and parameter tuning.

Document Classification General Classification +4

Cannot find the paper you are looking for? You can Submit a new open access paper.