mg摆脱电子游戏数据科学研究所

Data Science Institute

We aim to set the global standard for a truly interdisciplinary approach to contemporary data-driven research challenges. Established in 2015, the Data Science Institute (DSI) has over 300 members and has raised nearly £35 million in research grants.

Research Themes

Latest Events

DSI不平等撤退-米德兰酒店,莫克姆

On the 19th and 20th May the Society theme had a fantastic two-day retreat on the topic of Inequalities, 标志着DSI内部一个新的战略重点的启动. Academics from across the university came together for lively interdisciplinary discussion exploring a wide range of inequalities and discovering connections between fields of health, sociology, politics and policy, economics, environment, computing, and design. We look forward to building on these conversations with further opportunities for members to meet up and collaborate in the months to come.

撤退的第一天提供了4组会谈, 其间穿插着吃饭的时间, 高质量的谈话和一些放松.

CLUSTER A

当地的不平等:理解和丰富该地区

  • Jo Knight (Eden North)
  • 艾玛·哈利迪(健康研究)
  • 米歇尔·柯林斯(健康研究)
  • 克里斯·博伊科(兰卡斯特当代艺术学院)

CLUSTER B

国际不平等:应对全球挑战

  • Michaela Benson (Sociology)
  • 莎莉·卡伍德(mg游戏中心环境中心)
  • Jasmine Fledderjohann(社会学)录音

CLUSTER C

不平等的意义:大规模数据集的价值

  • 伊恩•沃克(mg摆脱电子游戏管理学院)
  • Katie Hunter (Law School)
  • Steffi Doebler (Sociology)
  • 希瑟·布朗(健康研究)

CLUSTER D

Inequalities: 基础设施和环境的政治

  • 高登·沃克(mg游戏中心环境中心)
  • 布兰·诺尔斯(计算机与通信学院)
  • 马修·约翰逊(政治与政策)

第2天提供了当前资金状况的信息, 大学新的安全数据科学基础设施, 为你自己的工作提供自由的时间, 思考或合作对话.

演讲的是下面这些人

不平等研究:资金状况(奥杰特·杜赫斯特), 高级研发经理, RES)

NIHR North West Research Support and Development Team, Julie Mugarza and James Connolly plus Q&A

兰卡斯特的安全数据科学基础设施:凯伦·布罗德赫斯特 & Geraint Harries (ISS)观众Q&A

The Midland Hotel

尊敬的发言人——布伦特·米特尔施塔特博士, 今年5月,他在DSI上发表了mg游戏中心“谈论偏见”的演讲, fairness, 以及人工智能领域的非歧视法律"

来自牛津互联网研究所的Brent Mittelstadt博士为DSI做了演讲

Abstract: Western societies are marked by diverse and extensive biases and inequality that are unavoidably embedded in the data used to train machine learning. 用有偏差的数据训练的算法可以, without intervention, produce biased outcomes and increase the inequality experienced by historically disadvantaged groups. Recognising this problem, much work has emerged in recent years to test for bias in machine learning and AI systems using various fairness and bias metrics. Often these metrics address technical bias but ignore the underlying causes of inequality and take for granted the scope, significance, 以及道德上对现存不平等的接受程度.

现在正在录音 YouTube.

In this talk I will introduce the concept of “bias preservation” as a means to assess the compatibility of fairness metrics used in machine learning against the notions of formal and substantive equality. The fundamental aim of EU non-discrimination law is not only to prevent ongoing discrimination, but also to change society, policies, and practices to ‘level the playing field’ and achieve substantive rather than merely formal equality. Based on this, I will introduce a novel classification scheme for fairness metrics in machine learning based on how they handle pre-existing bias and thus align with the aims of substantive equality. Specifically, I will distinguish between ‘bias preserving’ and ‘bias transforming’ fairness metrics. 这种分类制度旨在弥合平等概念之间的差距, non-discrimination law, 以及如何衡量机器学习的公平性和偏见的决策. 偏见转换指标对于在实践中实现实质性平等至关重要. I will conclude by introducing a bias preserving metric ‘Conditional Demographic Disparity’ which aims to re-frame the debate around AI fairness, 把它从正确的公平指标上移开, 以及道德上的认同, legally, socially, or politically preferable conditioning variables according to the requirements of specific use cases.

Research Themes

Data Science at Lancaster was founded in 2015 on Lancaster’s historic research strengths in Computer Science, 统计与运筹学. The environment is further enriched by a broad community of data-driven researchers in a variety of other disciplines including the environmental sciences, health and medicine, 社会学和创意艺术.

  • Foundations

    Foundations research sits at the interface of methods and application: with an aim to develop novel methodology inspired by the real-world challenge. 这些可能是mg游戏中心人员运输,货物运输的研究 & 服务、能源消耗和全球天气模式变化的影响.

  • Health

    健康主题范围广泛. Current areas of strength include spatial and spatiotemporal methods in global public health, 临床试验的设计和分析, 流行病预测和人口建模, 卫生信息学和遗传学.

  • Society

    Data Science has brought new approaches to understanding long-standing social problems concerning energy use, climate change, crime, migration, the knowledge economy, ecologies of media, 日常生活中的设计与沟通, 或者是金融化经济体的财富分配问题.

  • Environment

    The focus of the environment theme has been to seek methodological innovations that can transform our understanding and management of the natural environment. Data Science will help us understand how the environment has evolved to its current state and how it might change in the future.

Professor Christina Pagel

Professor Christina Pagel gave a talk to DSI on 'What Independent SAGE has taught me about the current biggest issues in light of COVID-19 and where data science can help'. 请听她对当前大流行的见解和观察.

DSI Society - Inequalities

A recording is now available of the launch of the book by Amy Clair and our very own Jasmine Fledderjohann and Bran Knowles entitled, “社会政策和人权的分水岭时刻?:新冠肺炎后英国的下一步发展”. The event included an overview of the key concepts and themes in the book; invited talks from Aaron Reeves (University of Oxford), Kayleigh Garthwaite(伯明翰大学), 丹尼尔·格林(马里兰大学).

DSI Video

In 2019 we recorded a short film to document the research themes and activities and to explain the Institute structure of Data Science at Lancaster.

Latest News