Personalisation — Our Work — Humanising Machine Intelligence

PERSONALISATION OVERVIEW

One of AI's most commercially successful applications—and one of the drivers of innovation—has been to use data and inferences about you to provide a personalised user experience: product recommendations, micro-targeted adverts, tailored newsfeeds, tailored prices. This automated personalisation has one goal: to affect your behaviour, in the pursuit of either profit or power. In this subproject, we explore how personalisation is changing our social world now, what the goals of personalisation should be, and how to realise those goals in real socio-technical systems.

PersonalisationHMI Staff1 July 2023Sub-Project

What's wrong with automated influence?

Claire Benn and Seth Lazar ask what is wrong with online behavioural advertising and recommender systems, in this paper published in the Canadian Journal of Philosophy.

Personalisation, PublicationsHMI Staff5 November 2021Claire, Seth

Fairness in EU Data Protection Law

This entry is an analysis of the fairness principle as provided for in Article 5(1)(a) of the General Data Protection Regulation. The analysis tracks this principle's uncertain contours and also points to its constitutional foundations with reference to Article 8 of the Charter of Fundamental Rights of the European Union.

Publications, PersonalisationHMI Staff27 April 2021Damian

Children’s Privacy in Lockdown: Intersections between Privacy, Participation and Protection Rights in a Pandemic

Children and young people throughout the world have felt the effects of Coronavirus Disease 2019 and the decisions made in response to the public health crisis acutely. Questions have been raised about adequately protecting children’s privacy, as schooling, play and socialising went almost exclusively online. However, due to the historical lack of children’s rights being embedded throughout decision-making processes (including important participation rights), the effects of the increased surveillance as a result of the pandemic have not been thoroughly considered. This article pursues three objectives. First, it seeks to develop the literature on the enabling aspects of privacy for children in relation to education and play. Second, it seeks to expand the discussion on the exploitative risks endemic in not protecting children’s privacy, including not only violent harms, but commercial exploitation. Third, it suggests some policy responses that will more effectively embed a children’s rights framework beyond the ‘parental control’ provisions that dominate child-specific data protection frameworks.

Publications, PersonalisationHMI Staff27 April 2021Damian

Esports - child’s play during COVID-19

Children's Rights and eSports.

Publications, PersonalisationHMI Staff27 April 2021Damian

Radflow: A Recurrent, Aggregated, and Decomposable Model for Networks of Time Series

We propose a new model, Radflow, for networks of time series that influence each other.

Publications, PersonalisationHMI Staff31 March 2021Lexing

Describing and Predicting Online Items with Reshare Cascades via Dual Mixture Self-exciting Processes

It is well-known that online behavior is long-tailed, with most cascaded actions being short and a few being very long. A prominent drawback in generative models for online events is the inability to describe unpopular items well. This work addresses these shortcomings by proposing dual mixture self-exciting processes to jointly learn from groups of cascades.

Publications, PersonalisationHMI Staff1 December 2020Lexing

SupMMD: A Sentence Importance Model for Extractive Summarization using Maximum Mean Discrepancy

We present SupMMD, a novel technique for generic and update summarization of document collections based on the maximum mean discrepancy from kernel two-sample testing. SupMMD combines both supervised learning for salience and unsupervised learning for coverage and diversity.

Publications, PersonalisationHMI Staff1 December 2020Lexing

EthicalAI - Navigating Bias, Fairness and Ethics in Machine Learning

In this QuantumBlack Australia virtual Meetup, the ethics of artificial intelligence was discussed with the Gradient Institute and HMI. Click through for more information.

Education, Events, Personalisation, Algorithmic-EthicsHMI Staff20 October 2020Seth, Tiberio, Kimberlee

Can policymakers detoxify social media?

Can policymakers detoxify social media? Listen to HMI CI Jenny Davis, Dr Jennifer Hunt, and Yun Jiang discuss online hate, anti-social behaviour on digital platforms, and what policymakers can do about it.

Policy, Media, PersonalisationHMI Staff10 September 2020Jenny

Linking Online Attention to Measurable Actions (Grant)

In this project, we aim to link attention metrics and communication strategies to real world actions. In particular, we start by contrasting popularity and engagement of online social movements. We then link the measurements to real-world metrics of these activities, as measured by participant turnout, election outcome, legislative success, and others. Answers to these questions will empower content producers, consumers, and hosting platforms to channel attention in mutually beneficial, and socially responsible ways.

Grants, PersonalisationHMI Staff17 August 2020Lexing

Fairness and the General Data Protection Regulation

This report analyses the literature exploring the meaning of the fairness principle in EU data protection law. Fairness is included in both Article 8(2) of the Charter of Fundamental Rights of the European Union (the Charter) and Article 5(1)(a) of the General Data Protection Regulation (GDPR). Despite these foundations however, the fairness principle has been largely unexplored and remains broadly undefined in the data protection framework, case law and guidance literature.

Policy, PersonalisationHMI Staff14 August 2020Damian