Big Data: The good, the bad and the ugly
Join us for this online discussion, hosted as part of the Melbourne School of Psychological Sciences' 75th Anniversary.
Big tech companies like Twitter and Meta/Facebook are remarkably successful at using people's digital traces to predict and influence human behaviour. In the psychological sciences, big data is just starting to gain traction. Our panelists will discuss how big data is being used to understand beliefs, attitudes and cognitions, as well as the ethical dilemmas that this work entails.
How are insights from big data changing our theories of mind? Can big data change the translational relevance of psychological inquiry? In a world where our personal information is a commodity, who should own this data? How can we gain the insights we desire while also protecting the privacy of our participants? Join us as we grapple with some of the foundational issues for psychological science in the 21st century.
When: Monday 28 March, 11am - 12pm
Where: Online
Registrations: Via Eventbrite
Meet our panel
Professor Simon Dennis
Professor Simon Dennis is the Director of the Complex Human Data Hub (CHDH) and the head of the Memory and Language Lab in the Melbourne School of Psychological Sciences.
Simon’s research utilises large-scale real-world data, experimental paradigms and computational modelling techniques to investigate the cognitive architecture underlying memory and language. Much of his research uses experience sampling technologies to study psychological processes. He has created an extensive data collection, retrieval, visualisation and analysis ecosystem provided by Unforgettable Research Services Pty Ltd, of which he is the CEO. Simon also has an interest in privacy and the concept of participant-owned data.
Dr Simon de Deyne
Dr Simon de Deyne’s research focuses on how the mind acquires and represents word meaning through our experiences with the world and the use of language. This involves the use of computational models that encode which words go together in speech and text, in order to determine what kind of information is useful for people to understand the meaning of a word or sentence. Simon also explores how knowledge is represented, which includes mechanisms for knowledge retrieval and the evolution of the mental lexicon. On top of this, Simon studies semantic knowledge across the lifespan and how connotative meaning varies across different languages and cultures.
Dr Khandis Blake
Dr Khandis Blake is an expert on the psychology of gender relations who combines nature and nurture frameworks to understand conflict, especially between men and women. Her research has involved using large amounts of Twitter data, and addresses big issues – including how and why people compete, the manifestations of status-seeking and social climbing, and the psychological effects of hormonal birth control and sex hormones. She also explores how these issues are influenced by the interplay between sociocultural, biological, and economic forces.
Associate Professor Daniel Little
Associate Professor Daniel Little is the head of the Knowledge, Information & Learning Lab in the Melbourne School of Psychological Sciences.
Daniel’s research focuses on understanding complex decision making, which requires the integration of multiple sources of evidence that might conflict or interact in surprising ways. His research aims to reveal the processes and representations that underlie these decisions. Specifically, his research explores how our knowledge influences how we perceive and interpret new information, how we develop knowledge through experience and learning, and how our knowledge affects our decisions and behaviour.
Professor Charles Kemp
Professor Charles Kemp’s research focuses on computational models of learning and reasoning. Humans regularly make inferences that go beyond the data they have observed, and Charles attempts to characterise the knowledge that supports these inferences and to explain how this knowledge might be acquired. Charles is particularly interested in high-level cognition, and has developed models of categorisation, generalisation, causal reasoning and relational learning. This interest in categorisation has led to a line of work that explores the meanings of words in different languages.