PROJECT SUMMARY

Man working on laptop

Quantifying and Measuring Bias and Engagement

Focus Areas: News & Media, Health
Research Programs: Machines, Data
Status: Active

Automated decision-making systems and machines – including search engines and intelligent assistants – are designed, evaluated, and optimised by defining frameworks that model the users who are going to interact with them. These models are typically a simplified representation of users (e.g., using the relevance of items delivered to the user as a surrogate for system quality) to operationalise the development process of such systems. A grand open challenge is to make these frameworks more complete, by including new aspects such as fairness, that are as important as the traditional definitions of quality, to inform the design, evaluation and optimisation of such systems.

Recent developments in machine learning, information access, and AI communities attempt to define mechanisms to minimise the creation and reinforcement of unintended cognitive biases.

However, there are a number of research questions related to quantifying and measuring bias and engagement that remain unexplored:
– Is it possible to measure bias by observing users interacting with search engines, or intelligent assistants?
– How do users perceive fairness, bias, or trust? How can these perceptions be measured effectively?
– To what extent can sensors in wearable devices and interaction logging (e.g., search queries, app swipes, notification dismissal, etc) inform the measurement of bias and engagement?
– Are the implicit signals captured from sensors and interaction logs correlated with explicit human ratings w.r.t. bias and engagement?

The research aims to address the research questions above by focusing on information access systems that involve automated decision-making components. By partnering with experts in fact-checking, we use misinformation management as the main scenario of study, given that bias and engagement play an important role in three main elements of the automated decision-making processes: the user, the system, and the information that is presented and consumed.

The methodologies considered to address these questions include lab user studies (e.g., observational studies), and the use of crowdsourcing platforms (e.g., Amazon Mechanical Turk). The data collection processes include: logging human-system interactions; sensor data collected using wearable devices; and questionnaires.

PUBLIC RESOURCES

Person working on laptop on wooden desk next to window

Open Source Software: Factchecking – Presentations

Target audience: Researchers, Software Developers
Code type: Python

View on Github

PUBLICATIONS

Human-AI Cooperation to Tackle Misinformation and Polarization, 2023

Spina, D., Sanderson, M., et al.

Journal article

Examining the Impact of Uncontrolled Variables on Physiological Signals in User Studies for Information Processing Activities, 2023

Ji, K., Spina, D., et al.

Conference paper

Can Generative LLMs Create Query Variants for Test Collections? 2023

Alaofi, M., Sanderson, M., et al.

Conference paper

Mitigating Negative Transfer with Task Awareness for Sexism, Hate Speech, and Toxic Language Detection, 2023

Spina, D., Rosso, P., Felipe Magnossão de Paula, A.

Conference paper

Do Social Media Users Change Their Beliefs to Reflect those Espoused by Other Users? 2023

Alknjr, H.

Conference paper

How do Human and Contextual Factors Affect the Way People Formulate Queries? 2023

Abu One, N.

Conference paper

Towards Detecting Tonic Information Processing Activities with Physiological Data, 2023

Ji, K., Hettiachchi, D., et al.

Conference paper

Ranking Interruptus: When Truncated Rankings Are Better and How to Measure That, 2022

Spina, D., et al.

Conference paper

Where Do Queries Come From? 2022

Alaofi, M., Spina, D., et al.

Conference paper

User-centered Non-factoid Answer Retrieval, 2022

Alaofi, M.

Conference paper

A Crowdsourcing Methodology to Measure Algorithmic Bias in Black-box Systems: A Case Study with COVID-related Searches, 2022

Scholar, F., Spina, D., Chia, H., Le, B.

Conference paper

AWARDS

2023 Pervasive and Ubiquitous Computing (UbiComp) International Symposium on Wearable Computing (ISWC)
Student Challenge Award
zzzGPT: An Interactive GPT Approach to Enhance Sleep Quality
Yonchanok (Pro) KhaokaewKaixin Ji, Marwah Alaofi, Hiruni Kegalle, Thuc Hanh Nguyen (UNSW) and Prof Flora Salim

2023 Pervasive and Ubiquitous Computing (UbiComp) International Symposium on Wearable Computing (ISWC)
Best Poster Award
Towards Detecting Tonic Information Processing Activities with Physiological Data’
Dr Damiano SpinaKaixin Ji, Prof Falk Scholer, Dr Danula Hettiachchi and Prof Flora Salim

17th Conference on Evaluation of Information Access Technologies (NTCIR-17)
Best Oral Presentation
Sachin Cherumanal Pathiyan

RESEARCHERS

Dr Damiano Spina

Dr Damiano Spina

Lead Investigator,
RMIT University

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ADM+S Chief Investigator Anthony McCosker

Assoc Prof Anthony McCosker

Chief Investigator,
Swinburne University

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ADM+S Investigator Flora Salim

Prof Flora Salim

Chief Investigator,
UNSW

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ADM+S Chief Investigator Mark Sanderson

Prof Mark Sanderson

Chief Investigator,
RMIT University

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Danula Hettiachchi

Dr Danula Hettiachchi

Associate Investigator,
RMIT University

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ADM+S Associate Investigator Jenny Kennedy

Assoc Prof Jenny Kennedy

Associate Investigator,
RMIT University

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ADM+S Chief Investigator Falk Scholer

Prof Falk Scholer

Associate Investigator,
RMIT University

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ADM+S Member

Nuha Abu Onq

PhD Student,
RMIT University

Marwah Alaofi

Marwah Alaofi

PhD Student,
RMIT University

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ADM+S Member

Hmdh Alknjr

PhD Student,
RMIT University

Sachin Pathiyan Cherumanal

PhD Student,
RMIT University

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Kaixin Ji

Kaixin Ji

PhD Student,
RMIT University

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PARTNERS

ABC logo

Australian Broadcasting Corporation

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AlgorithmWatch Logo

Algorithm Watch (Germany)

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Bendigo Health logo

Bendigo Hospital

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Google Logo

Google Australia

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RMIT ABC Fact Check Logo

RMIT ABC Fact Check

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