Mhairi Aitken

Orcid: 0000-0002-4654-9803

According to our database1, Mhairi Aitken authored at least 17 papers between 2018 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
Dataset
Other 

Links

On csauthors.net:

Bibliography

2024
AI Sustainability in Practice Part Two: Sustainability Throughout the AI Workflow.
CoRR, 2024

AI Ethics and Governance in Practice: An Introduction.
CoRR, 2024

AI Fairness in Practice.
CoRR, 2024

AI Sustainability in Practice Part One: Foundations for Sustainable AI Projects.
CoRR, 2024

2022
Data Justice in Practice: A Guide for Developers.
CoRR, 2022

In Private, Secure, Conversational FinBots We Trust.
CoRR, 2022

Data Justice Stories: A Repository of Case Studies.
CoRR, 2022

Advancing Data Justice Research and Practice: An Integrated Literature Review.
CoRR, 2022

Human rights, democracy, and the rule of law assurance framework for AI systems: A proposal.
CoRR, 2022

2021
Artificial intelligence, human rights, democracy, and the rule of law: a primer.
CoRR, 2021

2020
Establishing a social licence for Financial Technology: Reflections on the role of the private sector in pursuing ethical data practices.
Big Data Soc., January, 2020

Public preferences regarding data linkage for research: a discrete choice experiment comparing Scotland and Sweden.
BMC Medical Informatics Decis. Mak., 2020

Technologies for Trustworthy Machine Learning: A Survey in a Socio-Technical Context.
CoRR, 2020

The relationship between trust in AI and trustworthy machine learning technologies.
Proceedings of the FAT* '20: Conference on Fairness, 2020

Simulating the Effects of Social Presence on Trust, Privacy Concerns & Usage Intentions in Automated Bots for Finance.
Proceedings of the IEEE European Symposium on Security and Privacy Workshops, 2020

Keeping it Human: A Focus Group Study of Public Attitudes Towards AI in Banking.
Proceedings of the Computer Security - ESORICS 2020 International Workshops, 2020

2018
Who benefits and how? Public expectations of public benefits from data-intensive health research.
Big Data Soc., July, 2018


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