Royal Society Machine Learning Public Dialogue
As part of the machine learning working group, the Royal Society engaged in public dialogue to understand perspectives on machine learning technologies. The work took place across January and February 2016 and the results were published in April 2017.
A press article associated with the release is available from the Guardian here.
Executive Summary: Background and Objectives
Machine learning is a way of programming a system to learn from data and self-improve. Traditionally, programmers set static instructions to tell a computer how to solve a problem, step by step. In contrast, machine learning algorithms can identify patterns in data and use this information to learn how to solve the problem at hand. Machine learning algorithms enable the analysis of much larger quantities of data than a human could work with, and, as a result, can identify complex patterns or relationships. The models built on the basis of this analysis can then be used to make predictions or decisions.
The Royal Society launched a project on machine learning in November 2015, which aims to increase awareness of this technology, demonstrate its potential, and highlight the opportunities and challenges it presents. The project’s focus is on the current and near-term (5-10 years) applications of machine learning. The UK public is a key audience for this project, and public engagement is an integral part of the programme of work. The Royal Society therefore commissioned Ipsos MORI to carry out research into public knowledge of, and attitudes towards, machine learning.
Ipsos MORI’s task for the research was to create an evidence base about public perceptions around the potential benefits and risks of the technology, to inform the Royal Society’s policy project on machine learning. Exploring these issues required an approach involving depth, breadth and iterative engagement. As such, the methodology used in this research was designed to incorporate three elements: a quantitative survey, public dialogues, and an online community.
Between 22 January and 8 February 2016, 978 face-to-face interviews were conducted with members of the public across the UK. All interviews were carried out in-home, using computer-assisted personal interviewing (CAPI) on Ipsos MORI’s weekly omnibus survey. Final data was weighted to ensure the individuals selected for interview were representative of the national population. 1 This was followed up by qualitative research, which involved two weekend-long dialogue events in Birmingham and London, along with two evening focus groups in Oxford and Huddersfield.
The Royal Society public dialogue report can be downloaded here.
Click to see what the machine says about the archive and the book
Machine Commentary
NL: This report was published in April 2017 based on work done across 2016. Can you summarise what the public perspective was on machine learning and suggest to what extent the technology community has addressed the hopes and fears reflected in the report?
Claude: I’ll analyze the key public perspectives from 2016-2017 and reflect on how they’ve been addressed since then.
Key Public Perspectives from the Report:
Progress Since 2017:
Overall Assessment: The technology community has made meaningful progress addressing some concerns raised in the report, particularly around:
However, several core tensions identified in the report remain unresolved:
The public’s instinct for a balanced, human-centered approach to ML deployment appears prescient. While technical capabilities have advanced significantly since 2017, many of the social and ethical considerations highlighted by the public remain highly relevant to ongoing debates about responsible AI development.
Let me know if you would like me to expand on any particular aspect of this analysis.
NL: Can you augment your summary with perspectives on different domains of application from the report (what public saw as pros and cons).
Claude: I’ll analyze the domain-specific perspectives from the report, looking at how the public viewed different applications of machine learning in 2016-2017:
Cons:
Cons:
Cons:
Cons:
Cons:
Cons:
Cons:
Cons:
Common Themes Across Domains:
This domain-specific analysis shows how public acceptance varied significantly based on application context, with healthcare showing highest potential acceptance (with caveats) and creative arts showing lowest perceived value, despite also having lowest perceived risk.