Artificial intelligence (AI) glossary
This glossary compiles key terms used in recent POST research on artificial intelligence (AI).
Find out how we communicate uncertainty in our research, view our readability scores, and find out what we are doing to continuously improve our editorial processes.
Uncertainty is a fundamental part of all research. It requires that those who produce research and those who communicate it to others, can recognise it and understand how best to explain it. Uncertainty in research can take several forms:
There has been a lot of research into how people interpret and understand uncertainty, and how best to communicate it clearly. High quality communication about uncertainty changes how people understand and interpret research, and the level of trust they place in it.
Several reports outline the consequences of poor communication about uncertainty and highlight best practice.[1],[2],[3] In a policy context, failing to effectively communicate uncertainty in research evidence can lead to suboptimal decisions.[4]
This is why, when summarising research evidence for parliamentarians, communicating uncertainty is critical to presenting research findings in a responsible, transparent and meaningful way. Those providing impartial research services for parliamentarians must therefore be clear about the strengths and weaknesses of studies, they must explain numerical data in ways that are easy to understand, and they have to be clear about how uncertainties might be resolved and when.
POST publishes three types of reports:
In order to understand how POST communicates uncertainty in research in these briefings, and if this might be improved, we worked with Dr John Kerr (Winton Centre for Risk and Evidence Communication, University of Cambridge), who joined POST on a Parliamentary Academic Fellowship in 2021. The project involved two analyses. One examined the readability and use of uncertain language across all POST’s outputs. The other was an in-depth content analysis of how uncertainty about evidence is communicated in a sample of 40 recent health-related briefings.
Figure 1 shows readability scores across POST’s three briefing types, calculated using the Flesch Reading Ease formula. The formula uses the length of words and sentences to calculate a score for a given text, with higher numbers meaning easier to read.
Most POST briefings had readability scores in the ‘difficult’ range of 30-50 on the readability scale, indicating they would be challenging for people without a university education. On average, Rapid Responses were more readable than POSTnotes and POSTbriefs. Nearly all POST documents were more readable than the average summary (called an abstract) in a scientific research paper (approximately a 10 on the readability scale), but less readable than news articles in The Times newspaper (about 49). Analysis shows that the average readability of POST briefings does not vary by author and has not changed over time.
Considering the use of uncertain language (words like ‘unsure’ and ‘approximately’), there are no significant differences, on average, between POST’s different report types. The briefings with the most uncertain language were those that focus on future scenarios. Examples include population growth, natural hazards and climate change.
Figure 2 shows how the percentage of uncertain words in Rapid Responses decreased over time. Almost all Rapid Responses focused on COVID-19. This pattern tracks with the overall trajectory of decreasing uncertainty, as the scientific understanding of the virus and its impact increased.
POST briefings frequently communicate uncertainty about research evidence. However, some ways of expressing uncertainty in POST’s work are more common than others.
Uncertainty around specific statistics is rarely quantified, such as by providing a range around a given number. It is more often expressed verbally through words such as approximately, or around.
Briefings often include information to indicate how much confidence should be placed in research evidence. For example:
Based on the recommendations in the report POST is updating its training and guidance for authors, making better use of relevant resources, and enhancing editorial practices.
To improve readability, POST will be using software tools to provide authors with greater insight on readability during drafting and editorial stages. POST aims to publish briefings that have higher readability scores, scoring at least 30 on the Flesch scale (to avoid the ‘very difficult’ category).
POST will be working to ensure that best practice in communicating uncertainty is applied to its research. This involves updating training and guidance for staff, and by providing clear information on how to communicate uncertainty in all its forms. Editorial processes will also be amended so that the communication of uncertainty is considered specifically for each publication. Examples include:
For general information about this project please contact Dr Sarah Bunn at post@parliament.uk.
For academic inquiries about this research please contact Dr John Kerr, Winton Centre for Risk and Evidence Communication, University of Cambridge.
[1] How to Communicate Uncertainty, 2020, Full Fact
[2] Uncertainty Toolkit for Analysts in Government, 2020, Government Actuary’s Department
[3] Communicating Quality, Uncertainty and Change, 2018, Office for National Statistics
[4] Fischhoff Baruch, Communicating Uncertainty: Fulfilling the Duty to Inform, Issues in Science and Technology 28, No. 4 (Summer 2012)
Photo by Marcin Nowak on Unsplash
This glossary compiles key terms used in recent POST research on artificial intelligence (AI).
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