A global group of AI experts and data scientists have put together a voluntary framework for developing AI products safely. There are 25,000 members including Meta, Google and Samsung staff with a checklist of 84 questions for developers to consider at the start of an AI project. Data protection laws from various territories are included, and whether it is clear to a user that they are interacting with AI amongst others. There are questions for individuals developers, teams involved and for people who may be testing products. The public are invited to submit their questions. As the field of AI is rapidly evolving questions around bias, legal compliance and transparency and fairness, developers can contribute to building responsible and trustworthy AI systems. Link https://www.bbc.co.uk/news/technology-66225855
Category: Uncategorized
Review: Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts. Justin Grimmer and Brandon M.Stewart (Human Review)
Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts. Justin Grimmer and Brandon M.Stewart
This paper although published nearly ten years ago still has some valid points in today’s world as it discusses that “language is the medium for politics” and policy, whether spoken or written. In our quest to understand politics, from terrorist manifestos to peace treaties, we need to know what political actors are actually saying. The authors caution around using automated methods as the premise of applying careful human thought and robust validation are needed to ensure rigour. But with today’s ever evolving technology is this still the case?
To understand politics we need to ascertain what is actually being said and by whom, in whatever medium it is delivered. However, the volume of material is massive and hiring people to read and code is expensive and scholars cannot do it all themselves. Automated content analysis methods can make this type of analysis possible. The authors do state that automated methods “amplify and augment” careful reading and thoughtful analysis, and their paper takes the reader though all the steps needed for this content analysis. Firstly acquiring the documents, pre-processing them and seeing if they meet the research objective, followed by classification, categorisation and then unpacking the content further. Automated content analysis methods can make the previously impossible possible. Despite the authors initial reservations they offer guidelines on this “exciting area of research” minimising misconceptions and errors and describe “best practice validations across diverse research objectives and models”. Four principals of automated text analysis are identified and the authors encourage revisiting these often during research, these are as follows:
1. All quantitative models of language are wrong – but some are useful. i.e. a complicated dependency structure in a sentence could change the meaning.
2. Quantitative methods for text amplify resources and augment humans.
3. There is no globally best method for text analysis. i.e. there are a lot of different packages available, one of which may suit a particular dataset better than another.
4. Validate, validate, validate. i.e. avoid the blind use of any one method without validation.
The authors point out that automated content analysis methods provide many tools that can be used to measure what is of interest, there is no one size fits all. Whichever tool is chosen needs to be content specific. New texts probably need new methods and ten years ago they identified that commonalities would allow “scholars to share creative solutions to common problems”. Important questions could be answered by the analysis of large collections of texts, but if the methods are applied without rigour then few relevant answers will be forthcoming. When undertaking text analysis it is important to realise the limits of statistical models and the field of political science will be revolutionised by the application of automated models.
The overwhelming message of this paper is that textural measurement, the discovery of new methods and inference points allow us to build upon scientific interpretation and theory, and the journey does indeed continue at pace. Machine learning techniques have revolutionised our ability to analyse vast quantities of text, data and images rapidly and cheaply.
Link to paper: https://web.stanford.edu/~jgrimmer/tad2.pdf UK Defence Science and Technology Laboratory
CAISS goes to AI UK, London March 2023
Around 3,000 delegates attended the QE2 Centre for AI UK. One of the most popular sessions dealt with the much hyped ChatGPT and was delivered by Gary Marcus, Emeritus Professor of Psychology and Neural Science at New York University. He began by stating that although we have a lot of individual AI solutions (for example, GPS) so far there is not a general purpose system that will do everything for us. ChatGPT is the one most advanced and reliable system to date, taking in massive amounts of data and has good guardrails, so it will not for example write an article on the benefits of eating glass! But is it the universal panacea?
Problems:
- It will make things up and it can even give references for fake information, there is an illusion that adding more information will mitigate the incorrect outputs.
- After completing eight million chess games, it still does not understand the rules.
- Driverless cars involves deep learning, this is not AI. This technology is just memorising situations and is unable to cope with unusual events. The system cannot reason in the
- same way that a human being does.
- If the circumstance is not in the training set it won’t know what to do, in Chat GPT4
- (which is the latest version) we do not know yet what that training data set is?
Positives:
- It can help with de-bugging, it can write pieces of code that are 30% correct and then humans can fix them, this is easier than starting from scratch, the “best use case”.
- It can write letters, stories, songs and prose, it is fun, fluent and good with grammar.
- Large Language Models (LLMs) can be used to write articles – looks good but they have errors. If someone does not know the facts though it could be believed. But if it is a story and fiction, does this matter?
Worries and considerations:
Chat GPT is being used at scale, leading to misinformation and a possible polluting of democracy, there is an opportunity for fake information, potential discriminatory, stereotypical or even offensive responses. The 2024 US Presidential Election could be a concern, as the technology could be used by State Actors or as an advertising tool – leading to a spread of misinformation that appears plausible. It can write fictitious news reports, describe data etc. e.g. Covid 19 versus vaccines, the results will look authoritative. This could result in millions of fake tweets/posts in a day output via “troll farms”. Large Language Models (LLM) without guardrails are already being used on the dark web. ChatGPT has been used in a programme to solve CAPTURES – when challenged the bot said it was a person with a visual disability! Already it is being used in credit card scams and phishing attacks.
Classical AI is about facts, LLM’s do not know how to fact check e.g. Elon Musk has died in a car crash – we can check this as humans. With LLM’s, as this is such a wide and fast moving area, should we be looking at them in the same way that we would look at a new drug? Possible controlled releases with a pause in place for a “safety check”?
AI literacy is important for future generations – understanding the limits is crucial, people still need to think in a critical way. Is a coordinated campaign needed to fully understand and warn about the limits of such technology?
Other presentations included Professor Lynn Gladden on Integrating AI for Science and Government, Public Perceptions of AI, how we can “do better in data science and AI”, the on-line safety bill, creating economic and societal impact, what can data science do for policy makers and individual skills for global impact. Overall it was a fascinating two days with many opinions and high profile speakers under the overarching banner of open research, collaboration and inclusion.
CREST Conference BASS22– Lancaster July 2022
The Behavioural and Social Sciences in Security conference BASS22 was held in July at Lancaster University. An International audience were brought together to enhance their understanding of the psychological and social drivers of threats to national security with an explanation of skills, technologies and protective security measures with two workshops on addressing bias in computational social science.
Highlights from the workshops included:
- Can bias ever be removed in the long term;
- Problems can be introduced when there is human interaction in the machine.
- Do Social Science theories introduce bias?
- How can we use models to our advantage without introducing bias?
- One key takeaway was, “It is not how the model works, but how can we use the model to our advantage?”
And we’re off………
The Computational Social Science Hub (CSS Hub) is a collaboration between The Alan Turing Institute (Turing), Lancaster University and Dstl, sitting under the new Defence Centre for AI Research (DCAR). CSS largely focusses on computational approaches to social science – often using large scale data to investigate and model human behaviour and activity. It is made up of social and computer scientists, collaborating on defence and security related problems. After a successful first year, August allowed for a sunny kick off at the Turing Institute in the British library. The team discussed a range of research options to be conducted in the coming years. Our upcoming work covers a range of topics such as Bias, Virtual Reality and Facial Recognition. Additionally, we hope to recruit a PHD student to encourage early researchers into this emerging field.
A primary goal of the CSS Hub is to foster a collaborative community, encouraging work between disciplines. To achieve this we are launching a community group with the Turing, and this regular newsletter focussed on highlighting new CSS work and how it impacts Defence.