Month: February 2022

Have you ever noticed how often we use metaphors in our day-to-day language? The words we use matter, and metaphorical language paints mental pictures imbued with hidden and often misplaced assumptions and connotations. In looking at the impact of metaphorical images to represent the technologies and concepts covered within the term artificial intelligence, it can be illuminating to drill down into one element of AI – that of data.

Hattusia recently teamed up with Jen Persson at Defend Digital Me and The Warren Youth Project to consider how the metaphors we attach to data impacts UK policy, amalgamating in a data metaphors report.

In this report, we explore why and how public conversations about personal data don’t work. We suggest what must change to better include children for the sustainable future of the UK national data strategy.

Our starting point is the influence of common metaphorical language: how does the way we talk about data affect our understanding of it? In turn, how does this inform policy choices, and how children feel about the use of data about them in practice?

Metaphors are routinely used by the media and politicians to describe something as something else. This brings with it associations made in response in the reader or recipient. We don’t only see the image but receive the author’s opinion or intended meaning on something.

Metaphors are very often used to influence the audience’s opinion. This is hugely important because policymakers often use metaphors to frame and understand problems – the way you understand a problem has a big impact on how you respond to it and construct a solution.

Looking at children’s policy papers and discussions about data in Parliament since 2010, we worked with Julia Slupska to identify three metaphor groups most commonly used to describe data and its properties.

We found that ​​a lot of academic and journalistic debates frame data as ‘the new oil’, for example; while some others describe it as toxic residue or nuclear waste. The range of metaphors used by politicians is more narrow and rarely as critical.

Through our research, we’ve identified the three most prominent sets of metaphors for data used in reports and policy documents. These are:

• Fluid: data can flow or leak
• A resource/fuel: data can be mined, can be raw, data is like oil
• Body or bodily residue: data can be left behind by a person like footprints; data needs protecting

In our workshop at The Warren Youth Project , the participants used all of our identified metaphors in different ways. Some talked about the extraction of data being destructive, while others compared it to a concept that follows you around from the moment you’re born. Three key themes emerged from our discussions:

• Misrepresentation: the participants felt that data was often inaccurate, or used by third parties as a single source of truth in decision-making. In these cases, there was a sense that they had no control over how they were perceived by law enforcement and other authority figures.
• Power hierarchies and abuses of power: this theme came out via numerous stories about those with authority over the participants having seemingly unfettered access to their data, thus enforcing opaque processes, leaving the participants powerless and with no control.
• The use of data ‘in your best interest’: there was unease expressed over data being used or collected for reasons that were unclear and defined by adults, leaving children with a lack of agency and autonomy.

When looking into how children are framed in data policy, we found they are most commonly represented as criminals or victims, or simply missing in the discussion. The National Data Strategy makes a lot of claims of how data can be of use to society in the UK, but only mentions children twice and mostly talks about data like it is a resource to be exploited for economic gain.

The language in this strategy and other policy documents is alienating and dehumanises children into data points for the purpose of predicting criminal behaviour or to attempt to protect them from online harm. The voices of children themselves are left out of the conversation entirely. We propose new and better ways to talk about personal data.

To learn more about our research, watch this video (produced by Matt Hewett) in which I discuss the findings. It breaks down exactly what the three groups were, how the experiences which young people and children had related to data linked back to those three groups, and how changing the metaphors we use when we talk about data could be key to inspiring better outcomes for the whole of society.

We also recommend looking at the full report on the Defend Digital Me website here. 

The following is based on an excerpt of the upcoming book “Self-imposed Algorithmic Thoughtlessness and the Automation of Crime Control”, Nomos/Hart 2022 by Lucia Sommerer

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Language is never innocent: words possess a secondary memory, which in the midst of new meanings mysteriously persists.

Roland Barthes¹

The societal, as well as the scholarly discussion about new technologies, is often characterized by the use of metaphors and analogies. When it comes to the legal classification of new technologies, Crootof even speaks of a ‘battle of analogies’². Metaphors and analogies offer islands of familiarity when legally navigating through the floods of complex technological evolution. Metaphors often begin where the intuitive understanding of new technologies ends.³ The less familiar we feel with a technology, the greater our need for visual language as a set of epistemic crutches. The words that we choose to describe our world, however, have a direct influence on how we perceive the world.⁴ Wittgenstein even argues that they represent the boundaries of our world.⁵ Metaphors and analogies are never neutral or ‘innocent’, as Barthes puts it, but come with ‘baggage’⁶, i.e. metaphors in the digital realm are loaded with the assumptions of the analogue world from which the imagery is borrowed.⁷ Consider the following question about one of the most widespread metaphors on the subject of algorithms, the black box:

What do you see before your inner eye, when you hear the term ‘black box’?

Some people may think of a monolithic, robust, opaque, dark and square figure.

What few people will see is humans.

This demonstrates both the strengths and the weaknesses of the black box image and thus its Janus-headedness. In the discussion about algorithms, the black box narrative was originally intended as a ‘wake-up call’⁸ to direct our attention – through memorable visual language – towards certain risks of algorithmic automation; namely towards the risks of a loss of (human) control and understandability. The black box terminology successfully fulfils this task.

But it also threatens to obscure our view of the people behind algorithmic systems and their value judgements. The black box image conceals an opportunity to control the human decisions behind an algorithmic system and falsely suggests that algorithms are independent of human prejudices. By drawing attention to one problem area of the use of algorithms (non-transparency), the black box narrative threatens to distract from others (controllability, hidden human value judgements, lack of neutrality). The term black box hides the fact that algorithms are complex socio-technical systems⁹ that are based on a multitude of different human decisions¹⁰. Further, by presenting algorithmic technology as a monolithic, unchangeable and incomprehensible black box, connotations such as ‘magical’ and ‘oracular’ often arise.¹¹ Instead of provoking criticism, such terms often lead to awe and ultimately surrender to the opacity of the black box. Our options for dealing with algorithms are reduced to ‘use vs. do not use’. Opportunities that would allow for nuances in the human design process of the black box go unnoticed. The inner processes of the black box as a system are sealed off from humans and attributed an inevitability that strongly resembles the inevitability of the forces of nature; forces that can be ‘tamed’ but never systematically controlled.¹² The black box narrative also ascribes such problematic inevitability to negative side effects such as the discriminatory effects of an algorithm. This view diverts attention away from the very human-made sources of algorithmic discriminatory behaviour (e.g. selection of training data). The black box narrative in its most widespread form – namely as an unreflected catchphrase – paradoxically achieves the opposite of what it is intended to do; namely, to protect us from a loss of control over algorithms.

In reality it is, however, possible to disclose a number of human value judgements that stand behind even supposed black box algorithm, for example, through logging requirements in the design phase or output testing.

The challenge posed by the regulation of algorithms, therefore, is more appropriately described as an ‘algorithmic veil’ than a black box; an ‘algorithmic veil’ that is placed over human decisions and values. One advantage of the metaphor of the veil is that it almost inherently invites us to lift it. A black box, on the other hand, does not contain such a prompt. Quite the opposite: a black box indicates that an attempt to gain any insight whatsoever is unlikely to succeed. The metaphors we use in the discussion about algorithms, therefore, can directly influence what we think is possible in terms of algorithm regulation. By conjuring up the image of the flowing fabric of an algorithmic veil, which only has to be lifted, instead of a massive black box, which has to be broken open, my intention is not to minimize the challenges of algorithm regulation. Rather, the veil should be understood as an invitation to society, programmers and scholars: instead of talking about what algorithms ‘do’ (as if they were independent actors), we should talk about what the human programmers, statisticians, and data scientists behind the algorithm do. Only when this perspective is adopted can algorithms be more than just ‘tamed’, i.e., systematically controlled by regulation.

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1 Roland, Writing Degree Zero, New York 1968, 16.
2 Thomson-DeVeaux FiveThirtyEight v. 29.5.2018, https://perma.cc/YG65-JAXA.
3 So-called cognitive metaphor, cf. Drewer, Die kognitive Metapher als Werkzeug des Denkens. Zur Rolle der Analogie bei der Gewinnung und Vermittlung wissenschaftlicher Erkenntnisse, Tübingen 2003.
4 Lakoff/Johnson, Metaphors We Live By, Chicago 2003; Jäkel, Wie Metaphern Wissen schaffen: die kognitive Metapherntheorie und ihre Anwendung in Modell-Analysen der Diskursbereiche Geistestätigkeit, Wirtschaft, Wissenschaft und Religion, Hamburg 2003.
5 Wittgenstein, Tractatus Logico-Philosophicus – Logisch-Philosophische Abhandlung, Berlin 1963, Satz 5.6.
6 Lakoff/Wehling, „Auf leisen Sohlen ins Gehirn.“ Politische Sprache und ihre heimliche Macht, 4. Aufl., Heidelberg 2016, 1 ff. speak of the so-called ‘Issue Defining Frame’.
7 See for example how metaphors differently relate to the data we unconsciously leave behind on the Internet: data as the ‘new oil’ (Mayer-Schönberger/Cukier, Big Data – A Revolution that will transform how we live, work and think, New York 2013, 20), ‘data waste’ (Harford, Significance 2014, 14 (15)) or ‘data extortion’ (Singer/Maheshwari The New York Times v. 25.4.2017, https://perma.cc/9VF8-J7F7). A metaphor’s starting point has great significance for the outcome of a discussion, as Behavioral Economics Research under the heading of ‘Anchoring’ has shown, see Kahneman, Thinking, Fast and Slow, London 2011, 119 ff.
8 In this sense, Pasquale, The Black Box Society – The Secret Algorithms That Control Money and Information, Cambridge et al. 2015.
9 Cf. Simon, in: Floridi (Hrsg.), The Onlife Manifesto – Being Human in a Hyperconnected Era, Heidelberg et al. 2015, 145 ff., 146; for the corresponding work of the Science & Technology Studies see Simon, Knowing Together: a Social Epistemology for Socio-Technical Epistemic Systems, Diss. Univ. Wien, 2010, 61 ff. m.w.N..
10 See Lehr/Ohm, UCDL Rev. 2017, 653 (668) (‘Out of the ether apparently springs a fully formed “algorithm”’) .
11 Elish/boyd, Communication Monographs 2017, 1 (6 ff.);Garzcarek/Steuer, Approaching Ethical Guidelines for Data Scientists, arXiv 2019, https://perma.cc/RZ5S-P24W (‘algorithms act very similar to ancient oracles’); science fiction framing and a reference to the book/film Minority Report, in which human oracles predict murders with the help of technology, are also frequently found; see Brühl/Steinke Süddeutsche Zeitung v. 4.3.2019, https://perma.cc/6J55-VGCX; Stroud Verge v. 19.2.2014, http://perma.cc/T678-AA68.
12 Similarly, as early as 20 years ago, Nissenbaum, Science and Engineering Ethics 1996, 25 (34).

Title image by Alexa Steinbrück