Multi level bar chart array

Digitisation of the workplace - Who will not be replaced by computers?

Challenges in risk communication

Communication of category-specific uncertain events to consumers.

Why is it relevant to communicate category-specific uncertain events to consumers?

The aim is for the consumer to be able to make better decisions. Therefore, they need to know precise facts and figures - the so-called evidence - in order to weigh options against each other and better assess risks. In addition, the presentation of the exact probabilities of occurrence helps the user not to underestimate or overestimate certain risks. This is particularly relevant when probabilities of occurence differ for various categories (e.g. age groups).

Why is it problematic to communicate category-specific uncertain events to consumers?

If you want to communicate risks, you face a number of challenges:

  • How can probabilities of occurrence be conveyed in general?
  • How can probabilities of multiple categories be compared?
  • How can risk communication provide solutions?
  • And how can the risks be presented in an appealing way so that consumers also enjoy engaging with them?
What is a suitable scientific approach?

The method of multi level bar chart arrays (grouped bar charts with multiple levels) can be used for risk problems, i.e. when reliable numbers are available about how often certain events occur. The method is supposed to give consumers an understanding of the probability of occurrence of such events. Multi level bar chart arrays offer the possibility to compare a certain risk for many different groups,and link them with the help of a hierarchy in such a way that higher-risk and lower-risk groups can be clearly distinguished by the user.

The interactive and dynamic visualisations are designed to help inform consumers and to be appealing at the same time.

Proof of effectiveness

First results of the RisikoAtlas communication studies with multi level bar chart arrays show that they are better received and more likely to be explored than tables with the same information. This information is extracted and remembered as well as with traditional tables.

Recommended literature on methodological basics
  • Ancker, J. S., Senathirajah, Y., Kukafka, R., & Starren, J. B. (2006). Design features of graphs in health risk communication: A systematic review. Journal of the American Medical Informatics Association, 13(6), 608–618.
  • Frey, C. B., & Osborne, M. A. (2017). The future of employment: How susceptible are jobs to computerisation? Technological Forecasting and Social Change, 114, 254–280.
  • Garcia-Retamero, R., & Galesic, M. (2010). Who proficts from visual aids: Overcoming challenges in people’s understanding of risks. Social Science & Medicine, 70(7), 1019–1025.
  • ONet (2018). ONET Resource Center. [Letzter Abruf, 31.12.2018]
  • Streit, M., & Gehlenborg, N. (2014). Bar charts and box plots. Nature Methods, 11(2), 117.
How can you implement the method?

Option 1: You can embed the given visualisation
It is possible to embed the visualisation from our website including the frame text via iframe. To do this, use the following html-code for your website: <iframe frameborder="0" height="650px" src="" width="1024px"></iframe>

Option 2: You can adapt the given visualisation
If you use your own data as a multiplier, your web developers can enter it into your own consumer fact box with experience-based learning.

We will provide the person responsible for your website with the documented code for download via github. You can then edit the material. The link to the repository is available on request. Contact details can be found here.

Option 3: You can apply the scientific principle independently
If you require assistance, please consult the final report on the RiskAtlas project from July 2020 or contact us. Contact details can be found here.

When using the instruments, please mention the funding agency, which is the German Federal Ministry of Justice and Consumer Protection, and the Harding Centre for Risk Literacy as the responsible developers.

Logos can be downladed here.

Links to other methods
Visualisation with frame text

Digitisation of the workplace - Who will not be replaced by computers?

Digitisation is changing the demand for certain jobs. You can observe how new jobs are created, but other jobs are no longer needed, since the tasks can be performed directly or indirectly by machines, computers or algorithms. Some professions are more likely than others to disappear as a result of digitisation. For example, an administrative employee who is supposed to write down the license plates of passing vehicles can be replaced to 100% by the capturing technology of a computer with camera and image recognition software.

The good news is that you can find alternatives for almost any job that match your skills and interests and are less likely to be automated. So which profession should you choose if you don't want to be replaced by a machine in the near future? It is worth taking a look at our chart before choosing a course of study, vocational training programme, or professional development.

In addition to bare figures, certain rules of thumb are also advisable for reducing the risk: consider occupations with a variety of work tasks or even alternating tasks; or occupations in which one works with people. Be cautious about occupations in which the main tasks consist of taking measurements, forwarding or applying fixed rules.

What does the chart show?

The chart illustrates the risk of certain professions being replaced by present-day machines and computers. In order to convey the numerical ratio of the different automation risks, these are represented with colours, and an overview of all occupations, arranged in categories, is given. Red means a high automation risk, green a low one. The search function allows occupations to be searched for directly. With a click on the colours you can also dive into the respective categories of occupations. In this way, the automation risks of the occupations in a category can be seen side by side, and similar occupations can be suggested by clicking on an individual occupation. Up to five career alternatives with a lower risk of being completed by present-day machines and computers are displayed. Click on the arrow in the upper left corner to return to the original view.

Sources and quality of the data

Where are the numbers coming from?

All figures represent exemplary estimates of the automation risk of a profession due to its requirements, given today's technological development, see Frey, C. B., & Osborne, M. A. (2017): The future of employment: how susceptible are jobs to computerisation?. Technological Forecasting and Social Change, 114, 254-280.

All occupational alternatives are proposed as similar using standardised requirement descriptions. These requirements come from the database of the National Center for O*NET Development, which is supported by the U.S. Department of Labor. Not all occupations from the American databases can be transferred to Germany; conversely, the German occupations are not fully refelcted in the American databases either. O*Net (2018). O*NET Resource Center: [Retrieved, 31.12.2018].

What is the quality of the data?

The proportionality of the various professions to each other can be roughly expected to be the same today, but the absolute figures are only approximate values. Pure modelling data without empirical observations are generally to be regarded as evidence of low quality. Only very limited conclusions can be drawn from a model about the actual automation and replacement of jobs carried out by humans. Thus, economic developments, cultural values and future technological developments cannot be taken into account in these figures. 

Last update: 14 Oktober 2019.

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