In March 2018 I participated in a conversation on “(K)ein Traum von der digitalen Demokratie: Big Data als Chance oder Risiko?” as part of the Grenzgänger Wissenschaft talk series together with Professor Neuschwander of the HTWG Konstanz . We have talked about a number of questions and here are the answers I prepared:
What kind of data is there?
We currently distinguish four types of data:
- Administratively collected data: Surveys, or transactions in which we are open-eyed. Very detailed data that are requested and deliberately filled in. Highly structured.
- Open data: This means that some of this data is published, for example, by the public administration on open data platforms in aggregated and machine-readable format. Sometimes already helpful evaluated or visualized (unemployment figures)
- Data generated by users or citizens: Outside a formal transaction, on social media channels, other Internet transactions (shopping on Amazon), but also crowdsourcing actions, weblogs, clickstreams, search data. Can be private or public.
- Automatically generated data from human and physical sensors: measuring probes on buildings, buses, police body cameras, collected automatically without human intervention or consent. Advantage: very comprehensive data collection (all potholes through which a bus passes, complete data sets). Tracking of geographical locations, e.g. when you open your weather app.
Taken together, these are each in themselves “huge data sets” – some of a private / non-public nature, some of them public. All in all, this is unstructured Internet-generated data that is linked to structured data sets and can also contain geo-tags.
Where do we encounter Big Data in everyday life?
- Internet resources: Social media interactions, mobile phone apps, videos, photos that are shared, online search behavior, Google Nest in homes, etc.
- Structured data: Online shopping, mobile phone networks (who phones with whom), email exchanges
- Geo data: Automatic login to mobile phone poles, satellites
What do you think are the 3 biggest risks and what are the 3 biggest opportunities?
- Previously unimagined insights into the behaviour and preferences of citizens
- Quick data availability and decisions in real time (nowcasting)
- The potential of democratisation: Who will be heard?
- Distribution of Fake News
- Transparent Citizen: We do not know which algorithms are used or how they affect citizens.
- Political and economic decisions are influenced. Google Flu Trends
Where does the data actually come from? Do we make them ourselves?
Each of us is involved in the creation of Big Data every second. This happens through each of our online interactions (be it Google search, Amazon shopping, social media channel interactions, smart home, smart metering, fitness wristbands, smart phones and automatic log-in to phone poles, calls we receive, emails we send, streaming services such as Netflix or Amazon Video. Thus we leave behind so-called digital traces. Mostly passive and inactive, even without our knowledge and even if we do not actively use our devices. Do not participate to share our data. Even if you are not actively involved in a social media network, the exclusion says something.
What skills do citizens need in relation to Big Data?
There are two ways to protect yourself: First through personal actions and then also through systemic changes.
I. Rethink personal behavior:
a) Do not share everything immediately with the full power of emotions on social media! According to the latest, largest MIT study, it is clear that fake news is distributed faster and further than truths and the damage is already done. So first think about whether you want to be part of this machinery, like Pizza Gate during Hillary Clinton’s US presidential campaign.
b) Maybe read an article or the text format instead of a YouTube video or an exciting TV news show, so that you don’t let yourself be influenced by the pictures. Creating an emotional distance to the news.
c) But consider whether this can be true at all
d) Interpretation of who shares what and how and what their motives can be
e) Use offline networks such as clubs and village communities more than networking in the artificial online world.
II. Systemic changes necessary:
- Information and data literacy: articulate information needs, localise and retrieve digital data, information and content. Assessing the relevance of the source and its content. For storing, managing and organizing digital data, information and content.
- Communication and collaboration: interaction, communication and collaboration through digital technologies, taking into account cultural and generational diversity. Participation in society through public and private digital services and participatory citizenship. To manage his digital identity and reputation.
- Creation of digital content: Create and edit digital content Improve and integrate information and content into existing knowledge and understanding of the application of copyrights and licenses. Knowing how to give clear instructions to a computer system.
- Security: To protect devices, content, personal data and privacy in digital environments. Protect physical and mental health and be aware of digital technologies for social well-being and social inclusion. To be aware of the environmental impact of digital technologies and their use.
- Problem solving: Identify needs and problems and solve conceptual problems and problem situations in digital environments. Use of digital tools to innovate processes and products. To keep up with the digital evolution.
From the state’s point of view, too, the digitisation and use of big data in public administrations is important. What are examples of this?
Use of Big Data in public administration
- The first Big Data study in public administration was a combination of scientific data with Big Data from social media data (Twitter): The U.S. Geological Service was the first public administration to not only use scientific data on the magnitude of earthquakes, but also combine it with social media data to find out the impact of earthquakes on the affected citizens. By using these so-called humane sensors, it is possible to determine more quickly which decisions have to be made in the event of a natural disaster.
- To protect against terrorists: analyze large amounts of data, check for anomalies, investigate forensic evidence and help avoid terrorist attacks. This can be done with sensors on physical buildings and then synchronize the data in real time with other databases, analyze telephone traffic, bank connections, online shopping, etc..
- Use of VAT payments on online platforms are already actively analysed by tax offices in all OECD countries. All participants in economic transactions are provided with risk indicators, so that the tax office knows which transactions are risky (because they fall out of line) or which transactions are normal for a certain buyer/seller. Theoretically these analyses happen overnight and in the afternoon the tax investigator is already standing on the mat and tries to collect the allegedly evaded money.
- Use Big Data to predict the financial health of individual companies, cities or regions. In combination with various data sets, the public administration can diagnose whether a company can survive in the market and is on the verge of bankruptcy. This is important information for the public administration, as it affects jobs. This leads to increased social expenditure, such as unemployment benefit, or even the brain drain from a region, because the unemployed have to move to where jobs are available. It is therefore in the public administration’s own interest to use all available data sets to determine what is in store for them in the future.
- Government and jurisdiction should, however, be involved in the regulation of large social media companies, search engines and sales platforms:
- o hold companies accountable for allowing so-called fake news to be distributed. For example, Twitter and Facebook have only now, under pressure from hearings, looked for how Russia has placed purchased advertising in the news feeds. This means that both companies clearly benefited from this propaganda, but did nothing about it. Researchers find this propaganda very simple – so I wonder why the social media companies pretend that they have to search for it for a long time and
- “Weaponizing the Web”: YouTube as a place of radicalisation for young people and supporters of terrorist groups
- Establish ethical principles for the use of online media that social media companies must also adhere to. NetzDG (Network Enforcement Law) = Law to prevent hate speech and hate speech on the Internet. Decisions should not be made in Silicon Valley, however, but in our linguistic area, in which we understand the nuances of language (irony, sarcasm) and also the context and thus do not block wrongly criticised content.
What do you think would happen if you completely ban the collection of data?
Data collection, e.g. of Facebook data, is already prohibited in the EU, but it has become clear that the EU is not taking action and is really checking whether the data collection (the associated sale) is not actually taking place. One reason for this is that the servers are located in the USA.
From my point of view, the users are particularly in demand:
- Everyone should think about what they share on social media (parents who present their children publicly to get a few uhs and ahs)
- Do you only want to use social media personally, for example, or professionally? Add colleagues, then?
- Great discipline what you say online.
- We have become aware of some risks and threats to democracies in the context of Big Data. In the beginning, however, you also had three chances each named by Big Data for democracy. We know you look at the subject neutrally from a scientific perspective BUT what would you say if you had to make a flaming plea for Big Data?
Democratization effects of Big Data:
- There was a time when we all thought that Big Data had a democratizing effect. Everyone has pointed out that the Arab Spring would not have been possible if the demonstrators in Egypt had not gathered online worldwide to meet physically in Tahier Square. These are undreamt-of possibilities that Big Data offers: Information is distributed to many people and the power these demonstrations have can actually change the course of a government. Whether the results are always what you hoped for from a movement is another question. There are many examples, such as #BlackLivesMatter hashtag, NRA student protests in the US against the government and against the National Rifle Association -> no great improvement for the affected groups.
- On the other hand, hashtags like the #metoo campaign skipped the channel and led to accusations, job losses, publicity that could possibly lead to a change in behavior. In any case to an empowerment (strengthening) of the position of women,
The promise to gain unexpected insights into the actual behaviour and preferences of groups of people or even whole nations:
- Be it political voting behavior,
- Purchasing preferences,
- Effects of natural disasters on entire regions,
- But also reactions of citizens to changed laws and the effects on certain population groups that were not previously on the radar
Additional material is available here:
- Article “Big Data in Public Affairs” in Public Administration Review [free pdf file]
- Article “Big Data in Public Affairs Education” in Journal of Public Affairs Education (JPAE) [free pdf file]
- European Group of Public Administration keynote speech 2017: Big Data in Public Affairs (full Powerpoint presentation available on ResearchGate)