INNOVATIVE GOVERNANCE APPROACHES NEEDED: Some Thoughts on How to Master Our Future Using Big Data (also ‘Avatars’, ‘Digital Twins’) with a new Kind of Multi-Lateralism

There is little doubt that we are seeing ever more useful Big Data and AI applications that can make a contribution to a better world. It is much less obvious, however, whether the use of those technologies will benefit humanity and nature altogether, i.e. on average. Below, I just mention a few of the reasons that matter in this context:

• Digital technologies consume an exponentially increasing energy share and are expected to consume more than 20% of the energy produced by 2030, which seems to undermine the achievement of the sustainability goals: https://www.nature.com/articles/d41586-018-06610-y
• Using Big Data and AI tools does not require domain-specific scientific background knowledge, say, in physics, chemistry, biology, or economics. ML and AI learn patterns in data sets by themselves. There is, hence, a considerable risk that flaws in data analyses and AI applications (e.g. due to non-representative data samples or false positive and false negative classification errors or socially undesirable discrimination effects) will remain unnoticed for a long time, such that bad applications harming society or nature may spread.
• Using AI to make nations, companies, and/or people more powerful may further increase conflicts in the world.
• In a geopolitical situation where we witness a struggle of different political systems for global supremacy, the increasing use of Big Data and AI for cyber war and hybrid war is likely.
• In fact, there are more and more complaints about hacking critical infrastructures, and concerns about the vulnerability of our critical infrastructures are growing:https://www.tagesanzeiger.ch/die-digitalisierung-macht-uns-angreifbar-122235086291  
• There are many signs, indeed, that we are currently involved in a global information warfare using our personal data, and that it is closely related with various serious human rights violations: https://www.militairespectator.nl/thema/operaties/artikel/behavioural-change-core-warfighting  
• Since Edward Snowden, it is known that all major data sets are being aggregated by secret services such as the NSA. At the same time he has revealed some of the misuse that happens with this data, such as the JTRIG program: https://theintercept.com/2014/02/24/jtrig-manipulation/  
• The recent NSO scandal around the misuse of the Pegasus software against high-level politicians, journalists, and human right activists stresses that the use of digital technology is out of control: https://www.tagesanzeiger.ch/die-ueberwachungsindustrie-ist-ausser-kontrolle-245615078117  
• The Cambridge Analytica Scandal has, furthermore, unveiled the manipulation of democratic elections with personal data in many countries, where links to military and secret services circles have become obvious: https://www.opendemocracy.net/en/dark-money-investigations/cambridge-analytica-is-what-happens-when-you-privatise-military-propaganda/  
• A recent blog summarizes some further known dual uses of Big Data cyberinfrastructures: http://futurict.blogspot.com/2021/07/partial-list-of-possible-dual-uses-of.html
  

Considering all this, fundamental reforms are needed in the way personal data is being collected, managed, and used. Otherwise it is possible that the digital transformation may fail altogether. Here are a few proposals for consideration and implementation:

A high level of cybersecurity is clearly needed. In this regard, backdoors are counterproductive.

Distributed data storage and control would be a strongly recommended safety feature.

Accountability: Any processing of sensitive data must be documented for a sufficient period of time, if ever possible in a reproducible way, such that errors and dual uses can be detected and corrective measures taken.

Transparency: Data and algorithms used must be opened up[1] to a certain number of mutually independent supervisory instances such that a sufficient quality of data processing from a statistical, scientific and technical point of view can be ensured (e.g. in terms of avoiding unjustified discrimination or in terms of requiring meaningful data analysis [no spurious correlations; sufficiently small false positive and false negative error rates; reproducibility; results not sensitive to small changes in the dataset, algorithm, or hardware, etc; of course, one also needs to consider the other known issues of Big Data analytics])

Open intelligence approaches are to be favored over secret services, whenever possible.

The organization of data access may be hierarchically organized, in terms of time delays of data access and accessible data volumes. The hierarchy level should depend on demonstrated responsible use and benefits created for the world. On the highest level, there should probably be at least 7 competitive, mutually supervisory instances (probably tied to leading countries). Their best scientific institutions should also have privileged access for the sake of quality assurance. The highest levels (serving as “guardians” of the system) would have to satisfy particularly strict rules in terms of independence and avoidance of conflicts of interest. On the next levels, with a larger time delay and smaller data volumes processed, there would be a larger number of participating entities, probably big companies and other countries. Again with some additional time delay and smaller data volumes processed, there would be SMEs and other scientific institutions as well as civil society initiatives, etc. Entities providing more (useful) data to the system should be higher up in the access hierarchy, to be able to make reasonable returns on their investments. With suitable access rules, one can ensure that there would be sufficiently many competitors, such that monopolies and a dangerous concentration of power is avoided. It would further be reasonable to have clear rules according to which entities move up or down in the hierarchy, based on how useful their contributions to the state of the world and the future and humanity were.

Ethical standards and responsible innovation must be ensured. People dealing with sensitive data should probably have to swear something like a “Hippocratic Oath for data scientists” (I can make suggestions, if desired). Qualified ethics committees must be put in place to ensure the ethical use of data, in compliance with human rights.

A diversity of teams as well as an inter-, multi-, and transdisciplinary approach (properly considering knowledge from the natural and complexity sciences, from the cognitive and social sciences, from history, philosophy and so on) as well as participatory opportunities for civil society projects should be ensured, in accordance with the peace room concept: https://www.theglobalist.com/technology-big-data-artificial-intelligence-future-peace-rooms/

Note that – due to their tendency of being extremely focused – neither a utilitarian approach, as it is often common in the economy, nor a military approach are expected to be suited to successfully run advanced, complex, multi-faceted, multi-objective, thriving societies and civil-izations living in peace with each other.

Participatory governance: There should be representatives of those affected by decisions, such that they have a say regarding what is going to happen to them. For example, regarding health applications, it is required to have a representative, independent patient council, which can voice preferences and concerns.

Digital democracy is a concept that aims at fostering collective intelligence, better solutions and collective action at scale: http://futurict.blogspot.com/2020/06/digital-democracy-how-to-make-it-work.html

Informational self-determination is needed to ensure that people will still be able to control their own lives in the future: http://futurict.blogspot.com/2019/01/a-platform-for-informational-self.html

This does not mean though that everyone should do what they like, independently of how much harm is caused to others or to nature.

The socio-ecological finance system (‘finance 4.0’) is a multi-dimensional feedback, coordination and incentive system based on a participatory use of the Internet of Things that is combined with multiple new currencies. It offers to make sure that people will ‘feel’ the good or bad impacts of their behaviors on others and on nature, thereby helping them to take better decisions. The participatory system rewards people and companies accordingly. It is a democratic system that promotes the co-evolution of symbiotic interactions and the development of a sustainable circular and sharing economy: (Click on image for link to book)

Further reading:

M. Batty (2018) Digital Twins, Environment and Planning B 45, 817-820.

E. Arcaute et al. (2021) Future Cities: Why Digital Twins Need to Take Complexity Science on Board, preprint.

D. Helbing et al. (2021) Ethics of Smart Cities, preprint.




[1] at least with a delay – using time delayed self-decrypting encryption