AI and Politics

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Source: AI, machine learning and marketing: a brave new world,  http://www.thedrum.com/profile/news/265498/ai-machine-learning-and-marketing-brave-new-wor

At the end of last week I attended a presentation about Economy 4.0 and Artificial Intelligence (AI). Idea is that all the expected improvements in services and products, the Internet of Things (IoT) depends on the intelligence of the communication of out machines.

Most of the simple repetitive tasks are already computerized; replacing workers either by smart software, or by robots. The next step, which is taken rapidly, are learning algorithms. Learning by algorithms can be described as repeating processes, games or tasks, evaluating the results and adapting the reaction to the initial input. This repetition had to be accompanied by human input, to distinguish right and wrong outcomes. Yet, the last generation algorithms can even learn from other algorithms. By computerizing simple task, so is the expectation, labour productivity will increase by 40%.

The learning process requires lots of data on inputs, procedures and desired outcomes. Therefore, Big Data is of importance and is one of the functions of the IoT to generate and analyze data. The idea is to generate so much data that every task becomes simple and repetitive. In the (nearby) future, all task will become divided in simple repetitive tasks. The presenters sketched a future in which people can enjoy a 24/7 economy where interactions are real-time, but with a robot, who can help you based on your emotional pattern, earlier request, request of similar identities and communication with other AI’s in both your and its own network.

In my view, if something is possible, in time it will be realized. However, this is true for positive and negative developments. So four remarks with respect to the Economy 4.0-future:

  • The underlying models of the algorithms determine the validity of the outcomes of the analyzes and actions. For example, criminal profiling depends on the correctness of the relationship between the chosen profiles and the probability of criminal behaviour. Another example is the fact that advertisements on Facebook and websites are determined by past behaviour. boyd and Crawford (2012) cite Bollier: “As a large mass of raw information, Big Data is not self-explanatory”.
    Often the remark is heard that after the buy of a pair of sneakers, or in my case a casserole, the algorithms for some time will offer us the same sneakers and casseroles. Learning, then, takes the form of changing the advertisements after I placed a new buy on the internet. A less sympathetic feature is the fact that if you have looked for airline tickets, the price increases with each visit to the website. Some people even reserve one computer for looking and another one for buying! The Financial Times recently asked attention for “The algorithms that seduce our children“: “The tech industry is under scrutiny for how its algorithms manipulate adults but little attention has been paid to how algorithms seduce children, who are far more susceptible than their parents. Children often lack the self-control or even the means to change the channel“.

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    Illustration reproduced with permission of Pâté pateontoast.co.uk

  • An implicit assumption in the Big Data approach is that every task can be divided in simple tasks which can be described by an algorithm, simplifying complex activities in sets of computerized tasks. Activities which are to complex today can be solved by gathering more data. Yet, as someone remarked: finding a needle in a haystack is not made simpler by adding more and more hay to the stack.

boyd and Crawford (2012) compare the influence of Big Data with the assembly line of Ford, stating: “[..] the specialized tools of Big Data also have their own inbuilt limitations and restrictions. For example, Twitter and Facebook are examples of Big Data sources that offer very poor archiving and search functions. Consequently, researchers are much more likely to focus on something in the present or immediate past – tracking reactions to an election, TV finale, or natural disaster – because of the sheer difficulty or impossibility of accessing older data“. Kate Metzler argues that academics in social sciences either lack the access to Big Data or the capabilities for Big Data analyses. According to her, the digital age will result in a division between in company researchers and academic researchers, resulting in a majority of research aimed at selling more, and a minority of research trying to understand social processes and outcomes.

  • The gathering of data also raises issues about privacy and ownership of the data. If my behaviour is recorded by some home device, which learns to make expectations on my personal life: raising the temperature after 5, ordering pizza if I’m not home at 7; it is uncomfortable to know that this data is shared with some anonymous IT-workers in Silicon Valley. Especially when firms, but also government agencies, will use this data to forecast my behaviour and use this knowledge to influence my decisions.
    To quote boyd and Crawford (2012): “Just because it is accessible does not make it ethical”. Arguments for such actions are often found in “it is convenient for you….”, “it is only to help….”, of “it is for your/the national safety……”. Next to privacy and unwanted influences, data on your behaviour, on and of the web, is worth money. The firms pay the data collecting firms money for the data you have given them. An old internet proverb states that if you don’t pay for the product, you become the product; often followed by the remark that you agreed with the user agreements. Yet, without agreeing with a long list of conditions, you can’t use Facebook, web browsers or other ICT-applications, used for modern communication. So if I want to keep communicating with my family in our WhatsApp group, I cannot state that I agree with conditions 1 – 10, but not with 11 -121. Yet, I still think that there should be some discussion on the ownership of data on myself, or at least have a say in the way it is used and by whom.
  • The emergency of Economy 4.0 will cause serious disturbances, even if the algorithms are right, the needle is found and agreement is arrived. As the Austrian economists as Mises and Hayek already showed around the start of the last century, changes in the real economy take time. When workers become obsolete, this will result in unemployment, losses in income and human capital, with real effects. In that sense, Economy 4.0 can increase inequality and social instability.

Economy 4.0 can influence our lives in positive and negative ways. It is expected to increase the ease of use of several appliances, increase quality of life through self-learning and communicating between different machines and applications, mechanization of dirty and tedious work. On the other side, it gives the opportunity for firms, governments and others to influence our behavior in an undesirable way, can give rise to wrong or socially unacceptable decisions, for example by supporting biases etcetera.

These (potential) developments ask for a Public Management 4.0; not only in the sense that government agencies apply the new technologies, but in a way that supports the positive sides of AI and IoT, and suppresses the negative sides.

To do so, governments have to leading in the knowledge of the underlying technologies, but also make the usage of these technologies transparent. Why is a loan denied, what kind of commercials are financed by whom? And in extreme cases, governments should be able to pass laws, forbidding some kinds of usage of the Economy 4.0 technologies.

Next to direct interferences in the negative sides of the technological possibilities, is the obligation to educate the new generations in the do’s and don’ts. So they (we?) know when we are manipulated, know that ‘fake news’ exists, but also see that it requires some effort to make a distinction between good and bad.

Much is possible, much will be positive, but to enjoy the advantages of the AI/IoT developments, society has to defend itself against the negative aspects.

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Openness, lessons from innovation for education

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In two seminal papers, Dahlander and Gann (2010) and Huizingh (2011) try to define openness as used in open innovation.  Here, I try to use these definitions of openness in describing openness in education, drawing some lessons for both sectors.

Definitions on openness in innovation

Although Huizingh (2011) bases its definitions on Dahlander and Gann (2010), it is easier to start with his distinction between the innovation process and the innovation outcome. Openness in terms of the process is determined by the amount of knowledge which is obtained externally, or developed internally. The openness of the outcome is determined by the fact if the resulting process or product is proprietary or made freely available for external partners.

Innovation process: Innovation outcome:  
  Closed Open
Closed Closed Innovation: proprietary innovation developed inhouse. Public Innovation: the outcome is available for others, whereas the innovation was developed inhouse.
Open Private Open Innovation: a proprietary innovation, developed with the input of external partners. Open Source Innovation: both the development as the result of the innovation are open.

Source: Huizingh (2011, p. 3)

Closed innovation is the traditional way innovations were developed. The aim of public innovation often is the development of a standard. For example, by making the PC the standard in computing during the 80’s, IBM could control part of the market for personal computers.

Another way to divide open innovation is to make a distinction between inbound and outbound innovation. In the definition of Huizingh (2011, p. 4): Inbound open innovation refers to internal use of external knowledge, while outbound open innovation refers to external exploitation of internal knowledge. Dahlander and Gann (2010) combined these types with the question whether there is money involved or not.

  Inbound Innovation Outbound Innovation
Pecuniary Acquiring Selling
Non-pecuniary Sourcing Revealing

Source: Dahlander and Gann (2010, p. 702)

Revealing seems to be used to attract collaboration, especially in situations without strong IPR regimes. It also resembles Public Innovation of Huizing (2011), in aiming to set a market standard. Sourcing refers to the absorption of external available knowledge to create new products and services. The literature suggests an inverted U-shaped curve: searching for external knowledge will be profitable up to a certain level, after which the “over-search” will become more costly than profitable.

There seems to be a paradox in openness: as Huizingh (2011) states, companies perform more inbound than outbound activities (which recently confirmed by studies of the open innovation network, http://oi-net.eu/), yet inbound activities of one organization should generate reciprocal outbound effects from other organizations?

Openness in education

As we noted elsewhere (De Langen, 2013), there are a lot of definitions of openness in education. Openness in the sense of free to obtain (MOOCs), free to use (OER) or the absence of entry barriers (Open Universities).

If we define the process as a measure of openness of the process, leading to the product, we can distinguish between free to access, free to use or even collaboration in design and production. The outcome is the education, the course or the program. Traditional education is mostly distributed in a closed form: it is exclusively for students of the institution. Traditional education is often designed and developed by a single teacher, by an internal group of teachers (both examples of closed process) and in some cases with developers outside of the own institution (often subsidy-led) or the usage of open resources and MOOCs. The Open Outcome-side describes the production of open educational products and services. The closed production of open outcomes are typically of the production of MOOCs. A situation of open production and open outcomes is found in situations where communities both develop and use educational resources. For example in the case of knowledge bases and portals, developed and exploited by communities of fellow teachers; two examples are MERLOT and FEmTechNet.

Educational process: Educational outcome:  
  Closed Open
Closed Closed Education: traditional education with an one-to-one relationship between students and teachers. Free to use: the outcome (courses, programs) are open to use, but the teaching/developing process is closed. We can distinguish different regimes:

a.       Traditional education without fees, as in large parts of Europe is practice; Open Universities

b.      MOOCs, where the product is free, but the process of developing the course is proprietary.

c.       Certain forms of Open Access, in the sense that the production process belongs to the researchers (holding the copyrights, sometimes having to pay a fee), whereas the published research results are free for the public.

Open Use of free: the use of free (open) resources to develop educational resources for traditional institutions; for example Lumen Learning offers to teach educators to use OER to develop courses and programs for usage within traditional institutions. Open Education: Open educational resources, DOCCs, communities of practice and alike.

If we look into the role of money in (open) education, than is the pecuniary side of the inbound knowledge acquisition the fact that most teachers use standard textbooks, produced and sold in a for-profit-business model by publishers. Of course, in traditional education teaching is one of the courses of income, however there are more opportunities. For example,

  Inbound education Outbound education
Pecuniary Acquiring textbooks and materials. Selling knowledge, texts and competences.
Non-pecuniary Sourcing: collaborating to acquire knowledge and resources. Revealing: collaborating to supply knowledge, competences and resources.

Another model

Another way to categorize education is based on Yunus et al. (2010). In their view, organizations optimize either financial profit or social value. On the other dimension, they distinguish the way organizations are financed: either they have to earn back the invested capital, or they don’t. In this last case, another organization will supply the funds necessary for the long term survival of the organization. Traditional HEI’s were placed either in the Not-for-profit category for public education; or in the For-profit-category for private educational firms. Interesting are those organizations (websites, portals, knowledge bases ect.) which resulted in the past years, as result of inter-organizational collaborations, subsidies or individual initiatives.

Financial Profit Maximization
No recovery of Not sustainable in the long term For profit organizations Repayment of
Invested capital

(depending on external funds)

(Traditional) Not-for-profit organizations Social businesses Invested capital

(self-sustainable)

Social Profit Maximization

Next to the educational knowledge and competences, their survival will depend on the capability to generate funds to reimburse the capital used in the production and exploitation of open education.

Literature

Dahlander, L., & Gann, D. M. (2010). How open is innovation?. Research policy, 39(6), 699-709.

De Langen, F. H. T. (2013). Strategies for sustainable business models for open educational resources. The International Review of Research in Open and Distributed Learning, 14(2), 53-66.

Huizingh, E. K. (2011). Open innovation: State of the art and future perspectives. Technovation, 31(1), 2-9.

Open Innovation in European industries (2015), study for the European Commission, http://oi-net.eu/.

Yunus, M., Moingeon, B., & Lehman-Ortega, L. (2010). Building social business models: lessons from the Grameen experience. Long Range Planning, 43, 308-325.