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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Open Education, efficiency, collaboration and management

Ben Janssen, a former colleague who started his own consultancy on change and (open) education, and myself discussed several experiences we have with open educational resources and alike.

In this context he made an interesting remark, as Ben often does. In his view, Open Education is not only a public good, but can also be used as a communication channel. As he stated:

“in my work as an external consultant I often find that departments within an organization are working on the same projects, starting the same pilots and the same programs”.

Even over organizations he sees the same phenomena: organizations who work on the same projects without knowing what happens a stone throw away.

By opening up, organizations make this kind of information available for potential partners. In the same sense as commercial organizations try to innovate through openness and collaboration, offering knowledge and materials invites others to collaborate and improve on the original resources. Yet, as in open innovation, organizations should do so from their own strength: giving away your core competences is bad business, even when you’re not in business.

So opening up education in this way offers the possibility to share programs, or as Wiley (2014) argues, developing competence profiles and the accompanying programs, techniques and assessments.

By offering open competence programs, more institutions can develop new experiments based on these programs, improve and change the programs, which will feed back in the education of the original developers. This line of thought opens an interesting question: What is the core competence, resource, program or technique of a specific educational institution? What is the distinctive characteristic which distinguishes one HEI from another?

There is also a dangerous side to these possibilities. We know that both governments as boards of HEI’s have seen Open Educational Resources, MOOCs and other open educational materials as a way to reduce teaching costs. It causes a paradox: using OER can decrease teaching costs, producing OER will increase costs of the organization. The sensible management decision will be to demand that people use OER in their teachings, forbidding them to produce free materials for others.

In the ’70s of the former century, this was called the innovation paradox and used to explain why the national level of innovation will be below its potential level. Cure for this paradox is a good system of IP’s, so the inventing firm can also secure the income of the innovation.

This remedy is of course impossible in a system which is built on openness. Protecting OER with IP-rights would remove the essence of sharing.

So accepting that:

  1. the production of OER is costly in the sense of hours spend;;
  2. there is none or little incentive for an individual organization or department to offer free materials and programs in isolation;

    and assuming:

  3. that open education will increase efficiency (lower overall costs of education) and,
  4. increase effectivity (best materials will be used, freeing resources for additional teaching and teaching materials),

there has to be an external force redistributing income over the producers and users of OER.

This could be an internal authority, for example the board of the HEI, which can stimulate the development and use of the same supporting courses (for example, the development of an open course on statistics for non-mathematical studies; developed by the intern mathematical department). The development costs can be earned back as usage outside the own department is rewarded by additional funding by the board.

On a national level, government agencies could reward the supply and use of open courses by subsidizing the suppliers, without punishing the users by cutting back there teaching funds (which in itself is not a challenge for the HEI’s, but more for the politicians to resist the temptation to save money on the education budget).

Yet, by reading each other signals in the sense that organizations will open-up non-core courses; collaboration in these fields can make education more efficient and effective. As Janssen said, collaboration needs communication.

Specialised teachers can provide free courses for non-specialist students, freeing sources to develop better and more courses, flipping the class room and freeing students from uninteresting class room lectures.

A win-win situation could be possible if we would agree to communicate our “weaknesses”, offering our “strengths” to our colleagues.

Literature

Wiley, D., (2014), The Open Education Infrastructure, and Why We Must Build It, July 15, 2014, http://opencontent.org/blog/archives/3410, accessed December 18, 2014

Why business models in education matter

Again, and again teachers rightfully state that there is no reason why they should take into account the business model of their course. However, on an institutional scale a business model describes the way an organization defines itself. It is not only an earning model: describing the earnings versus the costs, determining the net income of the organization.
The business model also contains collaborations, essential activities and processes and core competencies. By defining the organization in this way shows clearly what the organization sees as its raison d’être, its competitive position in regard to other institutions and organizations.

The individual teacher teaching a class in Latin may not be interested in the fact that her investment in offering an interesting program is only attended by small groups of students. At an administration level of the university, however, the imbalance between the costs of providing the class and the income generated through direct student fees and governmental subsidies. This imbalance and the financial long term effect of it can be fed back to the individual teacher, providing an incentive to change the way of teaching. In this case, sharing with other teachers over universities could be an answer to the investment costs (eq. through virtual classes, by video appearances). Yet, another measurement taken could be to when the institution sees this course as essential for its identity and does not want to share it with others. In that case, funds will be made available for teaching regardless financial shortages. An intermediary way could be to support the teacher to develop materials which could reduce the actual f2f time by offering online materials.

All these actions (innovative or conservative) require an understanding of the business model of the institution:
– why would we invest in innovation in our present education: this requires a view on the strategy of the institution and on the values of the stakeholders;
– will we cooperate and who are our partners, con-colleagues or co-creators?

A good business model can help in three ways: (1) analyses the present activities: are we still creating value for the present students and other stakeholders? (2) Given our strategic targets, are our activities still in line with these targets? (3) Given the wish for change, what does that mean for our activities, competences and partnerships?

Especially in education were the situation is complex as the stakeholder who provides the finances is not the same as the one who receives the education. Is education the service provided (towards the individual student) or is it the student with a degree who is delivered towards society? Another complicating factor is the interaction between the different business models for research, teaching, valorization and other activities as employed at HE institutions.

Again, a business model without a clear strategy or vision on the organization is like having a roadmap without a destination. If we know what we want to do for who; the next thing is to determine how and when. Describing the different business models could give an internal consistency on each major activity, but also show interdependencies and conflicts between the different business models.

Inside in the business model of an organization will stimulate innovation in a broader sense than only technology or demand driven. By aligning the demands of the stakeholders with the possibilities of the organization, possible improvements can be identified, raising the value for stakeholders, whether students, teachers, governments or society at large.

This should not mean that governments should control either content or methods of teaching, that administrations should make profits the main driver of education, but it isn’t a carte blanche for teachers to use unlimited resources in their teachings.
The acceptance of reciprocal interests and interdependencies should lead to an innovative mixture of alternative financing of new interesting teaching methods.

Education Changemakers: Business Models Matter http://marscommons.marsdd.com/business-models-matter/