Archive by Author | nenadveselinovic

Robot Ethics, Emphatic AI and The Importance of Multiple Perspectives

In her keynote speech in Interaction16 conference Mrs. Kate Darling from MIT Media Lab opened up a topic of social robotics. She started by reviewing common concerns that humans currently have – that robots will take over the world and eliminate human race. This concern was quickly dismissed by showing the state-of-the-art robots autonomously playing football. They cutely and clumsily stumble and fall even before reaching the ball to kick. That much about taking over the world, for now.


Humans empathize easily with cute, physical robots

Social robotics has its background in fundamental human property of being able to empathize with nearly anything. Physical objects are easier to empathize with than virtual ones. If the extra effort is made to make robot move, be cute and give it big eyes, there is no way human can resist it. This is why people behave like robots would be alive, even though they know this is not true. This is also what makes it possible to use social robotics to provide obviously value adding services like cheering up sick children, helping autistic children, being company to elderly people and easing consequences of demention.


Empathizing with a robot.

However, Mrs. Darling raised several ethical concerns and inconsistencies with the above use cases. Is it ethical to leave elderly in company of social robots and can that ever replace human to human interaction? And how it is different from leaving them in a company of a living animal? Will the people feel uncomfortable sharing personal data or undressing in front of a robot and will they feel like not having privacy anymore? And how is that different from satellites that can already take high definition pictures of nearly anything on earth? Is it really just the matter of design, i.e. making it hidden from people? Is it ethical to let people empathize with a robot and thereby leave room for emotional manipulation of human behavior?

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Participation, Narrow AI and Machines That Design

The opening keynote speech of Mr. Marko Ahtisaari in Interaction16 conference started with a piece of music. And not just about any piece but recently recomposed Vivaldi’s Spring part of the Four Seasons. How appropriate for any beginning, and especially at the time spring still needs some encouragement to show its face to the streets of Helsinki. It is not by chance that music is chosen to open the event. Mr. Ahtisaari is the CEO and cofounder of the Sync project whose aim is to untap the potential of music as a precision medicine. Yes, you read it right! Music as a highly personalized alternative to pills! A research has shown that music has powerful effect on our brain and can even unlock the people out of the motoric inability caused by Parkinson’s disease. The project is building a platform which will map music characteristics on their biometric effects. The machine learning will then be used on this set to create and deliver personalized music therapy for sleep, pain and movement disorders, to name a few.


The Sync project

In addition to the above wonderful value proposition, Mr. Ahtisaari touched on several other topics and issued several calls for action. What resonated the most is call to shift our attention from User Centricity and Objects to Participation and Systems. We need to finally realize that we coexist on this world and any action we (do not) take impacts environment around us. As Mr. Ahtisaari pointed out, we are heading to the age of Entanglement, where we highly interact with each other. Being only user-centric and optimizing experiences for the (possibly selfish) needs and wants of a single persona might not be enough anymore. This only reinforces the importance of being actively present, connected and constantly learning. In other words, being now-ists and using effectual thinking at least as much as being futurists. Fellow service dominant logic enthusiasts, doesn’t this sound familiar?


Call for action: Participation over user-centricity

Learning to live and co-create with intelligent machines is another must. As Mr. Ahtisaari puts it, we are destined to live in the world where machines will have power to surpass us and shape the world for themselves in a previously unimagined ways. This will mark the end of the era of Enlightenment.

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IoT service kit in action

Few days ago I had a great privilege to participate in the workshop sponsored by Futurice, the powerhouse of digital services. The workshop named “Creating tomorrow’s services, together” was organized as part of IxDA’s Interaction Week and Interaction16 conference, which is taking place 1.-4.March in Helsinki.

It takes a framework and toolkit …

The workshop hosts Ricardo Brito, Paul Houghton and Jane Vita kindly gave us an introduction of service design process in Futurice. The process is based around three fundamental pillars, namely Business, Technology and Service Design. The key ingredient of their success is Lean Service Creation, which has its roots in lean startup, lean agile development and service design. The process enables teams of T-shaped individuals to look at the service in a holistic way and thereby maximize chances of service success. At the same time it makes it possible to “succeed faster by failing early” and pivoting if needed.


The three pilars of a successful IoT service design

While it only sounds logical, our hosts assured us that getting people of different background to talk to each other in a meaningful way can be a nightmare. Futurice’s IoT service toolkit is an attempt to attack this problem. Its purpose is threefold. First, it establishes a common language for people with different backgrounds. Second, it guides participants’ thinking and gets them used to the fact that nearly everything around us might be connected to the Internet. Last but not least, it enables team to work together and co-create.

… And getting hands dirty …

We were given a ready set of trend cards, well known tool of Futures Thinkers. Each selected the ones that resonated strongest with one’s own interests and the teams emerged around those. Our team focused on the smart cities and the future of mobility within them. We started divergence phase by ideating the most pressing mobility problems of the smart cities. After a while we converged and decided to focus on the problem of people with limited mobility. We identified two personas, “native digital” and the “old school”.


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IoT service kit in action

IoT service kit came into play to help us diverge again. With it’s ready set of cards and 3D printed models it quickly enriched our conventional thinking with urban connected furniture, wi-fi stations, beacons, drones, self-driving cars, assistive robots, kinetic suits, horizontal elevators and all kinds of connected sensors. After quick post-it brainstorming and clustering we converged again and decided on the core enablers of our service offering, i.e. autonomous cars and assistive robots. We moved on to create storyboards and journey maps for the key use cases. We did another quick round of divergence around the business model and relatively quickly agreed on the mixture of the private-public funding and related value network.

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The Network As a (Service) Design Material

The afternoon of Interaction16 workshop day offered the event named “The network as a design material: distributed systems UX for the internet of things”, which I had a pleasure of attending. According to the workshop hosts, Claire Rowland and Helen Le Voi, it is predicted that 33 billion devices will be connected to the Internet by 2020, three times as many as in 2014. The range of services that will be built on top of this enormous mass of devices will range from smart homes, over wellbeing assistants to emergency services and beyond. Some of these will be extremely critical and will be saving lives. Others will be less critical and simply making lives more convenient. One thing is common for all of them: they will require network connectivity to bring all the devices together into a seamless experience.

Can we assume that the network connectivity will always work as expected? Certainly not. Network connections in most cases involve a heterogeneous mix of complex protocols and technologies.


A roleplay illustrating technological complexity of communicating over network

Outages, glitches and delays are part of network’s everyday. When they happen the digital service user might be experiencing unresponsive connected physical objects that were otherwise responding immediately in an unconnected world. This might be difficult for a user to accept, once it was taken for granted. Take an example of a light bulb, which is turned on and off by a physical switch in an unconnected scenario. In IoT world, that same light bulb might become unresponsive to the switch implemented as a mobile application. Reliability and latency of the network connection and limited power of the connected devices will translate into unreliable user perception of the real environment being sensed and controlled through these devices. The dimensions that should be understood and explored in the IoT case range from most visible like are UI/visual design, interaction design, industrial design, over medium-visible interusability and conceptual modelling to least visible like service design, platform design, and productization.

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From designing with an impact to investing with an impact

Public sector austerity measures are very much a reality all over Europe. This means that many of the projects that have the potential to positively impact wider populations’ well-being are short of investment. Alternative models are needed, as pointed out in a recent article, where stronger cooperation between public and private investors is made possible. Impact investing is one possible way to untap this potential, and it is gaining significant ground all over the world.

Why now, why here?

In Finland, this model has emerged in form of Impact Accelerator Programmes, a joint effort of Sitra (Finnish Innovation Fund) and FiBAN (Finnish Business Angels Network).


A danger of looking for purely financial returns (Mompi 2016)

The aim of the programmes is to support companies and organizations that solve the well-being challenges of the Finnish society. The  first three-months programme started in October 2015. It aimed at improving the ability of participant companies to develop effective services, as well as sustainable and investment-ready business model. The first programme culminated in the first ever Impact Investment Pitch in Finland, held on 23.February in Startup Sauna, where participant companies had a chance to pitch their business ideas to the investors.

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Bringing big data back to the customers

The amount of digital trace we leave behind us every day is no doubt already large, growing and increasingly unstructured. In other words, it is big, as Big Data term proponents like to say. At the same time humans have a need to create information, knowledge, understanding and wisdom out of any data (Ackoff 1989, Rowley 2007, Bellinger et al 2015), big or small. Luckily, technology which enables us to do it at scale is there. So things seem to be all right and a lot of value seems to be generated on a daily basis. But is it really so? In search for fresh answers I attended the Aalto Digital breakfast on Big Data in the Industry where data experts from Yle, Smartly, M-Brain, Supercell and Tieto presented their uses of Big Data to create value for their customers.

It is everywhere, and the companies benefit from it

Based on Rossi et al (2015) big data has found its applications in nearly every field of business (see Figure 1) through digitalization of services. An example is gaming industry which remains the frontrunner with using the data to figure out who, when, where and how is playing their games. Another example are media and retail industry which are finding out which content the customers are likely to like and purchase next based on their earlier preferences. Further example is market and media intelligence which uses massive datasets to identify trends and assist and simplify decision making within companies.

Big data in business and society

Big data in business and society (Vakkuri 2015, Digi Breakfast on Big Data in the Industry)

It therefore appears that both businesses and end-user customers benefit and realize value from Big Data, big time. Business users mostly benefit by being able to more precisely target their offers to customers that are most likely to (dis)engage, or validate whether the existing offer has sufficient engagement. End customers eventually benefit from the offer that might best fit their own aspirations and needs. Most use cases are, however, still largely business driven. It is still not that often that end customers fully consciously give up their digital-trace in order to be offered tailored data products and services. The general feeling is still that of companies being in the driver position and end customers merely following them. This does not look like co-creating value with the customer but still very much trading goods for money. Humans’ behavior is considered to be mere sequence of numbers, and a lot of them. The general focus is on quantity of data since it gives sufficient statistical significance. The assumption is thereby that humans are statistically predictable creatures, which will behave the same in the future as they have behaved in the past. But is that really true? It is almost commonly accepted that most of the human decisions are based on emotions at least as much as on facts and numbers.

But how to really bring it back to the customer?

While being powerful, Big Data analytics technologies still seem to mostly generate information and at best knowledge, at least based on definitions of Ackof (1989). They seldom stretch to answer the question of “why (does the customer behave like she behaves)“, based on pure numerical data. The data which, when brought together, could eventually answer these questions is locked into corporate silos. As suggested by Vakkuri (2015) efforts similar to and Helsinki Region Infoshare could be extended nationwide to bridge this gap. Even if available, in order to answer the “why” question a lot depends on the domain knowledge and intuition of a data scientist. As summarized by Valtonen et al (2015), the huge amounts of data can be analyzed automatically to generate information and knowledge which gets outdated fast, but it is still human touch that is needed to make sense of it and turn it into longer lasting wisdom.

Some of the key skills to reach to level of information and knowledge mentioned by Rossi et al (2015) are statistics, scripting, software development, parallel computation platforms, presentation skills and last, but not least, domain knowledge. While these may be sufficient to communicate with the customer indirectly, i.e. through data, one has to remember that the gained insights are thereby bound to be “thin”. In order to collect “thick” data and get to the level of wisdom one requires ethnographic research methods as well (Madsbjerg & Rasmussen, M.B. 2014). This still seems to remain out of the big data scientist toolkit, without obvious reason.

The customers nowadays offer their digital existence to businesses, and pretty much for free. But is their story understood by the businesses? Are customers getting in return what they really value? We might be just a conversation away from finding out.


Rossi A., Ojala M., Kärkäs P., Valtonen K., Vakkuri M. Digi Breakfast on Big Data in the Industry,, Accessed on 14.Dec.2015

Ackoff, R. L. 1989. “From Data to Wisdom”, Journal of Applies Systems Analysis, Volume 16, 1989 p 3-9.

Rowley J. 2007. The wisdom hierarchy: representations of the DIKW hierarchy, Journal of Information Science, 2007, 33(2), p 163-180

Bellinger G, Castro D., Mills A. 2015. Data, Information, Knowledge and Wisdom,, Accessed on 14. Dec. 2015

Madsbjerg, C., Rasmussen, M.B. 2014, The Power Of “Thick” Data,, Accessed on 11.Dec.2015

How soft is business ecosystem creation?

Electric vehicles (EV) seem to be the only reasonable future replacement for what we call car today. Everyone seems to agree on that. Still, the amount of EVs on the streets is still quite close to peanuts. The reason for that is the lack of a supporting service ecosystem, i.e. the fact that the “tipping point” has not been reached yet. The Tekes project EV-ACTE has investigated the role of the soft strategies in creation of an ecosystem around EVs. The project researchers have recently reported results in a series of interesting presentations. The two concrete examples of soft strategies that the EV-ACTE project researchers have studied are affective influence and narratives.

Doing is better than talking

Affective influence is a process of actively influencing people’s affective reactions. One way this could be done is through embodied affective influence. E.g. instead of “doing a lot of political talk” (Vuori, Huy 2015) to the decision makers in the EV ecosystem, one puts them e.g. into a real driver’s seat and lets them test drive an EV themselves. What is said is left to the recipient of the information to interpret and a lot of things remain unclarified. This in turn leads to making decisions under uncertainty. As shown by Laureiro’s presentation (2015) this falls into a mode of exploration, i.e. a mode where decision maker needs to think long term, plan, be creative and exercise more dynamic behavior switching. This is proven to take a lot of human brain’s energy and is therefore not so easy to do (Laureiro, 2015). On the other hand, by experiencing e.g. driving an EV first hand, one can reduce the feeling of uncertainty and shift the decision making into the mode of exploitation. This is mode where one needs to deal with incremental changes and decisions, and engages different parts of the brain, the ones that deal with learning, memory, and persistence, which seems to consume less energy and is therefore easier to do.

If you have to talk – do it with passion

Another way to build affective influence is through using rich and vivid language that is close to the regular human being, as opposed to using “analytical information and getting the facts straight” (Vuori, Huy 2015). Narratives, as explained by Gustafsson and Rowell (2015) are “how we interpret the world”. Established car makers, for example, interpret the world of EVs as “the next step in vehicle production”. For Tesla, on the other hand, EVs are “innovation in batteries and energy that can be applied to any product” (Gustafsson, Rowell 2015). There seems to be a slight difference, right?


Venture narratives of established car makers vs. disruptor (Gustafsson & Rowel, 2015)

Co-produce, rather than blindly show the way

Yet another way to build affective influence is by generating perceptions of participation with different stakeholders, as opposite of being explicit of company’s own top-down dominance and others being followers. This also strongly resonates with service logic which has service co-production with the customer as one of the fundamental principles (Lusch & Vargo, 2014).

It is proven – it pays off to be soft!

Both of the researched company clusters had nearly equal starting points, and had promising businesses in the beginning. However the companies that adopted affective influence approaches managed to attract more partners and thereby to build a business ecosystem than the ones that did not “care” about affective influence. Some of the partners simply stopped working with the later and started working with the “emotional” and “passionate” company. This, although indirectly, also serves as a validation to some of the service logic principles. It pays off to be soft!



Influencing business ecosystem creation process by means of affective influence (Vuori&Huy 2015)


Timo Vuori, Quy Huy (2015). Affective Influence in Ecosystem Creation, in Soft Strategies in Business Ecosystem Creation: Narratives, Cognition and Emotions workshop, November

Robin Gustafsson (2015). Venture Narrative Strategies in Emerging Ecosystems, in Soft Strategies in Business Ecosystem Creation: Narratives, Cognition and Emotions workshop, November

Daniella Laureiro-Martinez (2015). Strategists’ brains: cognitive neuroscience and strategic management, in Soft Strategies in Business Ecosystem Creation: Narratives, Cognition and Emotions workshop, November

Lusch, Robert F.& Vargo, Stephen L. (2014). Service-Dominant Logic. Premises, Perspectives, Possibilities. Cambridge University Press, UK