The Self and the System

The Johari window [43] was developed in the 1950s to model how one perceives oneself and how others perceive us. It uses a two-by-two approach based on whether the information is known to oneself and whether the information is known to others. We use the same approach, but in terms of what is known to the user and what is known to the machine. We thus can outline four different types of subjective contextual information: mutual understanding, technology

and human misinterpretation, and unknown contextual cues unfamiliar to both the users and the system (see Table 2).

In our earlier examples, we explored the ideal situation in which there is a mutual understanding of context. In these cases, the system is able to correctly understand the context and adapt to those needs. Thus, this type of situation al-lows the user to trust the system. However, there are two other interesting cases in which there is knowledge imbalance. The first is technology misinterpretation, which occurs when the technology does not understand the context. The second is dehumanization, which occurs when the user does not understand the context.

The system understands context

No Yes

The user understands context

Yes mutual understanding technology misinterpretation

No dehumanization unknown

Table 2. The Johari window for technology.

Technology misinterpretation leads to situations where the human cannot trust the context awareness and automation. Existing literature has also shown that in social services naïve automation (anticipatory computing) leads to chal-lenges because it conflicts with the expected social rules in that situation [44], [45]. People engage in what is known as profile work, in which they go to great extent to carefully craft an image of themselves to their followers. However, so-cial media automation does not respect such detail work, and therefore the users must circumvent the automation techniques to maintain a profile through manual labor, ensuring that the system represents them “correctly.” On the other hand, an additional attractiveness of future subjective context applications may rely on the fact that there is a potential for individual empowerment, as discussed in previ-ous sections. Their appeal to a higher layer of understanding and not only to what users portray on their profiles—which may be more revealing of true feelings and states of mind, going beyond words and emoticon displays—is an added value.

As highlighted, this question is related to overall trust. As Fusco, Michael, and Michael (2010) discuss, trust both in the technical as well as the socio-technical system has a significant role in the uptake of these systems [46]. These research-ers tackle problems relevant to technical trust and user privacy, or how a user trusts a system not to share sensitive data with other users. In addition, Cheverst, Davies, Mitchell, Friday, and Efstratiou (2000) [and Antifakos, Kern, Schiele,

and Schawaninger (2005) discuss how systems build reliability for the user to trust the information provided and actions proposed by the system [47], [48]. As we have observed, the question of trust becomes critical when automating tasks;

users must trust that the automation works correctly and does not bring on nega-tive consequences. The fear of feeding a system with sensinega-tive data will never be an outdated discussion. However, the possibility for a system to understand human experience enough to account for its values and sentiments unavoidably involves data automation and invaluable value.

We find the most interesting cases to be those where the system is able to analyze—and thus adapt—the context correctly, whereas the human cannot do the same analysis. We refer to this as dehumanization; the human is no longer needed to adapt such context situations. Even though this sounds extreme, we have seen the emergence of such a level of understanding in more trivial tasks.

For example, self-driving cars have been known to avoid animals unseen by the human eye.

In context awareness, we argue that such development is based on the habit-building capabilities of the technologies. It is well known that technologies change and build habits [49]. Through these changes, people will adopt technolo-gies as part of their everyday lives. Furthermore, previously owned skills become obsolete and thus are forgotten or unused. A trivial example of such progress is the mobile phonebook application, which has reduced the need to remember phone numbers.

Thus, to understand habits, we must explore practice and reconfiguration of them. As Shove, Pantzar, and Watson (2012) argue, a (simplified) model of practices is the links between the competences, materials, and meanings [50]. In this terminology, the mobile phonebook application became an unused practice as material aspects, such as the possibility to directly call via the mobile phone, and the convenience of this technology reduced the need and the competences to maintain phone numbers.

Applying the notions of both habit and practice, we ask how subjective context awareness might reconfigure daily life. In the previous case descriptions, we ob-served that, similar to the phonebook application, previously commonly shared skills such as planning daily life or exploring information were automated. In general, we have argued that subjective context awareness will understand emo-tions and values as well as humans and then automate processes based on those understandings. Thus, this automation will replace many competences humans currently have and break the competence-material-meaning nature related to practices. This will disrupt the practices relating to self-regulation as well the management of interpersonal relations; however, we do not foresee that the

meaning of those activities would change, indicating that being a human remains a rather similar activity, albeit augmented by technology.

We find the conditions where the information is not mutually shared between the technology and the user most interesting. In a given case that there is a “nology misinterpretation,” then we can say that our previous discussion on tech-nology has failed to achieve subjective contextual information. In these cases, the context-aware services will have failed to provide added value, and instead, automation will lead to negative experiences, as noted earlier.

Similarly, the condition in which technology could surpass humans in recog-nizing and adapting to the context is interesting, implying that humans cannot comprehend all relevant factors. We suggest that these services will create habits, and that this is a likely outcome for many cases where humans can currently de-termine the context. Said differently, we argue that as people build trust in these technologies, they will also consider more automation possibilities; and as humans automate tasks, inversely, their skills to carry on similar tasks will decrease. Our argument about subjective context awareness notes that some of the trivial subjec-tive computational tasks will become automated as machines develop learning. We wonder if this contradictory process will make us less human in the end.