Designing predictive relations in more-than-human partnerships

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Introduction

With the rise of the Internet of Things and the shift from single products to decentralized systems, the functional working of artifacts will be defined for a great part in the digital layer. With the addition of Artificial Intelligence and Machine Learning capabilities, predictive relations are added to the mechanics of designing connected products, with implications for the agency users have in an algorithmic society.

The potential impact on the design space is explored through a design case of an intelligent object becoming a networked object with added predictive knowledge. This chapter introduces what will be the change that predictions will make to the relation of users and contemporary things^[1] on a conceptual level and proposes an approach to how to translate this to new activities in designing networked objects.

Defining predictive relations

Predictive relations are the way in which a user builds a relation with the future and produces a mental model of the working of the system. Predictive knowledge seems to unlock a new type of interplay between humans and the world and between humans and non-humans: the functional working of an artifact is now shaped through that interplay -- not so much its physical characteristics or the service it provides. Predictive relations are a changing digital condition for our relationship with contemporary things.

The influence of connectedness to the character of an object is explored in different concepts of smart objects, from blogjects, spimes, objects with intent, enchanting objects are some examples [1-4]. The object is static entity though its behavior is defined in the networked capabilities. With the notion of contemporary things objects are defined as constantly changing entities; or fluid assemblages [5]. In exploring predictive relations, the focus is on the relation of the human and the object. To understand this relation the point of departure is the concept of co-performance with the notion of contemporary things as fluid assemblages. In the concept of “co-performance” activities are delegated to a contemporary thing on the basis of the unique capabilities of human and artifact or human and expert system [6].

In decentralized systems, the consequence is that how a contemporary thing is experienced does not depend so much on its physical characteristics or the service it provides but on the relation the user has with the contemporary thing. A smart object defined as a construction of time and space that could understood by the perturbations it makes [7]. The specific functioning is depending on the interplay of the user and the contemporary thing: it is not a fixed state anymore. The lens offered by the notion of fluid assemblages helps to look at artifacts more explicitly as agents within decentralized networks, beyond a narrow focus on matters of user-product experience. The assemblage is here combining material and immaterial resources, and it is conceptualized as fluid because it is assembled in runtime and changes continuously by performing both on the front of the stage and backstage [5]. It adds an extra dimension to the relation as the decentralized network unlocks knowledge about possible futures in the relation with the contemporary thing. This knowledge has an influence on the appropriateness of the delegation that is taking place in the co-performance between the user and the contemporary thing, and on the specific relations that are being shaped in the process. In the future it is expected that the things know more than the user which might lead to asymmetry in the relation [8].

Model of predictive relations – interplay-mental model-distributed network

The notion of predictive relations is influencing the design space for designing connected products. The design space is shaped by the perceptions of the human about the interplay, and the mental model is where the relations are shaped and where decisions to interact are made. The mental model is instrumental for a user in using the artifact by making a prescription a user makes before using an artifact [9-11]. The mental model is where designers can intervene to simplify complexity and understand the world, designers create metaphors to extract the mental models [12].

The mental model needs to have predictive power to allow the person to understand and to anticipate. It reflects the beliefs of the user about the system and is not the same as the conceptual model that is shaped by the designer [13]. The gap between the conceptual model and the mental model Norman describes is widened with contemporary things and especially as opaque data from the network is influencing the working of the contemporary thing. The predictive knowledge can contribute to the predictive power of the mental model to anticipate the behavior of the contemporary thing. The predictive knowledge is shaped by three types of predictions on the partaking of the thing in the interplay: (1) Patterns from activities in the past (i.e., predictive analytics), (2) profile from stored rules and data that prescribes behavior and (3) predictions built from similar situations with similar, networked users. The cues from predictions need to be understandable and relatable and it is necessary for us to trust the predictions and adapt our behavior to the predictions [14]. In figure 1 the model of predictive relations is visualized.