Intelligent algorithms have reached a new level of performance in recent years and are increasingly employed in application areas such as speech and image recognition, data analytics, or recommender systems. The proliferation of these techniques poses a range of new challenges for the design and engineering of interactive systems since they tend to act as black boxes and do not offer the transparency and level of control to the user which is considered a prerequisite for user-centered design in the HCI field. In this position paper, we provide an overview of the broad areas related to intelligent algorithms and HCI that will need further research in the future to make systems useful, usable and trustable.
1 Interactive Systems Group, University of Duisburg-Essen juergen.ziegler@uni-due.de
Since the early days of computing, two fundamentally different concepts of the relationship between human and the technical system have been promoted by the fields of Artificial Intelligence (AI) and Human-Computer Interaction, respectively. Artificial Intelligence researchers have been aiming at automating and extensively substituting human mental (and physical) performance while the perspective of Intelligence Augmentation, as represented, among others by Douglas Engelbart, saw technology as a means of enhancing human capabilities which lead to the manifold HCI innovations that we have seen over the past decades.
While AI has in the past often been charged with far-reaching promises, often
followed by disillusioning results, recent developments have pointed to a breakthrough in
the performance of intelligent technologies. New methods of machine learning,
particularly deep learning methods based on neuronal techniques
While the potential for automating human performance has increased significantly
as a result of these successes, the new intelligent techniques also deepen the gap
between user and system, since transparency and traceability of decisions generated by
probabilistic techniques are mostly not given
In the following, we will discuss what we see as the main challenges for engineering interactive systems that are fully or partially based on intelligent algorithms. Each challenge stands for a broad range of research questions that have only initially been investigated so far. In general, the challenges ask for a much closer interdisciplinary cooperation between researchers in HCI and AI which up to now are still often separated by a large gap.
For the presentation of and interaction with AI components, many specific questions
arise beyond the classical aspects of user interface design. These include the choice of
a suitable interaction style, ranging from conventional GUI solutions to conversational
and language-based interaction to anthropomorphic virtual agents
One of the central problems with regard to the usability of intelligent algorithms is
the lack of transparency and explanation of automatically made decisions or
recommendations
In addition to the explanatory nature of algorithmic decisions, the further question
from the user's perspective is whether and how algorithmic decision-making processes
can be influenced and controlled. In principle, user control can start at different stages
of the process. This ranges from the selection of data to be used for learning processes
to the selection and parameterization of algorithms and feedback on proposals and
decisions made by the system
Future research activities on these topics should include the design of interactive methods for the control of algorithmic processes as well as the development of new algorithms that allow more far-reaching intervention possibilities and closer interaction between user and algorithm. 2.4
The old question of the division of tasks between humans and technical systems
arises in a new way in the context of AI techniques, as increasingly complex mental
tasks can be automated. From the point of view of human-computer interaction, AI
systems should support users in their tasks in the best possible way and not pursue a
pure substitution strategy. Their use raises fundamental questions regarding the agency
and autonomy of users and requires new solutions regarding the interaction and
cooperation between user and system. This requires research in areas such as
mixed-initiative interactions, handover processes (e.g. automated driving) and mutual learning in
collaborative work
When machines take on cognitive tasks, a variety of questions arise that would also
arise in a comparable form for human actors in the social context (Bostrom &
Yudkowski 2011). A central problem here, too, is the inexplicability of AI-based decisions, such
as in the granting of loans or in application procedures, which is critical both in ethical
and legal terms. The demand for the explanation of algorithmically made decisions is
also anchored in the European Data Protection Directive, at least in partial aspects,
without the implementation having been sufficiently investigated and clarified so far.
Algorithmic decisions can (intentionally or unintentionally) be biased or discriminatory
for certain groups of persons
The advent of new approaches such as Deep Learning has immensely enhanced the effectiveness of intelligent algorithms which have begun to pervade a wide range of application areas. A large fraction of these systems are interactive, in that they provide services and functions that are meant to support users in performing their tasks. The black-box nature of most of these techniques, however, prevents users from comprehending and controlling the system which may lead to dissatisfaction and mistrust in the system. So far, there are neither viable nor established methods for engineering AIbased interactive systems. A much closer cooperation between the different fields and an integration of their methods will be needed to overcome the current deficits of intelligent systems from the user’s point of view.