Authors: Jeffrey Heer
Summary
The paper discusses interactive systems in three different areas — data wrangling, exploratory analysis, and natural language translation — to showcase the use of “shared representation” of tasks, where machines can augment human capabilities instead of replacing them. All the systems highlight balancing of the complementary strengths and weaknesses of each, while promoting human control.
Reflection
This paper makes the case for intelligence augmentation i.e. augmenting human capabilities with the strengths of AI rather than striving to replace them. Developers of intelligent user interfaces can come up with effective collaborative systems by carefully designing the interface for ensuring that that AI component “reshapes” the shared representations that users can contribute to, and not “replace” them. This is always a complex task, and therefore, requires scoping down from the notion that AI can be used to automate everything by focusing on these editable shared representations. This has other benefits i.e. helps exploit the benefits of AI in a sum-of-parts manner rather than an end-to-end mechanism where an AI is more likely to be erroneous. The paper discusses three different case studies where a mixed-initiative deployment was successful in catering to user expectations in terms of experience and output.
It was particularly interesting to see the participants complaining that the Voyager system, despite being good, spoilt them as it made them think less. This can hamper adoption of such systems. A reasonable design implication here should be allowing users to choose the features they want or giving them the agency to adjust the degree of automation/suggestions. This also suggests the importance of conducting longitudinal studies to understand how users use the different features of an interface i.e. whether they use one but not the other.
According to some prior work, machine-suggested recommendations have been known to perpetrate filter bubbles. In other words, users are exposed to a similar set of items and miss out on other stuff. Here, the Voyager recommendations work in contrast to prior work by allowing users to explore the space, analyze different charts and data points they wouldn’t otherwise notice and combat confirmation bias. In other words, the system does what it claims to do i.e. augment the capabilities of humans in a positive sense using the strengths of the machine.
Questions
- In the projects you are proposing for the class, does the AI component augment the human capabilities or strive to replace it (eventually)? If so, how?
- How do you think developers should cater to cases where users are less likely to adopt a system because it impedes their creativity?
- Do you think AI components (should) allow users to explore the space more than they would normally? Any possible pitfalls (information overdose, unnatural tasks/interactions, etc.)
Hello! Interesting reflections and discussion points. Regarding the third discussion point, “Do you think AI components (should) allow users to explore the space more than they would normally? Any possible pitfalls?”, I had a similar question in mind when I read the paper. On the one hand, the inclusion of AI components is definitely beneficial since it can help broaden humans’ understanding of the data and can help humans combat confirmation bias. On the other hand, as mentioned in the paper, the users feel that they start to think less, which is definitely problematic. I feel that in the ideal scenario, AI components should be designed and integrated in such a way that it subtly guides the users and highlights different features of the dataset but lets the user decide which direction they want to move forward in.
I liked your summary and reflection of this paper. To answer your first question, the AI in my project processes the users’ speech in order to augment what they are doing and offload their cognition onto the environment. While it is not as capable as the AI in the systems described in the paper, I believe it is a decent way of augmenting the user’s capabilities.