Summary:
“Agency plus automation: Designing artificial intelligence into interactive systems” by Heer talks about the drawback of using artificial intelligence techniques for automating tasks, especially the ones that are considered repetitive and monotonous. However, this presents a monumentally optimistic point of view by completely ignoring the ghost work or the invisible labor that goes into making ‘automating’ these tasks. This gap between crowd work and machine automation highlights the need for design and engineering interventions. The authors of this paper try to make use of the complementary nature strengths and weaknesses of the two – creativity, intelligence, world-knowledge of the crowd workers and the cheap and no cognitive overload provided by automated systems. The authors describe in detail the case studies of interactive systems in three different areas – data wrangling, exploratory analysis, and natural language translation. These systems combine computational support with interactive systems. The authors also talk about sharing representations of tasks to include both human intelligence and automated support in the design itself. The authors conclude that “neither automated suggestions nor direct manipulation plays a strictly dominant role” and ” a fluent interleaving of both modalities can enable more productive, yet flexible, work.”
Reflections:
There is a lot of invisible work that goes into automating a task. Most automated tasks require hundreds, if not thousands, of annotations. Machine learning researchers turn a blind eye to all the effort that goes into annotations by calling their systems ‘fully automated’. This view is exclusionary and does not do justice to the vital but seemingly trivial work done by the crowd workers. One of the areas that one can focus on is the open question of shared representation – Is it possible to integrate data representation with human intelligence? If yes, is that useful? Data representation often involves the construction of latent space to reduce the dimensionality of input data and get concise and meaningful information. There may or may not be such representations exist for human intelligence. Maybe borrowing from social psychology might help in such a scenario. There can be other ways to go around this. For example, the authors focus on building interactive systems with ‘collaborative’ interfaces. The three interaction models: Wrangler, Voyager, and PTM do not distribute the tasks equally between humans and automated systems. The automated methods prompt the users with different suggestions which the end user reviews. The final decision making power lies with the end user. It would be interesting to see what would the results looks like if the roles were reversed and the system was turned on its head. An interesting case study could be if the suggestion was given by the end user and the ultimate decision making capability rested with the system. Would the system still be as collaborative? What would the drawbacks of such systems be?
Questions:
1. What are your general thoughts on the paper?
2. What did you think about the case studies? Which other case studies would you include?
3. What are your thoughts on evaluating systems with shared representations? Which evaluation criteria can we use?
Hi,
Your reflection about automated machines needing countless annotations made me wonder if some of the automated tools include crowdsourcing in them. This paper didn’t seem to mention anything about crowdsourced microtasks. If these systems do indeed include crowdsourcing, then I am in awe of how much money it would take to maintain the system (especially since we are all struggling to build our projects within the available funding).
As for your third question, I think I would measure speed. Since a big focus of this paper was boosting human capability via naturally integrated systems, it would make sense that a good system would also boost task completion speed.
Hi Akshita, really nice reflection. I think the paper overall was very powerful, and in fact, it inspired one of my own papers in the past as well. The case studies were interesting, but as is always the case, they can be a little limiting in their validity. I believe shared representations themselves, as an idea, is very powerful and can enable much stronger human-AI collaboration.