Summary
In this paper, the authors study the role of studying human-AI team performance in contrast to their individual performance and explain its necessity. They explain the importance of human inference of AI tools. Humans develop mental models of AI’s performance. Advances made in AI’s algorithm only evaluate the improvement in the prediction. However, the improvements cause behavioral changes in AI that do not fit the human’s mental models and reduce the overall performance of their team. To alleviate this, the authors propose a new logarithmic loss that considers the compatibility between human mental models and AI models for making updates to the AI model.
The authors construct user studies to show the development of human mental models across different conditions. Additionally, they illustrate the degradation in overall team performance with improvement in AI’s prediction. Furthermore, they show the addition of the additional loss increases the overall team performance of the AI model while increasing AI’s prediction efficiency.
Reflection
Humans and AI form formidable teams in multiple environments and I think such a study as a necessity for further development of AI. Most state-of-the-art AI systems are not independently useful in real-world and rely on human intervention from time-to-time (as discussed in previous classes). Till a point of time where this situation exists, we cannot improve AI independently and have to consider the humans involved in the task. I believe the evaluation metrics currently used in AI research are completely focussed on the AI’s prediction. However, this needs to change and the paper is a great primary step in the direction. I believe we should construct more such evaluation metrics for various other AI tasks. But, if we develop our evaluation metrics around human-AI teams, we take the risk of potentially making AI systems reliant on human input. Hence, there is a possibility that AI systems never independently solve our problems. I believe the solution lies in interpretability.
Current AI techniques rely on statistical spaces that are not human-interpretable. Focusing on making these spaces interpretable allows human comprehensibility. Interpretable AI is a rising research topic in several subareas of AI and I believe it can solve the current dilemma. We can develop AI systems independently and all the updates will be comprehensible by humans and they can accordingly update their mental models. But, we interpretability is not a trivial subject. Recent work has only shown incremental progress and the work still compromises on prediction ability for interpretability. The effectiveness of AI is observed because of their ability to recognize patterns in dimensions incomprehensible to human beings. The current paper and interpretability both require human understanding of the model and I am not sure if this is possible.
Questions
- Can we have evaluation metrics for other tasks based on this? Will it involve human evaluation? If so, how do we maintain comparative fairness across such metrics?
- If we continue evaluating Human-AI teams together, will we ever be able to develop completely independent AI systems?
- Should we focus on making the AI systems interpretable or their performance?
- Is interpretable AI the future for real-world systems? Think about, for every search query made, the user is able to see all their features that aids the system’s decision making process.
Word Count: 545
