Why Automate This? Exploring the Connection between Time Use, Well-being and Robot Automation Across Social Groups (2501.06348v1)

Abstract: Understanding the motivations underlying the human inclination to automate tasks is vital to developing truly helpful robots integrated into daily life. Accordingly, we ask: are individuals more inclined to automate chores based on the time they consume or the feelings experienced while performing them? This study explores these preferences and whether they vary across different social groups (i.e., gender category and income level). Leveraging data from the BEHAVIOR-1K dataset, the American Time-Use Survey, and the American Time-Use Survey Well-Being Module, we investigate the relationship between the desire for automation, time spent on daily activities, and their associated feelings - Happiness, Meaningfulness, Sadness, Painfulness, Stressfulness, or Tiredness. Our key findings show that, despite common assumptions, time spent does not strongly relate to the desire for automation for the general population. For the feelings analyzed, only happiness and pain are key indicators. Significant differences by gender and economic level also emerged: Women prefer to automate stressful activities, whereas men prefer to automate those that make them unhappy; mid-income individuals prioritize automating less enjoyable and meaningful activities, while low and high-income show no significant correlations. We hope our research helps motivate technologies to develop robots that match the priorities of potential users, moving domestic robotics toward more socially relevant solutions. We open-source all the data, including an online tool that enables the community to replicate our analysis and explore additional trends at https://hri1260.github.io/why-automate-this.

• The paper finds that time spent on household tasks does not significantly correlate with the desire for automation, challenging conventional assumptions.
• The study shows that emotional factors, especially low happiness and high pain scores, strongly influence the preference for automating tasks.
• Analysis uncovers gender and income-based differences, with women favoring stress reduction and men seeking to alleviate unhappiness in task automation.

Exploring the Connection Between Time Use, Well-being, and Robot Automation

The paper, "Why Automate This? Exploring the Connection between Time Use, Well-being and Robot Automation Across Social Groups," investigates the motivations that drive individuals to prioritize specific household tasks for automation. The research leverages three distinct datasets: BEHAVIOR-1K, the American Time-Use Survey (ATUS), and the ATUS Well-Being Module. The main goal of the paper is to understand whether the desire for automation of household tasks is influenced more by the time those tasks require or the emotional experiences they invoke, and how these inclinations vary across gender and income groups.

Key Findings

1. Time Spent vs. Desire for Automation: Contrary to common assumptions, the paper finds no significant correlation between the time spent on tasks and the desire to automate them. This insight challenges the prevailing notion that automation's primary appeal lies in time savings, suggesting instead that other factors might drive automation priorities.
2. Emotional Factors: The paper identifies happiness and pain as strong predictors of the desire for automation. Tasks associated with lower happiness scores and higher pain scores see a greater inclination towards automation. Conversely, sadness and tiredness, surprisingly, do not demonstrate a significant correlation with automation desire, an unexpected result given the intuitive assumption that unpleasant or exhausting tasks would be automated first.
3. Gender-Based Preferences: The analysis reveals gender-specific trends in automation preferences. Women tend to favor automating tasks that cause stress, whereas men prefer to automate tasks that induce unhappiness. These differences highlight the role social and cultural factors play in shaping automation choices.
4. Income-Level Disparities: Mid-income groups show a negative correlation between happiness/meaningfulness and the desire for automation, indicating these individuals prioritize automating less enjoyable activities. In contrast, low and high-income groups do not exhibit significant correlations between their emotional experiences and automation priorities, suggesting that economic factors might influence the perceived importance of these tasks differently.

Implications

The practical implications of these findings suggest that domestic robotics should not solely focus on time efficiency. Instead, they must account for emotional well-being and target tasks that align with users' psychological needs. This focus could lead to more mindful integration of robots into daily life, enhancing user satisfaction and acceptance.

Future Developments in AI

The results underscore the importance of developing AI systems that understand and prioritize human emotional states and well-being metrics. Future AI developments could involve models that predict optimal automation strategies by analyzing a broader array of emotional and contextual factors. The ongoing challenge will be creating AI that not only assists with menial tasks but also enriches human life by reducing stress and increasing happiness.

Overall, this paper elucidates the nuanced dynamics involved in the desire for domestic task automation, providing a more comprehensive basis for future research and development in socially conscious robotics. It opens avenues for creating AI systems that are more attuned to human well-being, thus promising a future where technology alleviates not just physical workload but also emotional burdens.