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Changes for page f. Effects

Last modified by Manali Shah on 2023/04/10 23:06
From version 2.1
edited by Manali Shah
on 2023/02/21 12:11
Change comment: There is no comment for this version
To version 3.1
edited by Manali Shah
on 2023/04/01 00:57
Change comment: There is no comment for this version

Summary

Details

Page properties
Content
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2 2  |(% style="width:113px" %)**Downside**|(% style="width:705px" %)(((
3 3  The system needs constant inputs and feedback to improve and learn the requirements of the patients. This requires data collection on a constant basis, and raises questions on the privacy of the human users.
4 4  
5 +The system could be perceived as annoying, intrusive and interrupting. This could lead to worsening of the mood swings of the patients.
6 +
5 5  Long term use could lead to heavy dependence on the system, and the users may not be able to function without the system (reminders, motivation etc), when the system is not present (ex, taken down for maintenance or upgradation).
6 6  
7 7  If caregivers rely too much on the AI system, they may disregard the benefits of human touch and care, which could lead to negative consequences for the patient.
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21 21  The system could ask the caregivers to enter a Yes/No for whether each task was performed. For example, did the patient take medicine after being reminded? Or did the patient eat their meal happily?
22 22  
23 23  - To find the dependency of the users on the system, the system could be taken down for a day or two. The caregiver (Eleana) could aid the patient instead and then answer questions on whether she was able to effectively perform tasks otherwise automated by the AI system
26 +
27 +- For the mood graph, if the values are between 1 and 10, we could keep a benchmark of around 5-6 so that the system improves its performance to adhere to the patient's preferences. While the patient's mental state is not always in control of the system, it could prove to be a stabilizing factor.
28 +
29 +- For the explicit feedback, we could set a benchmark of around 70-80% positive feedback, which would imply that the patient was able to perform 70-80% of the tasks successfully.
30 +
31 +(Scenario A)
32 +
33 +- To measure dependency, we could use the same explicit feedback but set a lower benchmark of 65-70% since we remove the system from the interaction.
34 +
35 +(Scenario B)
24 24  )))