=Paper=
{{Paper
|id=Vol-3276/SSS22_opening2
|storemode=property
|title=How to Cope with Bias in Well-being AI? - Towards Fairness in
Well-being AI by Personal and Long-term Evaluation
|pdfUrl=https://ceur-ws.org/Vol-3276/SSS-22_opening2.pdf
|volume=Vol-3276
|authors=Keiki Takadama
|dblpUrl=https://dblp.org/rec/conf/aaaiss/Takadama22
}}
==How to Cope with Bias in Well-being AI? - Towards Fairness in
Well-being AI by Personal and Long-term Evaluation==
https://ceur-ws.org/Vol-3276/SSS-22_opening2.pdf
How to Cope with Bias in Wellbeing AI? –
Towards Fairness in Wellbeing AI by Personal and Long-term
Evaluation
Keiki Takadama
The University of Electro-Communications
keiki@inf.uec.ac.jp
Abstract bias, (2) data bias, (3) sampling bias, (4) algorithmic bias,
This paper focuses on the fairness in ML (machine learning) (5) interaction bias, (6) self-selection bias, and (7) second-
meaning that output of ML should not be “biased” and aims order bias. The essential difference among them is summa-
at clarifying bias in wellbeing AI. From the analysis of bias rized as follows.
from the viewpoint of healthcare, the bias in wellbeing AI can
be reduced by employing the personal and long-term evalua- (1) Activity bias
tion while many biases in ML arise. To investigate an effec- This bias arises from the different number of active/silent
tiveness of the personal and long-term evaluation, our previ- users. For example, only top 4% of Amazon users posted
ous research conducted the human subject experiment by fo-
cusing on sleep of aged persons in care house and found that
the reviews, which means that we cannot receive mes-
our wellbeing AI based on the personal and long-term evalu- sages from all users, i.e., they are the biased messages.
ation succeed to extract knowledge for good sleep and to es- (2) Data bias
timate mind change of an aged person from her sleep quality This bias arises from the different number of data. For
change. example, the number of Westerner face pictures tends to
be larger than that of Asian in the face pictures in dataset
Introduction such as MS (Microsoft) celebrity dataset.
(3) Sampling bias
“Can ML (machine learning) provide a fair decision?”. To This bias arises from the fact where the sampled data is
answer this question, this paper starts to explain one exam- not always followed by true distribution. For example, an
ple. As you may know, Amazon developed the ML person- asthma patient rate in near highway tends to be higher
nel recruitment system but stopped it in 2018 because than the rate in whole area. This means that the data in
women do not tend to be recruited in comparison with men big city is different from the data in whole area
due to the reason why most of input data for ML is men’s (4) Algorithmic bias
data (Dastin, 2018). This example suggests an importance This bias arises from the different outcome caused by dif-
of fairness in ML. In other words, the output by ML should ferent algorithms. For example, a search ranking by
be fair or should not be “biased”. What should be noted here Google is different from the ranking by Bing. This means
is that many healthcare systems based on ML (hereafter we that the behaviors of users are biased by the different
call it as “wellbeing AI”) have also a rick of providing the search engine.
biased outputs and such outputs are very critical for our (5) Interaction bias
daily life. From this fact, this paper aims at investigating This bias arises from the different interaction according
what kinds of biases arises in wellbeing AI and how such to web presentation. When focusing on the medicine list
biases can be reduced to cope with them. For this issue, this on the pharmacy web site, for example, they are differ-
paper starts to explain bias in general by focusing on the bias ently displayed, e.g., one by one or all image. In the one
on the Internet and the bias in ML, and then clarifies bias in by one representation, users are hard to watch the less
Wellbeing AI. prioritized medicines because they need to scroll the web
page to find them. In the all image representation, on the
other hand, users tend to watch the upper left image of
Bias on the Internet medicine because we usually read the sentence from left
According to Baeza-Yeate, the bias on the web on the Inter- to right and its line starts from upper to lower. Such dif-
net is categorized as follows (Baeza-Yates 2018): (1) activity ferent representations causes bias of selecting medicines.
___________________________________
In T. Kido, K. Takadama (Eds.), Proceedings of the AAAI 2022 Spring Symposium
“How Fair is Fair? Achieving Wellbeing AI”, Stanford University, Palo Alto, California,
USA, March 21–23, 2022. Copyright © 2022 for this paper by its authors. Use permitted
under Creative Commons License Attribution 4.0 International (CC BY 4.0).
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(6) Self-selection bias ⚫ Personal information
This bias arises from the different number of users who Good information of others is not always good. For ex-
are willing to participate or not. For example, many ques- ample, the knowledge of good sleep for a certain person
tionnaires are returned from healthy persons, but not is not always useful for other persons. This indicates that
from the non-healthy persons. This is because healthy that the personal data is very important in wellbeing.
persons do not hesitate to tell their health information ⚫ Long-term evaluation
without worry about it, while non-healthy persons do not Current evaluation of health is not enough because keep-
want to tell their health information honestly due to their ing good health and better health (better life) are more
worry about it. important than the current health. This indicates that the
(7) Second-order bias long-term evaluation is very important in wellbeing.
This bias arises from the original bias. After a biased in- From the viewpoint of the personal information and long-
formation (in the high ranking) is spread, for example, term evaluation, the seven biases do not arise or can be re-
active users post other messages related to this infor- duced as the following reasons. As shown in Figure 2, firstly,
mation, and such messages are sampled in high possibil- the activity bias and self-selection bias do not arise because
ity and increases its rank in search engine. This cycle am- the data comes from only one person. This indicates that the
plifies the original bias. “single” user provides the “personal” data. Secondly, the
data bias and sampling bias can be reduced if we can get
Bias in Wellbeing AI long-term daily data. This is because such a kind of data is
not heavily biased in comparison with the short-term daily
To clarify the bias in Wellbeing AI for easy understanding, data due to the large number of data. Thirdly, an influence
let start to simplify the bias from the viewpoint of ML. Ac- of the algorithmic bias and interaction is very small because
cording to Mehrabi’s survey (Mehrabi et al. 2022), bias in only one person is affected. Finally, the second-order bias
ML arises in the cycle of (i) users, (ii) data, and (iii) algo- can be reduced because other biases in this cycle are reduced
rithm as shown in Fig. 1. The connection of the seven biases by the above reasons. From this analysis, the algorithm in
on Web to the cycle from (i) to (iii) is summarized as follows. Wellbeing AI analyzes the “personal” data that comes from
Firstly, the activity bias and self-selection bias arise in the the “single” user and provides the result to the user. This
cycle from “user” to “data” because both biases are caused indicates that the “personal and long-term evaluation” (pre-
by user and affect data. Secondly, the data bias and sampling cisely, the personal and long-term evaluation based on the
bias arise in the cycle from “data” to “algorithm” because personal data) are important for fairness AI.
both biases are found in data and affect algorithm. Thirdly,
the algorithmic bias and interaction bias arise in the cycle
from “algorithm” to “user” because both biases are caused
by algorithms and affect user. Finally, the second-order bias
also arises in this cycle, which is the same as the bias on the
web.
Figure 2: Bias in the cycle of Wellbeing AI
Personal and long-term evaluation
The goals of many examples of wellbeing AI are roughly
classified into the following two categories.
Figure 1: Bias in the cycle of ML
⚫ Keeping good health (not getting a disease)
Since many patients such as dementia, diabetes, and sleep
To consider the features of wellbeing in the cycle of aris-
apnea syndrome (SAS) want to worsen their health, it is
ing the bias of ML that connects with the bias on Web, the
important to find something wrong for early detection.
following features should be taken into consideration.
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For this issue, the personal and long-term evaluation is suggested to change the time of having rehabilitation from
needed for early detection. AM to PM. As a result, he could have a deep sleep. What
⚫ Better health (improving activities) should be noted here is that this knowledge is not always
For better health, it is important to know (measure) the useful for other persons. For person B, when the aged person
accumulated small progress and its change of activities of had rehabilitation in PM, he lost appetite due to tiredness of
daily living (ADL) for aged persons, performance after rehabilitation and could not diner as usual. This caused him
nap for office workers, and sleep quality for all ages. For not sleep very well because of hungry at night. For this prob-
this issue, the personal and long-term evaluation is also lem, our Wellbeing AI suggested to change the time of hav-
needed. ing rehabilitation from PM to AM. As a result, he could have
Among them, this paper focuses on sleep of aged persons in a deep sleep. This results clearly show that the knowledge
care house because sleep is significant for aged persons. For for good sleep is different among persons.
example, aged person easily wakes up due to light sleep and Mind change estimation of aged person
may wander in midnight. Sleep can also provide some mes- Figure 4 shows that the sleep quality of the aged diabetes
sage of mind change of aged persons when their sleep qual- person before/after the great east Japan earthquake, where
ity change from good to bad. This is because persons tend the blue dots indicate the deep sleep while the red dots indi-
to have deep sleep without anxiety but change to light sleep cate the light sleep. The horizontal axis (f1) indicates the
when they are worry about something. To tackle these issues, achievement degree of what an aged person wants to do,
our previous research developed the wellbeing AI system while the vertical axis (f2) indicates the achievement degree
for the first issue to extract knowledge for good sleep of what a care worker wants to do for an aged person. From
(Takadama et al. 2015) and for the second issue to estimate this figure, the blue dots are located at the right side while
mind change of aged person from sleep (Takadama 2013). the red dots are located at the left side before the earthquake.
These researches took the approach of the personal and This is because an aged person tended to have a deep sleep
long-term evaluation (in detail, we investigated the data of when she could achieve the activities (such as eating as
the individual persons in one year). Technically, we devel- usual) because of not being worry about anything while she
oped the sleep quality estimation system based on vital vi- tended to have a light sleep when she is hard to achieve the
bration data from pressure sensor (Harada et al. 2016). activities (such as less eating as usual) because of being
Knowledge extraction for good sleep worry about something.
In our experiment, the daily activity and sleep quality are What should be noted here is that the blue and red dots
recorded every day. In one day, many activities as such were mixed after the earthquake, which had a possibility of
meals, rehabilitation, and bathing, are scored in the integer the message of something mind changes of aged person. For
values. For example, when eating full amount of meal, the this issue, our Wellbeing AI estimated that amount of break-
score is 3. When no rehabilitation, then the score is 0. In fast should change from full to medium and the time of hav-
addition to the dairy activity, the sleep quality (i.e., the ratio ing rehabilitation should change from none to AM. After
of deep sleep) is estimated by our sleep stage estimation to these changes, she could have a usual sleep. To verity these
classify whether the deep or light sleep. suggestions, care workers asked to her and she said that she
Figure 3 shows the knowledge for good sleep. For person was not willing to eat a full amount of breakfast due to news
A, when the aged person had rehabilitation in AM, he be- of death of many people by tsunami caused by the earth-
came to be tired and mostly took a nap. This caused him not quake. Regarding the rehabilitation, she did not like it and
sleep very well at night. For this problem, our Wellbeing AI was often absent from rehabilitation. After the earthquake,
she noticed that many people killed by tsunami could not
extend their life while she could extend it by having rehabil-
itation to tackle her diabetes. This changes her mind to be
willing to exercise.
Figure 3: Knowledge for good sleep Figure 4: Light and deep sleep before/after earthquake
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Conclusions
To explore the answer to the question of how we should
cope with bias in Well-being AI, this paper started to focuses
on bias on the Internet and bias in ML and analyzed the bias
in wellbeing AI after connecting biases on the Internet and
bias in the ML. From this analysis, the bias in wellbeing AI
can be reduced by employing the personal and long-term
evaluation while many biases in ML arise. This paper dis-
cussed the fairness in Well-being AI from the viewpoint of
the personal and long-term evaluation and found that our
wellbeing AI based on the personal and long-term evalua-
tion showed its potential by extracting knowledge for good
sleep and estimating mind change of aged person from her
sleep quality change. Future work includes an investigation
of other domains.
References
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