Empathic AI: Human Factors, System Assessment and Standardisation

Empathic AI: Human Factors, System
Assessment and Standardisation
Professor Aladdin Ayesh
School of Natural and Computing Sciences
University of Aberdeen
Aberdeen, UK
aladdin.ayesh@abdn.ac.uk
https://orcid.org/0000-0002-5883-6113
Invited talk and open discussion session at
the 11th IEEE Tunisian Students & Young Professionals
Congress, Hamammat, Tunisia,
December 2023

Empathic AI: Human Factors, System Assessment and Standardisation
Outline
Introduction
Technological Context
Human Factors
Standardisation
Technologies that can help
Conclusion - Q & A
Open Discussion Session
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Introduction
Introduction
We start with some key technologies that enable empathic
systems. This provides the context within which key challenges of
developing and deploying empathic systems can then be identified
and discussed. Our focus will be key challenges that are related to
human factors, e.g. the psychological impact and potential
interrelationships with such systems. This leads us to discuss the
standardisation approach to providing guidance and assessment
measures with particular references to IEEE P7014 Standard for
Ethical considerations in Emulated Empathy in Autonomous and
Intelligent Systems 1
1
https://standards.ieee.org/ieee/7014/7648/
3 / 30

Introduction
Introduction
Once the presentation part of the talk complete, an open group
discussion to follow on some of the difficult topics in this area
some of the IEEE Standards working groups are trying to address,
e.g. IEEE P7014 2.
2
https://sagroups.ieee.org/7014/
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Introduction
Introduction
For example:
▶ The potential for interdependency between user and empathic
system and the implications of such interdependency on
physical and psychological well-being,
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Introduction
Introduction
For example:
▶ Issues around data ownership, both data provided directly by
the user or collected by the empathic system in the course of
continuous interaction with the user, i.e. observations.
5 / 30

Introduction
Introduction
For example:
▶ Issues of trust and trustworthiness especially in the contexts of
the previous two areas.
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Introduction
Introduction
For example:
▶ Issues of trust and trustworthiness especially in the contexts of
the previous two areas.
▶ The potential of unknown issues as such could emerge from
adaptive empathic systems that can evolve.
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Computational Models
There are various computational models to represent emotions and
reason about the affect state, thus enabling various forms of
human-machine and in some cases machine-machine interactions.
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Issues to consider when looking at computational models:
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▶ Data
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▶ Data
▶ Emulating
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▶ Data
▶ Emulating
▶ Estimating
7 / 30

▶ Data
▶ Emulating
▶ Estimating
▶ Understanding
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Sensors
In the last decades, we saw:
▶ Advances in sensors, e.g. ECG, EEG, etc.;
▶ Widened scope - ability to monitor so many human; activities,
e.g. sports and sleeping, at fine details, e.g. heart rate;
▶ Accessibility.
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Sensors
The accessibility of these sensors in recent years meant they are
the subject of intensive studies to explore their use in variety of
applications from sleep monitoring to assisting students in their
education.
In the process, systems are now more capable than ever to
recognise emotions, intentions, attentions, and wider range of
cognitive metrics and affective states.
9 / 30

Cognitive Frameworks
Cognitive frameworks allow the merger of internal representations
and environmental sensing into an integrated system.
We can simplify this into a planning-like cycle:
Cognition Cycle
Recognition - Representation - Expression cycle
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Cognitive Frameworks
Just imagine a situation where a machine, e.g. a cognitive robot,
can observe a user, emulate understanding of that user’s emotions,
and express that understanding into emulated emotional
expressions, gestures, speech, or other interaction forms.
In the process, the machine can give the impression of empathy
implicitly or explicitly.
11 / 30

Human Factors
Perception and Belief
▶ The user’s perception of a system with which they are
interacting could influence the user’s trust and acceptance of
the given system.
12 / 30

Human Factors
the given system.
▶ Empathic systems are highly relatable, could give a positive
outcome of a better system acceptance and usability.
12 / 30

Human Factors
the given system.
▶ Empathic systems could manipulate the user’s perception,
thinking process and views beyond what standard AI systems
could do?
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Human Factors
the given system.
could do?
▶ Could this lead to a misplaced trust?
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Human Factors
the given system.
could do?
▶ Could this lead to a misplaced trust?
▶ This leads to a much more complex debate, which we revisit
later.
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Human Factors
Interdependency
▶ Addiction to video games and social media can serve as
examples
▶ How often do you use personal assistant on your mobile
phone?
▶ Can you stop using your mobile phone for a day? a month?
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Human Factors
Competency
User’s competency is always going to be an issue with new
technologies. For example, how confident do you feel using a new
washing machine, a new car, or a new mobile phone?
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Human Factors
Psychological Impact
Empathic technologies have high psychological impact potential
▶ that can be utilised in health applications;
▶ can form part of managing AI impact on well-being,
▶ IEEE 7010 standard 3.
3
https://standards.ieee.org/ieee/7010/7718/, https://ieeexplore.ieee.org/document/9283454
15 / 30

Standardisation
Framework
In the case of developing a standard for AI empathic systems, we
may consider:
▶ AI development lifecycle(s);
4
IEEE SA: 5-issues-at-the-heart-of-empathic-ai
16 / 30

Standardisation
Framework
may consider:
▶ The risks and issues to address, or;
4
16 / 30

Standardisation
Framework
may consider:
▶ Different groups of stakeholders and how they relate to the
system, or;
4
16 / 30

Standardisation
Framework
may consider:
▶ Different groups of stakeholders and how they relate to the
system, or;
▶ Constructing and tracing use cases4.
4
16 / 30

Standardisation
Intimate Data
Personal data ⇒ Intimate data
There is a need for appropriate levels of data privacy to include
meaningful consent and data-usage limitation. This of course
comes with limitations beyond those already identified in current
research, potentially stifling accessibility, interoperability, business,
and innovation.
17 / 30

Standardisation
Culture, Diversity and Bias
Bias in developing empathic AI systems can take many forms.
Lack of diversity in training data is first to mind
But ...
The nature of empathic AI systems extends the causes of bias to
arrange of problems plaguing the human-machine interaction.
To give an example, an empathic enabled healthcare system may
recommend treatments and life adjustments that does not take
into consideration cultural factors or personal differences due to
gender, background, and so on.
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Standardisation
Affect and Rights
Let us assume you have an empathic fridge that can detect your
emotions and intentions every time you reach to its handle to
retrieve food or drinks.
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Standardisation
Affect and Rights
If that fridge can detect your mood and what you may be thinking
about for dinner, would you feel that:
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Standardisation
Affect and Rights
▶ you have any privacy of thought?
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Standardisation
Affect and Rights
▶ What if it is
▶ your car,
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Standardisation
Affect and Rights
▶ What if it is
▶ your car,
▶ your mobile phone,
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Standardisation
Affect and Rights
▶ What if it is
▶ your car,
▶ your gym equipment,
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Standardisation
Affect and Rights
▶ What if it is
▶ your car,
▶ your gym equipment,
▶ your TV?
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Standardisation
Implementation
This challenge has many facets, for example:
▶ technological,
▶ financial,
▶ environmental,
▶ business practices
Others?
21 / 30

Standardisation
Implementation
Influencing stakeholders, for example:
▶ developers,
▶ users,
▶ and?
Stakeholders and their influence may vary depending on the
application domain and/or service delivery.
22 / 30

Explainable AI
There are various end-users realisations of explainable AI, e.g.:
▶ Dialogue systems
▶ Simulators - games
▶ Generative AI
23 / 30

Privacy Preserving Models
There is always a trade off that is necessary between data utility
that enables data analytics and data privacy preserving.
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Conscious Machines
Machines that understand:
▶ the human cognitive situational perception, for example less
obvious emotions and affect states connected to the sense of
self, such as shame, embarrassment.
▶ sense of self in reference to itself, i.e. self-awareness.
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Conclusion - Q & A
Conclusion
▶ Empathic technologies are already here, and part of the future.
▶ We better be prepared to manage empathic technologies
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Conclusion - Q & A
Acknowledgement
My thanks to
▶ The the 11th IEEE Tunisian Students & Young Professionals
Congress organizing chairs and committees
▶ The IEEE Computer Society Distinguished Visitors Program
(DVP)
▶ The IEEE P7014 working group
Finally my thanks and gratitude to all of you for attending and for
your interest.
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Conclusion - Q & A
Q & A
Any Questions?
Contact
Please do not hesitate in contacting me:
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Conclusion - Q & A
Q & A
Any Questions?
Contact
Please do not hesitate in contacting me:
▶ And now let us explore some case studies ...
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30 minutes - three scenarios
Scenarios to discuss
▶ Scenario 1: What if your fridge knows what you are thinking
30 / 30

▶ Scenario 2: What if there is data market
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▶ Scenario 2: What if there is data market
▶ Scenario 3: What if you fall in love with your smart
car/home/companion robot
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Empathic AI: Human Factors, System Assessment and Standardisation

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