Oxford AI Society Brainstorm

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SUPPORTED BY:
PRODUCED BY OXFORD AI SOCIETY AND SUPPORTED BY DELL TECHNOLOGIES

The world of work faces
unprecedented challenges and
opportunities. The pandemic
has necessitated solutions
to monitor the minutiae of
workplace interaction that
is critical to facilitating
physically safe return to a
shared space and maintain
mental wellbeing during
these difficult times.
With increasing available and affordable
technology and work-life balance,
there has been a trend in recent years
towards digital transformation of
the workplace, with organisations
and individuals rethinking modes
of working and adopting remote
working, which has only been
accelerated by the pandemic, but to
do so in a manner which preserves
the productivity and connectivity
achievable when working closely
amongst colleagues.
Moreover, growing focus worldwide
on the environmental agenda has
underscored the urgent need for
solutions which make office working
more efficient and sustainable,
especially against a backdrop of
remote working at lower cost. These
solutions must all be undertaken
within a framework that upholds high
standards in data security, which will
be more important than ever before
with the shift towards distributed
working.
The recommendations we set out
seek to achieve our vision of work
redefined, one where a balance
of remote and office working
reaps the benefits of both worlds,
improving employee satisfaction and
productivity and reducing cost and
environmental impact.

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APPLYING AI FOR PRODUCTIVITY
01 Devise personalised employee back-to-work
plans with AI scheduling tools
02 Establish a virtual pooled knowledge base
with conversational AI
03 Strengthen digital skills with AI recommender systems
04 Reduce environmental impact with smart sensors and AI
05 Build a workplace right for employees
with generative design
APPLYING AI FOR SAFETY
06 Monitor PPE, social distancing and contact trace
with computer vision
07 Track employee wellbeing with natural
language processing
APPLYING AI FOR CYBER SECURITY
08 Flag suspicious cyber activity with anomaly detection
09 Safeguard data privacy with facial recognition
ETHICAL AND REGULATORY CONSIDERATIONS
10 Embed data privacy and equality considerations
into the design of technology from the outset
OXFORD AI SOCIETY BRAINSTORM
10 APPLICATIONS OF AI
FOR A REDEFINED WORKPLACE

MEET THE
OXFORD AI
BRAINSTORM
TEAM
Ella Mi: Computer Science
Ella is undertaking a Masters in Computer Science
with a focus on Machine Learning at the University
of Oxford and has a background in healthcare.
She practiced as a doctor in London during the pandemic
and, as part of her academic work, has conducted leading
studies applying machine learning to cancer care, winning
national prizes and receiving media coverage in the UK
and USA. She is passionate about translating technological
innovations into frontline healthcare practice, and has
worked with companies including Google Health and
Medopad (now Huma). She currently sits on the Technology
and Innovation Committee of the British Medical
Association and advises NHS England and Improvement
on the use of AI to improve leadership development of
healthcare professionals. In this project, she was overall
lead and focussed on the role of AI in workplace safety.
Ipshita Chatterjeei: Computer Science
Ipshita is pursuing a MSc in Computer Science at
the University of Oxford, focussing on artificial
intelligence for social good.
She has over 2 years of experience as a software engineer,
in large scale distributed systems and cloud technologies
at Adobe and a diverse technical background of research
in machine learning applications. She is a strong proponent
of diversity in technology and open source development,
and has been awarded the Outstanding Open Source
Contributor 2019 by Adobe. She was also a Rails Girls
Summer of Code Fellow in 2017, with the open source
project coala, and was also awarded the Best Girl Coder
(Delhi) 2016 by IBM. She currently serves as Outreach
Officer for Oxford Women in Computer Science Society,
for furthering her passions to promote participation of
women in technology. In this project, she has focussed
on the role of AI in redefining the remote workforce and
associated ethical considerations.

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Elizabeth Oliphant: Energy Systems
Elizabeth is a master’s student studying Energy
Systems with a passion for sustainable design.
She comes from the San Francisco Bay area and watched
the area transform into a tech hub. Early on she was
inspired to explore the awesome capabilities of computer
coding, artificial intelligence, and technology development.
In university she built environmental monitoring hardware
such as sensors and gauges, and in her Fulbright research
she used machine learning to study geological and chemical
patterns that show strong areas for geothermal energy
development. Now for her master’s at the University of
Oxford, she plans to translate the skills she’s learned along
the way into a thesis focused on using machine learning to
regulate energy demand and make whole city grids more
reliable and sustainable. In this project, she has focused on
the role of AI in physical office spaces and opportunities to
increase sustainability with the use of AI.
Jenna Yehrim Park: Law
Jenna is a third year undergraduate studying law
at Christ Church, Oxford.
She has been on the committee of OxAI since 2019,
and currently leads the Events & Media team in organising
various events with a view to make AI accessible to
all. She is particularly interested in the regulation and
governance of technology, and on AI ethics, and has been
exploring how technology interacts with the legal sector.
In this project, she focused on the ethical and regulatory
considerations of proposed technologies, and believes
that such considerations should underlie any emerging
technology which involves the use of personal data.
MEET THE
OXFORD AI
BRAINSTORM
TEAM

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OXFORD AI BRAINSTORM:
APPLYING
AI FOR
PRODUCTIVITY
01 Devise personalised employee
back-to-work plans with
AI scheduling tools
02 Establish a virtual pooled knowledge
base with conversational AI
03 Strengthen digital skills with
AI recommender system
04 Reduce environmental impact
with smart sensors and AI
05 Build a workplace right for
employees with generative design

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Planning safe return to
the workplace

Understanding the most effective way to
organize who comes in, on what days, and
for how long is a complex task, which can
be aided by AI powered scheduling and
planning tools. These can balance work
activities such as the nature of assignments
and customer interactions with risk levels
such as the kind of workspace and level
of physical contact and help chart back to
work schedules. Algorithms can also take
into account individual employee’s personal
risk factors e.g. age, pre-existing conditions
to individualise work schedules, and these
can be continuously adapted to shifting
organisational priorities and governmental
guidelines.
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Devise personalised
employee back-to-work
plans with AI
scheduling tools
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Establishing a virtual
pooled knowledge base

One of the most significant productivity
bottlenecks in remote working is the lack of
a pooled knowledge base, which a physical
office space provides. However, there is
increasing usage of conversational AI in
customer service and engagement, which
engage with users at scale, providing instant
responses round the clock and on demand.
Businesses could harness this to support
remote working. AI assistants can serve
as point of contacts for simple routine
questions and also direct employees
to other employees, with the relevant
skills and expertise, building a culture
of collaboration in remote workplaces.
Indeed, using conversational AI assistants
as virtual “buddies” as part of employee
onboarding processes may improve new
employee experiences and help new hires
fit into the organisational culture, reducing
alienation in a remote setting. Moreover,
the data collected through conversational
AI can be used to inform further training
needs of employees, especially pertaining
to those new to the organisation.
02
Establish a virtual
pooled knowledge base
with conversational AI
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Strengthening
digital skills
The shift towards remote working will
only be possible if it is matched by digital
skills, which need to be cultivated and
improved on a regular basis. At present, the
gap between current and requisite skills is
still sizeable for many organisations and
individuals, and is another productivity
bottleneck. AI solutions can be used to
track skill aspirations and trajectories and
recommend personalized learning content
and custom learning materials by filtering
the vast body of educational material
available. This would greatly improve
learning outcomes for individuals and
organisations alike, by closely matching
content to the requisite skills.
03
Strengthen digital
skills with AI
recommender systems
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Reducing
environmental impact
With the increasing shift toward remote
working, fewer employees in the physical
workplace and the operational savings
that come from this recognised by many
organisations during the pandemic, office
spaces and buildings need to be reimagined
to increase sustainability and improve
their environmental impact. AI technology
harnessing sensors and predictive analytics
can allow companies to gain a stronger
understanding of how their building is being
used and where there are opportunities
for energy savings and improved space
management. AI technology can be merged
with smart thermostats, lighting, and
operational machinery to create a smart
building that predict daily patterns and
reacts in real time to changes. The startup
turned Google enterprise, Nest, uses
machine learning algorithms to predict and
control temperatures within houses and
buildings. Nest’s Learning Thermostat can
be programmed to change the temperature
in buildings based on use, and it learns
from past temperature preferences. These
smart devices and buildings can help lower
overall energy use thus lower carbon
footprint and operating costs of office
buildings.
04
Reduce environmental
impact with smart
sensors and AI
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“OFFICE SPACES AND BUILDINGS
NEED TO BE REIMAGINED TO
INCREASE SUSTAINABILITY
AND IMPROVE THEIR
ENVIRONMENTAL IMPACT”
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Recruiting a remote workforce
With the rise of decentralized working,
geographical constraints of sourcing and
hiring employees have diminished. This
presents an opportunity for businesses to
emerge as truly meritocratic. AI tools can
help organisations target a larger talent
pool, with the potential to use intelligent
systems to automate more of the
administrative tasks related to recruitment,
which may include recommender systems to
enable delivery of promotional material for
particular roles to certain candidates based
on matching of their skills and experience
and the requirements of the position.
Streamlining work processes
While a shift to remote working is a
possibility for many organisations, some
industries, including raw materials,
manufacturing, retail, hospitality and
healthcare require employees to be on site
to create value. AI can be used to improve
working processes in these industries,
for better productivity and efficiency.
For example, Schneider Electric has been
using AI to build models of their oil fields
and predetermine when maintenance is
needed before any problems arise. This has
been shown to increase plant efficiency
by up to 20% in just a few days. Likewise,
Nokia introduced a video application that
uses machine learning to monitor assembly
lines and alert an operator if there are
inconsistencies in a production process
so that issues can be corrected in real
time, thereby increasing the volume of
non-defective goods. In healthcare, use
of remote monitoring technology, mobile
electronic health records and AI-driven
alerts can enable earlier recognition of
deterioration and intervention, leading
to better health outcomes, as well as
facilitate more decentralised working, as
has been seen with elective care during the
pandemic.
Building a workplace right for employees
Remote working during the pandemic has
highlighted the possibility of alternative
working environments. There is no doubt
that employees will return to the office
with new perspectives on the environment
they want to work in. Office spaces need
to adapt to satisfy these needs to maintain
and boost productivity. AI could be
harnessed for generative design of office
spaces, a process which identifies design
preferences and constraints and
uses machine learning algorithms to create
designs that meet these criteria, with the
advantage conferred by ML being that
many design preferences are taken into
consideration. For example, Autodesk, an
architectural and engineering software
company, used generative design to
redesign their Toronto office, surveying
around 250 employees about their office
preferences
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Build a workplace right for
employees with generative design
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APPLYING AI
FOR OFFICE
SAFETY
06 Monitor PPE, social distancing
and contact trace with computer vision
07 Track employee wellbeing with
natural language processing

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Planning safe return to the workplace
Understanding the most effective way to organize who comes in, on what days, and for
how long is a complex task, which can be aided by AI powered scheduling and planning
tools. These can balance work activities such as the nature of assignments and customer
interactions with risk levels such as the kind of workspace and level of physical contact
and help chart back to work schedules. Algorithms can also take into account individual
employee’s personal risk factors e.g. age, pre-existing conditions to individualise work
schedules, and these can be continuously adapted to shifting organisational priorities and
governmental guidelines.
Monitoring workplace safety

AI has the potential to offer fast, reliable
and affordable solutions, across the entire
chain of potential transmission, from
prevention of transmission to detection of
outbreaks to contact tracing. AI-powered
computer vision tools can automatically
monitor the workplace to ensure people
use appropriate PPE. Employees could be
issued with wearable devices that detect
the wearing of face masks and sends a
personal reminder when it is missing or
incorrectly worn, and such a device could
send notifications to a manager or HR
personnel to help identify staff members
who may require additional coaching.
Computer vision technology can also be
applied to monitoring and maintaining
social distancing within the office, guiding
more precisely tailored management of
work areas where breaches are most
frequent, for example additional training
of employees, better optimised layout and
improved signage. LandingAI developed
an AI-enabled tool that analyses real time
video streams to estimate the distance
between people which can be used to
instantly identify violations and allows
organisations to analyse data from these to
understand the locations in the office and
the kind of situations that lead to protocol
breaches. Going further, predictive
analytics can be used to track when office
spaces are being used and determine
how to optimize space while leaving
sufficient time for cleaning and sanitation.
Empiric launched WorkSafe Analytics to
monitor and evaluate pandemic safety
guidelines compliance and deliver real-
world workplace safety insights. Key
features include PPE violation detection,
real time social distancing and occupancy
monitoring, with heatmaps to highlight
areas of concern and comprehensive
analytics and reporting - compliance at
room, building, site-wide levels and their
trends over time. It provides insights
into how effective current measures
are, allowing employers to see where
they need to do more with education or
physical measures. Finally, given the risk
of environmental transmission, businesses
may shift to touchless security systems to
allow and track authorized personnel.
Here facial recognition may be a key tool.
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Monitor PPE, social distancing
and contact trace with computer vision
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Monitoring workplace safety
For early detection of cases, temperature
monitoring will be a critical tool. Wearable
devices issued to employees could have
thermal imaging capabilities which
routinely capture temperature throughout
the day and AI can help process data from
mass temperature screening, enabling
testing and self-isolation as quickly as
possible after symptoms develop. For
example, Canadian company PredictMedix
offers its AI-powered temperature
screening technology to help retail stores
prevent the spread of the pandemic.
Finally, in a self-contained environment
such as an office, contact tracing is vital to
break the chain of transmission and prevent
outbreaks. Facial recognition using data
from security cameras, alongside mobile
solutions, wearables and biometrics,
may significantly streamline this process,
automatically and rapidly identifying
close contacts of an infected person and
reducing reliance on recall. Leveraging its
smart camera technology, NTT Data has
taken contact tracing to a new level, being
able to identify all contacts of an infected
person where social distancing and PPE
was absent within a given timeframe.
Tracking mental wellbeing

While physical health and prevention of
the spread of the pandemicis a significant
priority, the need to safeguard mental
wellbeing of employees is equally
paramount, especially during a time of
economic uncertainty and remote (and
possible more isolated) working. AI-
powered solutions can analyse data
from email, text and instant messaging
platforms, video and teleconference
and identify indicators of stress, anxiety
or depression or those at risk. There
is also potential to harness natural
language processing in sentiment analysis
of language to infer emotional and
psychological states and companies could
also mine video communications for body
language and facial expression.
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Track employee
wellbeing with natural
language processing
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APPLYING AI
FOR CYBER
SECURITY
08 Flag suspicious cyber activity
with anomaly detection
09 Safeguard data privacy
with facial recognition

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Detecting suspicious
cyber activity
Machine learning may provide the missing
link between high cyber security and high
information availability. ML solutions can
be used to analyse how users work with
data, determine the role of the user and
fix permissions which keep corporate
trade secrets and IP secure. As part of this,
unsupervised learning approaches such
as anomaly detection can flag suspicious
activity. Machine learning has a particular
advantage in this as the rise in remote
working has resulted in organisations
having wider and more open network
perimeters; machine learning tools are able
to rapidly analyse the multitude of new
data points this brings and determine what
is and is not a threat based on that learning
whereas traditional rule-based security
information and event management
systems have to be manually configured
and tuned for every new input source.
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Flag suspicious cyber
activity with anomaly
detection
Ensuring authorised
data access
AI solutions can also aid data privacy.
Computer vision and facial recognition
technology could be leveraged to detect
device surroundings, particularly presence
of persons and automatically shut down
and deny access to sensitive business
files or communications if unauthorised
individuals enter the vicinity or if an
authorised individual leaves the device
unattended. This may go some way to
addressing the shift in mindset on data
security when working from home.
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Safeguard data privacy
with facial recognition
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“THE RISE IN
REMOTE WORKING
HAS RESULTED IN
ORGANISATIONS
HAVING WIDER AND
MORE OPEN NETWORK
PERIMETERS”
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ETHICAL AND
REGULATORY
CONSIDERATIONS
10 Embed data privacy and equality
considerations into the design of
technology from the outset

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Security and privacy
Any proposed technology which involves
the collection, use and storage of personal
data will need to be scrutinised from
data security and privacy perspectives.
Safeguarding employee data privacy is
not just a moral obligation; it also has
practical consequences for implementation.
Monitoring employee activity could breed
an environment of mistrust and damage the
relationship between employees and their
organisation, reducing compliance with
potentially severe impacts on safety and
productivity. This underscores the need for
handling any personal and identifiable data
of individuals in a responsible and ethical
manner, especially in light of increasing
public awareness about data privacy and its
emergence as a modern fundamental right.
The UK Information Commissioner’s Office
has issued guidance for organisations
regarding their approach to data protection.
The principles of the law – transparency,
fairness and proportionality – must be
applied.
This includes:
1
Only collecting and using data that is
necessary and proportionate, keeping
data collected to a minimum, prioritising
least privacy intrusiveness e.g.
anonymised data to reduce risk of re-
identification and only using data for the
stated purpose
2
Keeping information secure, ensuring
access only by those authorised, and
having a retention policy that sets out
when and how personal information
needs to be reviewed and deleted
3
Being clear, open and honest with staff
about their data
4
Allowing staff to have control over their
data and exercise their information rights
In handling of health monitoring data,
there are additional requirements including
identifying a lawful basis for using the
information collected, and conducting a
data protection impact assessment if the
data is being processed on a large scale.
Additionally, privacy considerations should
be build into technology according to
the principles of Privacy by Design and
Privacy by Default, an initial privacy impact
assessment needs to be conducted and
product roadmaps should be explained by
reference to privacy impact and control
measures.
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Ethical and
regulatory
considerations
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Security and privacy
Where additional functionality is
developed, there is a need to iteratively
reassess privacy implications and there
must be governance and accountability
processes for ongoing monitoring and
evaluation of data processing. Given that
these regulations are on the premises
that businesses have a responsibility in
the recovery phase to collect additional
personal information with the purpose
of providing a safe working environment,
there should be a commitment to lowering
the extent of monitoring once infection risk
decreases.
In the context of the recommendations
outlined above, data minimisation may
entail the organisations creating a checklist
of data points, and considering each
point’s contribution to the core objective.
In the monitoring and detection of social
distancing violations, for example, it may
be assessed whether there is a way to
issue warnings without identifying the
individual. Furthermore, it may be helpful
to note that preventative measures may
often be preferable to punitive, as they are
less likely to require identification of the
individual.
As well as regulation, organisations
must build a trust culture to engage with
employees. This will require a great deal of
communication on why and how AI is being
used, the key factors driving the system’s
recommendations, the technology’s
limitations, and the human judgment calls
that feed the system and interpret the
data, to help employees understand the
scope and benefits of these tools. Once
employees have been informed about
the process and understand the reasons
behind it, they should have a chance to
opt out without fear of penalty or, ideally,
proactively choose to opt in because they
see the value of doing so. The GDPR gives
people the right to object to the processing
of their personal data, but even if such
rights are not exercised, the onus is on
an organisation to ensure that the use of
data is necessary and proportionate in
the circumstances. In this process, they
should be mindful of compliance with
data protection principles such as data
minimisation and accuracy. As the ICO
iterates, it is “important for organisations to
be able to justify their reasoning and clearly
document their decision” in case of such a
challenge.
Finally, employees themselves should
benefit from sharing their data, which
could involve updates on their health
and feedback on their work activity for
personal development which can increase
job satisfaction and performance, and
sharing high-level findings to raise
awareness of common issues and good
practice while preserving individual
anonymity and confidentiality.
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Ethical and
regulatory
considerations
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In implementing any of the above recommendations,
to be mindful of the principles governing data
privacy and the general objectives of ethical AI
Equality and diversity
A key issue with the shift to technology
in the world of work is the risk of creating
access inequalities between those who are
more and less technologically proficient.
To this end, employers should be mindful
of the need for workforce training. The
usage of AI to bridge the digital skills gap
is a welcome step towards promoting
equality in access to knowledge in a
remote office environment, as these lower
cost solutions will help improve skills in
disadvantaged communities, allowing for
their greater representation in the remote
workforce of the future. Usage of AI in
the digital workforce must not create or
perpetuate biases based on employees’
gender, race, sexual orientation and other
protected characteristics. Thus, monitoring
of employees must be implemented with
care. Technology used to monitor staff
mental wellbeing, which may identify
mental health issues in certain employees,
must not lead to these employees receiving
less favourable treatment, missing out
on promotional opportunities or, in the
extreme case, losing their position. It is of
paramount importance that health issues
flagged are treated confidentially and
by staff independent of employees’ line
managers or chain of command. Similarly,
technology to monitor work activity
should avoid any assumptions or bias in
the interpretation of data insights which
lead to unfair disadvantage, for example
flagging an employee’s discussion about
their familial responsibilities as a sign of
low engagement, or worse still, associating
it with traditional gender roles e.g. females.
AI solutions used for hiring need to be
carefully scrutinised to eliminate any bias
against certain groups of candidates, which
would defeat the aim of these technologies
to provide equitable access to employment
opportunities. Such systems can suffer
from biases harboured by historical training
data, for example underrepresentation of
women in STEM industries, which could
result in an algorithm penalizing female
candidates. This should be one of the
primary points of consideration while
employing such tools in recruitment. Usage
of AI to create a shared knowledge pool
may encounter similar difficulties. It may
be that male employees in the past held
higher or more technical positions in an
organisation in which case an AI assistant
may learn to direct users towards female
employees for simpler, routine questions
while fielding tougher questions to male
employees. Thus, it is important that
weights of the features in the training
process are inspected such that the
greatest emphasis is placed only on the
expertise and experience of employees in a
certain area.
AI solutions can also create healthy
biases with regard to improving equality.
For example, while large organisations
may have the resources to use custom
enterprise solutions for bridging the digital
skill gap, smaller organisations will greatly
benefit from AI technologies which can
play a major role in democratization of
digital upskilling for all organisations.
Equally, AI to facilitate productive remote
working and improve efficiency of office
spaces, will benefit smaller players,
for whom premises is often a limiting
investment.

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“AI SOLUTIONS
CAN ALSO CREATE
HEALTHY BIASES
WITH REGARD TO
IMPROVING EQUALITY”

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Oxford AI Society is the
largest student-run AI
society in the UK, serving
as a platform to educate,
build, connect and employ
an interdisciplinary AI
community that drives
innovation and works toward
the long-term social benefit
of AI.
Dell Technologies shares the belief in the
power of innovative technologies to serve
the social good, resulting in this brainstorm
of how transformative technologies in AI
can address the highly current problem
of redefining work environments in the
modern era.
This whitepaper is the
result of a collaboration
between Oxford
AI Society and Dell
Technologies.
Arash Ghazanfari
CTO Dell Technologies UK
Dell Technologies is proud to support
Oxford AI society. The team working on this
project has engaged with the topic of Work
Redefined and come up with some novel
solutions. It is more important than ever to
engage in a dialogue for how technology
will redefine the workplace.
SUPPORTED BY:

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Oxford AI Society Brainstorm

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