Imagine
walking through a bustling railway station. You’re in a hurry, weaving through
the crowd, unaware that cameras are not just watching you but also recognising
you. These days, our biometric data is valuable to businesses for security
purposes, to enhance customer experience or to improve their own efficiency.
Biometrics,
are unique physical or behavioural traits, and are part of our everyday lives.
Among these, facial recognition is the most common.
Facial recognition
technology stems from a branch of AI called computer vision and is akin to
giving sight to computers. The technology scans images or videos from devices
including CCTV cameras and picks out faces.
The system
typically identifies and maps 68 specific points known as facial landmarks.
These create a digital fingerprint of your face, enabling the system to
recognise you in real time.
Face
landmarks include the corners of the eyes, the tip of the nose and the edges of
the lips. They help to create a mathematical representation of the face without
storing the entire image, enhancing both privacy and efficiency.
From
supermarkets to car parks and railway stations, CCTV cameras are everywhere,
silently doing their job. But what exactly is their job now?
Businesses
may justify collecting biometric data, but with power comes responsibility and
the use of facial recognition raises significant transparency, ethical and
privacy concerns.
When even
police use of facial recognition can be deemed unethical, then the business
justification becomes less convincing, especially as little is known how
businesses store, manage and use data.
Capturing
and storing biometric data without consent could violate our rights, including
protection against surveillance and retention of personal images.
Balancing
safety, efficiency and privacy rights is a complex ethical choice for
businesses.
As
consumers, we may often be reluctant to share our personal information. Yet
facial recognition poses more serious risks, such as deepfakes and other
impersonation threats.
Take for
instance the recent revelation that Network Rail has been secretly monitoring
thousands of passengers using Amazon’s AI software. This surveillance
highlights a critical issue: the need for transparency and stringent
regulations, even when a company is watching us with the aim of improving
services. A Network Rail spokesperson said: “When we deploy technology, we work
with the police and security services to ensure that we’re taking proportionate
action, and we always comply with the relevant legislation regarding the use of
surveillance technologies.”
One of the
core challenges is the issue of consent. How can the public ever give informed
consent if they are constantly monitored by cameras and unaware of who is storing
and using their biometric data?
This
fundamental problem underscores the difficulty in resolving privacy concerns.
Businesses face the daunting task of obtaining clear, informed consent from
people who might not even know they are being observed.
Without
transparent practices and explicit consent mechanisms, it’s nearly impossible
to ensure that the public is truly aware of and agrees to the use of their
biometric data.
Think about
your digital security. If your password gets stolen, you can change it. If your
credit card is compromised, you can cancel it. But your face? That’s permanent.
Biometric data is incredibly sensitive because it cannot be altered once it’s
compromised. This makes it a high-stakes game when it comes to security.
If a
database is breached, hackers could misuse this data for identity theft, fraud,
or even harassment.
Another
issue is algorithmic bias and discrimination. If data is used for
decision-making, how can companies ensure that diverse and sufficient data is included
to train the algorithm?
Companies
might use biometric data for authentication, personalised marketing, employee
monitoring and access control. There is a significant risk of gender and racial
biases if the algorithm is primarily trained on data from a homogenous group,
such as white males.
Companies
should also be ensuring that digital bias is not perpetuated. Failing to do so
may lead to societal inequalities.
Legislation
and awareness
As facial
recognition becomes more common, the need for robust legislation becomes
urgent. Laws must mandate clear consent before capturing anyone’s biometric
data. They should also set strict standards for how this data is stored and
secured to prevent breaches.
It’s equally
crucial that the public becomes more aware of the issue. While people are
becoming more conscious about data protection, facial recognition often flies
under the radar. It’s invisible in our everyday lives, and many don’t realise
the risks and ethical issues. Educating the public is vital.
Incorporating
the principles of responsible AI into the deployment of facial recognition
technology would be a good place to start. Responsible AI emphasises fairness,
accountability, transparency and ethics. This means that AI systems, including
facial recognition, should be designed and used in ways that respect human
rights, privacy and dignity.
However,
businesses might not necessarily prioritise these principles if they are not
being held accountable by regulatory bodies or the public.
Transparency
is a cornerstone of responsible AI. If organisations using facial recognition
remain secretive about their practices, we cannot trust them with our biometric
data.
Companies
armed with only your personal information can be very powerful in terms of
manipulative marketing. It takes only “one like” for bespoke campaigns to
target you very accurately.
But now,
political parties such as the PTI in Pakistan have embraced vision-AI
technology to allow leader Imran Khan to campaign despite serving a prison
sentence.
Visual data
capturing and analysis are particularly critical compared to non-visual data
because they provide richer, more intimate and more immediate insights into
human behaviour and identity.
That’s why
its growing use by businesses raises so many concerns about privacy and
consent. While the public remains unaware of the extent to which their visual
data is being captured and utilised, their information will be vulnerable to
misuse or exploitation.
Kamran
Mahroof
Associate Professor, Supply Chain Analytics, University of Bradford
Amizan Omar
Associate Professor of Strategic Management, University of Bradford
Irfan
Mehmood
Associate Professor in Business Analytics, University of Bradford
This article is republished from The Conversation under a Creative Commons license
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