In recent years, there have been
several cases of AI usage where the
algorithm has started to show
discriminating behaviour towards
certain groups of people.
AI systems are typically built on data.
If this data somehow reflects our
society, it has the potential to
incorporate inequalities that dwell
Inequalities in our current societies
can be hidden in data that algorithms
use and the result of this is not
always easy to predict.
One of the justifications for automated
decision making has often been to
eliminate the biases and prejudices of
humans – in essence, to make better
decisions. The technological world has
come to realise that AI might instead
actually complicate these issues.
If we build a machine that instead of
eliminating our biases, actually replicates
and mimics them – and does this even more
accurately – then it is highly problematic.
We would not want that kind of intelligence.
Sonal Makhija, Anthropologist and Lawyer (PhD), University of Helsinki
On the surface, it seems that to
eliminate biases, you could just
delete data properties that might
lead to discrimination such as
gender, age, home addresses and
various cultural background
However, the problems run deeper.
AI systems might still come to
conclusions about these factors
based on some other data.
For example, the algorithm might
detect subtle differences between
men and women even if gender as a
data property would be removed
from the data set used to train the AI
Overseeing the ethicalness of AI and preventing
discrimination from happening
is still currently very difficult. Right now, the
responsibility is very much on the designers of the
applications – but also on the end-users themselves.
The debate about this is still very much open.
Henrik Rydenfelt, Docent of Philosophy and Communications (University of
Helsinki), Postdoctoral Researcher (University of Oulu), Chairman of the Council
of Ethics for Communications in Finland.
Artificial intelligence solutions are
becoming more complex. This
means it is also more difficult to
understand the basis on which its
decisions or analyses are made.
Neural networks, or deep learning
systems, are able to make decisions
based on extremely vast data sets.
These systems can be very precise
but are rarely fully transparent.
This is known as the black box
What happens in a black box, stays
in a black box. And what if
discrimination is taking place there?
Maybe no one would even know.
I think many organisations yet do not
understand the full power of AI, and
they do not understand that building
machine learning models may land
them in a situation where they are
actually discriminating by accident.
Jani Turunen, AI Lead, Solita
Discussing the discriminatory nature of some
AI applications is part of a wider debate
concerning AI ethics.
As AI solutions already play an essential role
in shaping our lives – and even more so in
the future – we need to co-operatively
ponder the ethical and moral dilemmas of
AI and come to agreement on some
common principles of how to ensure that AI
is utilised in an ethically sound way.
Many questions still need to be answered, such as:
How will AI models
oversee the ethics
Should AI’s decision-
making be fully
How can we be assured
that AI models don’t
To learn more about how AI ethics affect
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