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Computer Vision AI Accelerators Inference & Training Brain-Inspired Computing
Chip Design Compute 2.0 Data Centers Machine Learning

17 May 2023 4 min read


AN INTRODUCTION TO ETHICAL CONSIDERATIONS IN ARTIFICIAL INTELLIGENCE

Artificial Intelligence

ANA SIMION

Read More

 1. What do we mean by “Ethics” in AI?
 2. Ethical considerations: Automation
 3. Ethical considerations: Bias
 4. Ethical considerations: Privacy
 5. Conclusions
 6. Bibliography


WHAT DO WE MEAN BY “ETHICS” IN AI?

Ethics involves the broader considerations of artificial intelligence (AI) and
how it plays a  role in society beyond the code. With Data Science holistically
being a working capital-hungry, high-risk investment, ensuring AI is deployed
without denigrating the rights and freedoms of individuals is key. So, let’s
start with a big one that often is brought up: automation.


ETHICAL CONSIDERATIONS: AUTOMATION

At the very heart, AI is a tool. Yes, it’s an incredibly fancy one but it’s a
tool. One of the key areas where AI ethics comes into play is automation.
Recently I’ve written an article “Is Artificial Intelligence killing
creativity?” which’ll also cover some of my thoughts on this area.

If we take the Arts sector, for example, some fear AI will take jobs people have
trained for years on: a very valid concern, and it’ll only be a matter of time
to see how this pans out.

According to the World Economic Forum Future of Jobs Report 2023, example jobs
likely to see the largest decline are clerical and secretarial roles. In
addition, an Accenture paper “A  new era of generative AI for everyone” predicts
approximately 40% of all working hours could be impacted by Large Language
Models (LLMs) such as ChatGPT- 4 (Chat Pre Trained Generative Transformer).

This is as a result of those language tasks accounting for 62% of the total time
employees work. A key consideration to keep in mind though is machines won’t
just replace humans: through a combination of augmentation and automation, 65%
of the time spent on language tasks could be transformed into productivity.



ETHICAL CONSIDERATIONS: BIAS

We then turn to another key ethical consideration. Bias, whilst not being new,
is one of a  number of areas to consider with AI systems. I think of bias as an
entity wrapped in an invisibility cloak: it can easily intertwine its way
through training data (the data we train the system to make decisions on), even
if special category data (ie: gender, race) is removed.  

There’s plenty of research going into bias in AI systems, and as a result, the
issue of bias creates an interesting domino effect of how it can occur in a
cycle of human decision making which could then seep into system design.

With the above in mind, within bias comes the concept of fairness. Now this is
where things get even more intriguing. Measuring fairness in itself can create
barriers. Technical ways of defining fairness have been created, for example,
the requirement of equal predictive value.

In addition, another is to require models to have an equal false positive and
false negatives across groups. It’s important to note thought that differing
definitions of fairness can’t always be simultaneously satisfied.

In summary, mitigating bias isn’t just about tuning or changing algorithms.
Let’s think about human-in-the-loop systems: having humans and machines working
together is a powerful combination.

Humans being able to select options generated by the system allows for a
 holistic assessment of how much weighting should be given to a system-generated
recommendation, thus in the longer term being useful in increasing both
confidence and transparency.


ETHICAL CONSIDERATIONS: PRIVACY

The last part I’ll cover is in my view the backbone of ethics: privacy. This is
an area that can result in a continued tug of war within organizations:
respecting the customers' privacy versus using data to enhance their experience
to keep them loyal.

The UK has some of the toughest privacy laws through the General Data Protection
Regulation (GDPR). With petabytes of data being shared around the world every
second, bringing AI into the mix creates murkier waters: a system could identify
an individual who originally wasn’t identifiable from the input datasets
standpoint.

On a separate note, whilst the input data into an AI system may be
straightforward the data processing in the “black box” could still reveal
unwelcome surprises. Whilst it’s impossible to eliminate the risk completely,
completing a Data Protection Impact Assessment (DPIA) is crucial in both
understanding and minimizing these risks.

As information sharing increases and AI-based systems become more advanced, AI
and privacy will continue to be a long and complex road to navigate. As a
result, regulating these systems to ensure they don’t get out of control will be
key, and one to watch too.



CONCLUSIONS

In summary, ethics is a fascinating part of a data journey. The rapid rise of
ChatGPT has resulted in a paradigm shift in how we think about AI and ethics.
Irrespective of whether you’re a startup or a multinational, I can’t emphasize
this enough: put ethics at the heart of your data strategy: don’t just launch
into the fancy code.


BIBLIOGRAPHY

Gordon, C. (n.d.). 2023 Will Be The Year Of AI Ethics Legislation Acceleration.
[online]  Forbes. Available at:
https://www.forbes.com/sites/cindygordon/2022/12/28/2023-will-be
the-year-of-ai-ethics-legislation-acceleration/?sh=345d8fade855 [Accessed 12 May
2023].

Manyika, J., Silberg, J. and Presten, B. (2019). What Do We Do About the Biases
in AI? [online] Harvard Business Review. Available at:
https://hbr.org/2019/10/what-do-we-do about-the-biases-in-ai.

A new era of generative AI for everyone. (n.d.). Available at:
https://www.accenture.com/
content/dam/accenture/final/accenture-com/document/Accenture-A-New-Era-of
Generative-AI-for-Everyone.pdf.

Bossmann, J. (2016). Top 9 ethical issues in artificial intelligence. [online]
World Economic  Forum. Available at:
https://www.weforum.org/agenda/2016/10/top-10-ethical-issues-in
artificial-intelligence/.




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