The document discusses the current state and implications of generative AI, highlighting its capabilities in creating content but also addressing significant ethical concerns such as deepfakes, bias, privacy, and intellectual property issues. It emphasizes the need for responsible AI governance to mitigate risks and ensure alignment with organizational values while considering future developments in AI technology. Additionally, it points out the challenges of adopting generative AI due to security and trust issues.

1. Responsible AI:
The Good, the Bad,
the Ugly

2. Prof. Shahab Anbarjafari
Chief Data Scientist, PwC Finland

3. Current state of AI

4. Kemira CFO Days
Generative AI is…
a subset of Deep Learning that
involves training a model to generate
new data that is like the training data
it was given. This type of AI can be
used to create art, music, text and
even entire virtual worlds, among
other applications.
Artificial Intelligence
Machine Learning
Deep Learning
Generative
AI
Deep Learning
a machine learning technique in which layers of
neural networks are used to process data and
make decisions
Machine Learning
subset of AI that enables machines to learn from
existing data and improve upon that data to make
decisions or predictions
Artificial Intelligence
the field of computer science that seeks to create
intelligent machines that can replicate or exceed
human intelligence
Generative AI
a capability of using prompts to create, improve,
and interact with text, images, video, and sound
using large trained models
Definitions

5. ChatGPT
Towards AGI
Basic models
Deep learning
6
Dec
2017
Revolution and Reinvention

6. 2023
AI vs Human
Benchmark saturation over time for popular benchmarks, normalized with initial
performance at -100 and human performance at zero.
6
Kiela et al., https://arxiv.org/pdf/2104.14337.pdf

7. 2023
Human-like capabilities of Super AI
7
Desire
Cognition
Super
Intelligence
Behavioral
intelligence

8. 2023
Video Generation
Video generation involves training a machine learning model
to generate coherent and contextually relevant videos based
on a given input prompt.
Video generation has numerous business use cases such as
content creation, video editing, and special effects.
Business Use Cases:
1. Content creation: Video generation can be used to automate
content creation tasks such as social media videos, explainer
videos, and promotional videos.
2. Video editing: Video generation can be used to automatically
create video edits, such as adding special effects, transitions, and
color grading.
3. Special effects: Video generation can be used to create special
effects in movies and TV shows, such as generating realistic
explosions, fire, or smoke.
Gen AI can be used to modified video contents or create new video contents.
8

9. Risks of AI

10. 2023
Ethical Concerns
1. Deepfakes: Generative AI can be used to create convincing
deepfakes, which are fake images, videos, or audio that are
designed to look and sound real. These can be used to
spread misinformation, manipulate public opinion, or even
defame individuals.
2. Bias: Generative AI can also perpetuate and amplify bias in
the data it is trained on, leading to discriminatory or unfair
outcomes. This can be particularly problematic in areas such
as hiring or lending, where decisions can have a significant
impact on people's lives.
3. Privacy: Generative AI can also be used to generate
personal information or manipulate existing data, which can
threaten individuals' privacy and security.
4. Intellectual property: Generative AI can create works that
blur the line between original and derivative, leading to legal
and ethical debates around ownership and attribution.
A discussion of the ethical concerns associated with generative AI, such as
deepfakes, bias, and privacy.
10

11. 2023
Think about it…
According to the latest available data, ChatGPT currently has
over 100 million users. And the website currently generates 1
billion visitors per month.
The model was trained using text databases from the
internet. This included a whopping 570GB of data obtained
from books, webtexts, Wikipedia, articles and other pieces of
writing on the internet.
The data needed to train next generation of GPT will have
bias towards its own generated text and as times goes it start
to promote what it generated.
Based on Towards Data Science and nearly every major tech publication that GPT-
4 model has been trained on 100 trillion parameters.
11
If you tell a lie big enough
and keep repeating it,
people will eventually
come to believe it.

12. 2023
Think about it…
1. Personalisation of news: What if the generative AI has
decided who will know what part of any news? Division of
society?
2. Regeneration of more power AI: What if generative AI
understands its own flaws and generate more powerful AI?
3. Define a goal: What if the generative AI define a goal and
lock the target? Story of killing ants while walking across the
street.
4. Intellectual property: What if Generative AI smartly violates
all IP regulations of artists?
What if AI become self-aware?
12

13. 2023
Think about it…
Responsible AI toolkit helps to have a better understanding of what
is happening with AI, whether it goes rouge or not, whether there is
an adversarial attack or not.
Responsible AI will potentially help to have Good AI at our service in
case Bad AI comes against us.
Regulations can help to assure AI development will always human
being, same as what happened with biology and nuclear physics.
A maximising principle vs a prioritisation principle
Should we be scared and move to Mars?
13

14. Organisations will need to understand key risks and answer fundamental
questions around design and deployment
14
Ethical
• Lack of values risk
• Value misalignment
• Further enhance existing
discrimination and bias
Economic
• Job displacement
• Further enhance economic
inequality
• Risk of ‘winner takes all’
concentration of power
Societal
• Reputational risk
• Autonomous weapons
proliferation
• Risk of intelligence divide
• Surveillance states
• Misinformation and
manipulation
Performance
• Risk of errors
• Risk of bias
• Risk of opaqueness
• Risk of performance
instability
Security
• Adversarial attacks
• Cyber intrusion &
Privacy risks
• Open source software
risks
Control/Governance
• Lack of human agency in AI
supported processes
• Inability to detect/control
rogue AI
• Unintended consequences
• Lack of clear accountability
and end to end governance
Application-level risks
AI Risk
Categories
Enterprise and National-level risks
How can I improve security and
robustness of AI?
How do detect and correct for
unfairness in my systems?
What can I do to improve human
understanding of the model decision
making?
How do I assess the ethical
implications of the development and
use of AI, and ensure alignment with
my organization’s values?
How can I track and check that AI
solutions operate in compliance with
relevant regulations?
How can I design effective AI
operating models and processes to
improve accountability and quality?

15. What is the RAI about?
No Silver Bullet Collection of tools, techniques
Not just about technology
It is about governance, people, process, tools,
techniques
Not just about AI ethics & code
of conduct
It is about how to contextualize and make it
relevant to the front line
Not entirely new
Leveraging existing governance, frameworks,
tools
Not about compliance after model
is built
Designing fairness, interpretability, security
etc., right from the start
What it is not? What it is?
15

16. Like traffic lights and crosswalks, RAI ensures a safe drive
through Innovation Avenue on your way to realize GenAI’s
potential
16
Lack of
Common
Sense and
Emotional
Intelligence
Originality
and
Personali-
zation
Security, moral
and ethical
concerns, bias
and stereotypes
Knowledge
Cut-Off
Dependency
on Input
Limited
Mathematical
Precision
Not suitable
for real-time
applications
Lack of
foresight
Hallucination

17. 2023
44%
Security Concerns
37%
Privacy Concerns
29%
Difficulty
developing own
models
29%
GenAI isn’t
trustworthy
enough
Barriers to Generative AI adoption
What are the top two barriers to using Generative AI in your organization?
n = 890, FERS Wave 6 Survey (IDC March 2023)

18. Think about it…
A bulldozer can be very strong
and destroy a building and
drive over any vehicle. But can
we say the same thing about a
bulldozer whose tank is empty?
The fuel
18

19. 2023
Future Possibilities
And Generative AI comes
after you?
19
The challenge is now that
GAI is here and it
performs tasks more
accurately and more
efficienly, what would be
the important skill sets to
assure we will stand a few
steps a head of the curve?

20. 2023
Future Possibilities
Shift from hard skills to soft skills has been amplified significantly.
20

21. 2023
Soft skills
Learn to know how you can leave a cooler impression behind!
21

22. 2023
The End!

23. 2023
Why was the generative AI always so tired?
Because it stayed up all night dreaming up new ways to take
over the world!
By ChatGPT 3.5
23

24. Thank you