Intelligenza artificiale: le sue potenzialità, la bozza di regolamento UE e rischi legali

Le potenzialità di business dell’intelligenza artificiale e come
sono impattate dalla bozza di Regolamento europeo sull’AI
Milan, 20th October 2021
Pietro Scarpino - VP, Head of IoT, VR & AI Service Line, NTT Data
Giulio Coraggio, Location Head, Intellectual Property & Technology Department, DLA Piper Italy

© 2019 NTT DATA Italia S.p.A. 2
How much data do we create
in one single day?

A man with a cheap
mobile phone has a
much faster access to
more information than
Reagan had during his
entire mandate.

Oracular
Society
Data has a better idea

Near time Marketing
Shipping than Shopping
Anticipatory Design

Looking for an
hyperspecialized
experience

Society 5.0
Super Smart Society

Transparent technology

Smile to pay

Talk to
pay

Grab & Go

How does it work?
Let’s change template…

What is AI
AI is the ability of a computer or machine to emulate human tasks through learning and
automation, generally understood to be the simulation of the higher order functions of
intelligent beings in areas such as visual processing, speech processing and analytics.
Speech
Vision
NLP
(Natural Language Processing)
Robotics
- Text to Speech
- Speech to text
- Computer Vision
- AI OCR
Others
- Translation
- Classification & Clustering
- Expert System
- Chatbot
AI
- Perception & Navigation
- Mission planning

Limits of Artificial Intelligence
Goal:
Replace a human brain
This is not what we have today
Strong AI
Goal:
Narrow and limited intelligence which takes over vertical tasks
from a human brain.
Computers solve problem by detecting useful patterns
Pattern-based AI is an Extremely powerful tool
This is the dominant mode of AI today
Weak AI
J.A.R.V.I.S. (Iron Man)
Skynet(Terminator)
Computer Vison
AI OCR
Translation
TTS
STT
Etc…

AI and ML Learning pills
Supervised
Learning
Unsupervised
Learning
Semi-supervised
Learning
Reinforced
Learning
ML is part of a sub-field of Artificial Intelligence and aims to make the machines automatically learn activities
performed by us human beings.
Classification
Regression
Clustering
Partial Labeling
Self Learning

Machine Learning Process
Data
Acquisition
Data Cleaning Train Dataset
Test Dataset
Hold out Test
Dataset
Train ML
Model
Test Model
Evaluate
Model
Deploy Model
Adjust Model Parameters
Most time spent here
Note that the machine learning process is a very iterative one. Findings
from one step may require a previous step to be repeated with new
information.
Information Type: Working Standard, Disclosure Range: , Information Owner: virgil.ilian, NTT DATA Romania

Importance of machine learning datasets
The accuracy of machine learning depends on
the quantity and quality of the data set.
Data annotation
Data Annotation work is sometimes a team
work and requires a lot of effort.
Garbage In, Garbage Out
A rule stating that the quality of the output is a
function of the quality of the input; put garbage
in and you get garbage out.

eXplainable AI (XAI) – Social Right to Explaination
Explainable AI (XAI) is artificial intelligence (AI) in which the results of the solution can be understood by humans
XAI is relevant even if there is no
legal right or regulatory
requirement
This way the aim of XAI is to explain
what has been done, what is done
right now, what will be done next
and unveil the information the
actions are based on

eXplainable AI(XAI) – Social Right to Explaination
If algorithms meet these requirements, they provide a basis for justifying decisions, tracking and thereby
verifying them, improving the algorithms, and exploring new facts.
XAI algorithms are considered to follow the three principles transparency, interpretability and explainability.
Transparency
The processes that extract
model parameters from
training data and generate
labels from testing data
can be described and
motivated by the
approach designer
Interpretability
The possibility to
comprehend the ML
model and to present the
underlying basis for
decision-making in a way
that is understandable to
humans.
Explainability
The collection of
interpretations that give the
possibility to explain a choice.
The features of the
interpretable domain, that
have contributed for a given
example to produce a
decision

21
© 2021 NTT DATA Italia S.p.A.
As AI implementation escalates, a number of unintended consequences arise with potential
negative impact on organizations, end-users and societies.
AI ethics addresses these challenges by preventing AI risks and building trust across systems
which requires organizations to take up new considerations:
What’s the Risk of my AI Strategy?
Responsible AI
Governance
MLOps
Data Privacy
Augmented Intelligence
Fairness &
Explainability
Agile and secure scaling of AI across the organization led by strong
AI governance.
Continuously deliver business value through AI by ensuring models
accuracy technically and functionally.
Prevent exposure of sensitive data through enhanced control
mechanisms.
Generating AI systems that leverage human + machine collaboration
to augment decision-making.
Implement tools and processes to assess fairness and explainability
through out the AI lifecycle.

A risk-based approach to AI by
the draft EU Regulation on AI
Am I using an AI
system?
Am I carrying out
a relevant
activity?
Is it a prohibited AI
practice?
Is it a high risk AI
system?
Is it an AI system
posing a
manipulation risk?

Software
Developed with either machine learning
approaches, or logic learning and knowledge
based approaches or statistical approaches
Which can, for a given set of
human-defined objectives,
generate outputs
What’s AI system for the draft EU Regulation on AI?

24
What’s a relevant activity for the draft EU
Regulation on AI?
A Provider established
inside or outside the
EU
• Placing into the EU
market
Putting into service in
the EU market
A User located within
the EU
• Using an AI Systemin
the EU outside of a
personal or non-
professional activity
Provider or User of AI
Systems that is located
outside the EU
• where the output
produced by the AI
System is used within
the EU

25
What’s the regime applicable to the AI system?

26
Natural Language Processing

27
Natural Language Processing Example
Contact Center Connector
Industry: Telco
Need: single contact point for customers
served by AI platforms and operators.
Solution: allow the activation of different
digital channels on Digital Contact
Center as chat, email, WhatsApp, voice,
smart speaker.
Outcome: 24 hours automatic support;
different digital channels; full tracing of
all user requests.
Automatic Document
Classification
Industry: Energy
Need: Have a document base always
classified, with an intelligent search
functions, easy to use and in natural
language
Solution: A system able to classify every
document in different format on the
basis of intelligent algorithms.
Outcome: Intelligent classification
system with a real-time smart search
available also in NLP.
Intelligent E-mail
Processing
Industry: Financial Services
Need: improve the management of
requests arriving via e-mail to the bank's
Customer Care.
Solution: able to understand and
evaluate the content of requests sent by
e-mail from customers, and provide
them with support by responding
autonomously.
Outcome: improvement of customer
service; reduction of costs.

28
Would the AI
system pose an
unacceptable risk?
• No, it is not a social scoring, manipulation or
facial recognition AI system
Would the AI
system pose a high
risks?
• No, the outputs of the AI system are not used
for employment, education and justice
Would the AI
system pose a
limited risk?
• It might if the technology is equipped with an
emotion recognition technology

29
Intelligent Data Processing

30
Intelligent Data Processing Tasks
Predictive Maintenance
Companies are collecting huge amounts of data to monitor the health of
their plants, systems and machines. ML proved effective for recognizing
in advance operating drifts and possible failures. This is very important
in order not to be caught unprepared by unexpected machine
downtime or quality problems in the finished product.
Fraud Detection
ML algorithms can be used to recognize fraud patterns and reduce
fraudulent claims. By analyzing the claims based on set rules and
indicators, ML can identify which may not be legitimate. These indicators
can include frequency of claims, past behavior and credit score.
Intelligent Automation
Most labor-intensive and repetitive back-office tasks can be automated
using ML. Intelligent automation represents the most mature area of ML
application for insurances and banks.
Product Recommendations
ML can be used to improve products recommendation to customers, thus
being more responsive to their specific needs. The use of ML can also
support the definition of product pricing based on individual needs and
lifestyle. This capability increases the potential client base and the
satisfaction of existing customers.
Demand Forecasting
Demand forecasting is pivotal for many industries, but it can be one of
the most challenging tasks. Companies often struggle to accurately fully
consider core business constraints and real-time trends. ML addresses
those challenges by using different data sources and providing accurate
forecasts.
Customer Churn Prediction
ML makes it possible to identify which customer are likely to churn by
analyzing customers’ behavior. This allows companies to take
appropriate actions and improving the companies’ ability to retain their
customers and avoid financial losses.

31
Federated Machine Learning, collaborative AI at scale
Federated Machine Learning
Federated Machine Learning is a key
technology to enable a healthy and
non-competitive collaboration among
multiple organizations. It allows the
application of collaborative AI at scale
with no fear of sharing private data,
also leading to the creation of new
business models.
Challenges and sharing data
Everyday organizations face challenges they would better
face together. However sharing data is not possible due to
privacy and security constraints.

32
Intelligent Data Processing Examples
Product Oversight
Governance
Industry: Banking
Need: meet the needs arising from
regulations issued by the Bank of Italy.
Solution: suggest the most appropriate
products with respect to the
characteristics of the customer and the
legislation.
Outcome: adequacy of the products
offered and sold to customers on the
basis of their characteristics in
accordance with the legislation.
AI for Internal Audit and
AML
Industry: Banking
Need: support Internal Audit service on
third-level control of the bank's
operations.
Solution: monitor aggregate transactions
data in order to highlight situations that
represent anomalies, such as situations
connected to money laundering.
Outcome: more effectiveness and
efficiency in the selection phase of
anomalous entities.
Smart Water Management
System
Industry: Energy & Utilities
Need: innovative IoT & AI platform
aimed at protecting and enhancing the
water resource throughout the whole
water cycle.
Solution: AI module to detect
anomalous patterns related to
malfunctioning in the water distribution
system.
Outcome: optimization of the water
supply; improvement of the planning of
maintenance intervention.

33
Would the AI
system pose an
unacceptable risk?
• No, but some advanced technologies are able to
manipulate customers’ behaviors and in such a case
would be prohibited
Would the AI
system pose a high
risks?
• No, the outputs of the AI system are not used for
employment, education and justice
Would the AI
system pose a
limited risk?
• It might if the technology is able to adjust the responses
based on customers’ emotions

34
Intelligent Vision

35
Computer Vision Tasks
Face Recognition
Face Recognition allows to detect faces inside images or video frames
and eventually identify a detected person by comparing facial features
extracted from the given image with the registered faces. This task is
widely applicable in several context as smart access or public
surveillance.
Emotion Recognition
Emotion Recognition allows identifying human emotions analyzing facial
expressions. It leverages facial attributes and combine them to assess
whether the person appear happy, sad, angry or even distracted.
It finds a wide range of applications as the customers satisfaction
assessment or the drivers monitoring.
Action Recognition
Action Recognition is a challenging Computer Vision task which aims at
recognizing human pose from images and classifying them.
It has many applications in video surveillance, customer tracking in retail
and healthcare.
Image Classification
Image Classification allows classifying images based on their visual
content, assigning a label from a fixed set of categories to an input image
with a probability score. It is one of the core problems in Computer Vision
and, despite its simplicity, has a wide range of practical applications.
Object Detection
Object detection is the task of image classification on single objects in the
image. An image may contain multiple objects that require localization
(bounding boxes) and classification. It is more challenging than the
simple image classification, as often there are multiple objects of
different types.
Optical Character Recognition (OCR)
Optical Character Recognition in a key task when retrieving information
from digital documents. It allows converting images of typed,
handwritten or printed text into machine-encoded text. OCRs is widely
used as a form of data entry from printed paper data and is a common
method of digitizing printed texts so that they can be electronically
edited.

36
Intelligent Vision Examples
Utility Bill
Processing
Industry: Energy & Utilities
Need: provide the best offer to a new
potential customer based on utility bill
uploaded on its website.
Solution: read the utility bill uploaded by
the customer and extract relevant
information in order to recommend the
best offer and streamline procedures for
contract activation.
Outcome: improvement of the user
experience; automatic information
extraction.
Automatic Car
Claims Estimation
Industry: Insurance
Need: identification of external car
damages and estimation of components
costs.
Solution: modern approach that use AI
algorithms able to recognize visible car
damages and estimate the repairing
costs.
Outcome: speeding up the claim
process; intuitive solution and user
interface; reduced expenses.
Satellite Crop Recognition
Industry: Public
Need: preventing fraud of European
funds for agriculture by verifying crop
fields.
Solution: analyze and classify satellite
imagery according the crop type
appearing.
Outcome: speeding up the verification
process and increasing the portion of
the fields verified.

37
Would the AI
system pose an
unacceptable risk?
• No, unless there are technologies able to manipulate
customers’ behaviors and facial recognition is used for
monitoring purposes
Would the AI
system pose a high
risks?
• No, the outputs of the AI system are not used for
employment, education and justice
Would the AI
system pose a
limited risk?
• It might if the technology is able to adjust the output based
on the customers’ behavior

38
Video

39
DeepFake Example

40
Would the AI
system pose an
unacceptable risk?
• No
Would the AI
system pose a high
risks?
• No, the outputs of the AI system are not used
for employment, education and justice
Would the AI
system pose a
limited risk?
• Yes, individuals shall be informed about the
deep fake

Fines of up to € 10/30 million (depending
on the nature of the violation), or (if
higher) a turnover-based penalty of up to
2% - 6% of the previous financial year's
annual global turnover, will apply
Fines
Unlike the GDPR, enforcement of the AI
Regulation is the responsibility of the
supervisory authorities and there is
neither a claim system nor specific rights
to be exercised directly by individuals
Rights

42
What do we understand by ethical AI?
AI should be:
Lawful: respecting all applicable laws and regulations
Ethical: respecting ethical principles and values
Robust: both from a technical perspective while
taking into account its social environment
The Guidelines put forward a set of 7 key
requirements:
• Human agency and oversight
• Technical Robustness and safety
• Privacy and data governance
• Transparency
• Diversity, non-discrimination and fairness
• Societal and environmental well-being
• Accountability

Thank you
Pietro Scarpino
@piscarpi
pietro.scarpino@nttdata.com
Giulio Coraggio
@giulio.coraggio
giulio.coraggio@dlapiper.com

Intelligenza artificiale: le sue potenzialità, la bozza di regolamento UE e rischi legali

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