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

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

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Quali sono le potenzialità di business dell’intelligenza artificiale, quale è il potenziale impatto del regolamento sull’IA sulle stesse e quali sono le questioni legali ed etiche che rimangono irrisolte dopo il regolamento? Ne abbiamo discusso in webinar organizzato da AIGI con lo studio legale DLA Piper. La presentazione qui pubblicata è stata realizzata da Pietro Scarpino – VP, Head of IoT, VR & AI Service Line di NTT Data e
Giulio Coraggio – Location Head of Italian Intellectual Property and Technology Department di DLA Piper

Quali sono le potenzialità di business dell’intelligenza artificiale, quale è il potenziale impatto del regolamento sull’IA sulle stesse e quali sono le questioni legali ed etiche che rimangono irrisolte dopo il regolamento? Ne abbiamo discusso in webinar organizzato da AIGI con lo studio legale DLA Piper. La presentazione qui pubblicata è stata realizzata da Pietro Scarpino – VP, Head of IoT, VR & AI Service Line di NTT Data e
Giulio Coraggio – Location Head of Italian Intellectual Property and Technology Department di DLA Piper

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

  1. 1. 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
  2. 2. © 2019 NTT DATA Italia S.p.A. 2 How much data do we create in one single day?
  3. 3. © 2019 NTT DATA Italia S.p.A. 3 A man with a cheap mobile phone has a much faster access to more information than Reagan had during his entire mandate.
  4. 4. © 2019 NTT DATA Italia S.p.A. 4 Oracular Society Data has a better idea
  5. 5. © 2019 NTT DATA Italia S.p.A. 5 Near time Marketing Shipping than Shopping Anticipatory Design
  6. 6. © 2019 NTT DATA Italia S.p.A. 6 Looking for an hyperspecialized experience
  7. 7. © 2019 NTT DATA Italia S.p.A. 7 Society 5.0 Super Smart Society
  8. 8. © 2019 NTT DATA Italia S.p.A. 8 Transparent technology
  9. 9. © 2019 NTT DATA Italia S.p.A. 9 Smile to pay
  10. 10. © 2019 NTT DATA Italia S.p.A. 10 Talk to pay
  11. 11. © 2019 NTT DATA Italia S.p.A. 11 Grab & Go
  12. 12. ©2018 NTT DATA NDDN 1
  13. 13. © 2019 NTT DATA Italia S.p.A. 13 How does it work? Let’s change template…
  14. 14. © 2019 NTT DATA Italia S.p.A. 14 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
  15. 15. © 2019 NTT DATA Italia S.p.A. 15 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…
  16. 16. © 2019 NTT DATA Italia S.p.A. 16 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
  17. 17. © 2019 NTT DATA Italia S.p.A. 17 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
  18. 18. © 2019 NTT DATA Italia S.p.A. 18 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. Information Type: Working Standard, Disclosure Range: , Information Owner: virgil.ilian, NTT DATA Romania
  19. 19. © 2019 NTT DATA Italia S.p.A. 19 eXplainable AI (XAI) – Social Right to Explaination Information Type: Working Standard, Disclosure Range: , Information Owner: virgil.ilian, NTT DATA Romania 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
  20. 20. © 2019 NTT DATA Italia S.p.A. 20 eXplainable AI(XAI) – Social Right to Explaination Information Type: Working Standard, Disclosure Range: , Information Owner: virgil.ilian, NTT DATA Romania 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. 21. 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.
  22. 22. 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?
  23. 23. © 2019 NTT DATA Italia S.p.A. 23 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. 24. © 2019 NTT DATA Italia S.p.A. 24 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. 25. © 2019 NTT DATA Italia S.p.A. 25 25 What’s the regime applicable to the AI system?
  26. 26. 26 © 2020 NTT DATA Italia S.p.A. © 2021 NTT DATA Italia S.p.A. 26 Natural Language Processing
  27. 27. 27 © 2021 NTT DATA Italia S.p.A. 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. 28. 28 © 2020 NTT DATA Italia S.p.A. © 2021 NTT DATA Italia S.p.A. 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. 29. 29 © 2020 NTT DATA Italia S.p.A. © 2021 NTT DATA Italia S.p.A. 29 Intelligent Data Processing
  30. 30. 30 © 2021 NTT DATA Italia S.p.A. 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. 31. 31 © 2021 NTT DATA Italia S.p.A. 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. 32. 32 © 2021 NTT DATA Italia S.p.A. 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. 33. 33 © 2020 NTT DATA Italia S.p.A. © 2021 NTT DATA Italia S.p.A. 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. 34. 34 © 2020 NTT DATA Italia S.p.A. © 2021 NTT DATA Italia S.p.A. 34 Intelligent Vision
  35. 35. 35 © 2021 NTT DATA Italia S.p.A. 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. 36. 36 © 2021 NTT DATA Italia S.p.A. 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. 37. 37 © 2020 NTT DATA Italia S.p.A. © 2021 NTT DATA Italia S.p.A. 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. 38. 38 © 2021 NTT DATA Italia S.p.A. Video
  39. 39. 39 © 2021 NTT DATA Italia S.p.A. DeepFake Example
  40. 40. 40 © 2020 NTT DATA Italia S.p.A. © 2021 NTT DATA Italia S.p.A. 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
  41. 41. 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. 42. 42 © 2021 NTT DATA Italia S.p.A. 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
  43. 43. Unsolved issues Criticalities
  44. 44. © 2019 NTT DATA Italia S.p.A. 44 Thank you Pietro Scarpino @piscarpi pietro.scarpino@nttdata.com Giulio Coraggio @giulio.coraggio giulio.coraggio@dlapiper.com

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