AUTOMATION AND A.I. Technology & Sourcing Webinar - 8 September 2016 Giangicomo Olivi and Gareth Stokes - DLA Piper

 
AUTOMATION AND A.I. Technology & Sourcing Webinar - 8 September 2016 Giangicomo Olivi and Gareth Stokes - DLA Piper
AUTOMATION AND A.I.
                   Technology & Sourcing Webinar
                   Giangicomo Olivi and Gareth Stokes

                   8 September 2016

www.dlapiper.com                                        8 September 2016   0
AUTOMATION AND A.I. Technology & Sourcing Webinar - 8 September 2016 Giangicomo Olivi and Gareth Stokes - DLA Piper
Introductions

                   Giangiacomo Olivi                Gareth Stokes
                   Partner                          Partner
                   Technology & Sourcing Group      Technology & Sourcing Group
                   Italy                            UK
                   T: +39 02 80 618 515             T +44 (0)121 262 5831
                   M +39 335 53 26 994              M +44 (0)7968 559 210
                   giangiacomo.olivi@dlapiper.com   gareth.stokes@dlapiper.com

www.dlapiper.com                                                                  8 September 2016   1
AUTOMATION AND A.I. Technology & Sourcing Webinar - 8 September 2016 Giangicomo Olivi and Gareth Stokes - DLA Piper
Agenda

 Introduction
 Some definitions - Science fiction, or everyday fact?
 Catalyst
 Sector-specific examples
   – Business Processes
   – Manufacturing
   – FinTech
   – Consumer web
   – Digital Assistants
   – Smart Products
 Legal Considerations
   – Contracting
   – Data privacy and security
   – Discrimination
   – Public liability & ethics
 www.dlapiper.com                                         8 September 2016   2
AUTOMATION AND A.I. Technology & Sourcing Webinar - 8 September 2016 Giangicomo Olivi and Gareth Stokes - DLA Piper
Back to Asimov's Law…

1. A robot may not injure a human being or, through inaction, allow a human being to
   come to harm
2. A robot must obey the orders given to it by human beings except where such order
   would conflict with the First Law
3. A robot must protect its own existence as long as such protection does not conflict with
   the First or Second laws

www.dlapiper.com                                                             8 September 2016   3
AUTOMATION AND A.I. Technology & Sourcing Webinar - 8 September 2016 Giangicomo Olivi and Gareth Stokes - DLA Piper
Robot or not?

 No particularly widely accepted definition of "Intelligence", so "Artificial Intelligence" is
  even more problematic.
 Need to distinguish 'General AI' from specialist artificially intelligent systems

                   1996

                                                               2016

"Every time we figure out a piece of it, it stops being magical; we say, 'Oh, that's just a
computation.' "
                     Rodney Brooks, Director of MIT's Artificial Intelligence Laboratory, 2008

www.dlapiper.com                                                                   8 September 2016   4
AUTOMATION AND A.I. Technology & Sourcing Webinar - 8 September 2016 Giangicomo Olivi and Gareth Stokes - DLA Piper
Specialist AIs – Learning the hard(ware) way

    Most current specialist AI systems are distinguished by a learning component
    Often need to be 'trained' initially, or 'evolved'
    Positive feedback loops mean that the system improves over time, enhancing its
     ability to handle new data more accurately.

"Does this
picture have a
cat in it?"

                       Training
                                         Testing           Results of    Testing
    Initiation – no      with
                                         on new            testing fed   on new
          data          initial
                                          data            into system     data
                         data
                                                                                               82%
                                                                                               87%
                                                                                               90%
         50%                            63%                              74%                   93%

   www.dlapiper.com                                                                8 September 2016   5
AUTOMATION AND A.I. Technology & Sourcing Webinar - 8 September 2016 Giangicomo Olivi and Gareth Stokes - DLA Piper
If you can't define, rename…

 Exactly what constitutes AI can still be hotly debated
 Turing Test, proposed by mathematician Alan Turing in 1950

               A                                                                    B

 Other forms of special AI go by different names:

                                            Virtual digital assistant
                                 Cognitive systems Deep learning         Digital life
                   Voice recognition
                      Zero-UI systems   Machine learning Evolved algorithms
                                                                          Virtual neuron

              Autonomous systems Image recognition   Self-programming systems
                              Neural network         Whole-brain emulation

 Sufficiently tricky problem to have inspired a podcast:
    https://www.theincomparable.com/robot/

www.dlapiper.com                                                                           8 September 2016   6
AUTOMATION AND A.I. Technology & Sourcing Webinar - 8 September 2016 Giangicomo Olivi and Gareth Stokes - DLA Piper
Genuine concern for the future?

 The "singularity" – a point at which General AI rapids accelerates the rate of increase in
  its own intelligence, leading to Artificial Super-Intelligence (ASI)
 Reckoned by serious commentators to be somewhere between 20 and 80 years away

                                                                        Humans

                                                 Intelligence
                                                                       Computers

                                                                1950               2016
                                                                        Time

www.dlapiper.com                                                                   8 September 2016   7
AUTOMATION AND A.I. Technology & Sourcing Webinar - 8 September 2016 Giangicomo Olivi and Gareth Stokes - DLA Piper
Current specialist AI - Catalyst

  Specialist AIs solve specific problems. E.g.:
     – Voice recognition to text
     – Natural language processing
     – Image recognition
     – Other types of pattern recognition in datasets (fraud / credit scoring / diagnostics etc.)
  Allows the creation of algorithmic systems that would previously have required some
   human input – as a simple example:

                      Voice        Human typist            Text can be edited, searched,
Previously          recording       transcribes             manipulated in a computer

Brave new             Voice      AI speech-to-             Text can be edited, searched,
world               recording     text engine               manipulated in a computer

  Result – specialist AI enables, enhances and accelerates current systems

 www.dlapiper.com                                                                   8 September 2016   8
AUTOMATION AND A.I. Technology & Sourcing Webinar - 8 September 2016 Giangicomo Olivi and Gareth Stokes - DLA Piper
AI in use – Business Process (Outsourcing)

 Many business processes involve human operators using computer systems in
  reasonably predictable ways in response to a given task list:
            – Helpdesk
            – Claims processing
            – Application processing
            – Finance systems etc.
 Building compatible systems to work with legacy IT can be expensive.
 A machine learning AI can 'watch and learn' using hardware that records the signal sent
  to a human operator's VDU, and mouse/keyboard input.
 Over time, the AI's 'prediction' of what the human will do next to process a given task on
  the task list will approach 100% accuracy
 At a given prediction accuracy – 99% perhaps – the AI can take over tasks of that type

www.dlapiper.com                                                              8 September 2016   9
AI in use – Business Process (Outsourcing)
       Pre-AI operating model

   1

       AI training phase - human operator undertakes the work; AI to watch, learn, predict

   2

       AI able to take over relevant tasks at 99%+ prediction accuracy

   3

www.dlapiper.com                                                                             8 September 2016   10
AI in use – Business Process (Outsourcing)

 Phased reduction in headcount as tasks transition to AI provision
 Labour costs form less of Total Cost of Ownership for the system; hardware and
  software costs are the main components
 Labour arbitrage less of a commercial driver
 Re-locate services from off-shore provision to local provision
    – Geopolitical risk reduction / jurisdiction and tax complexity reduction
    – Easier supervision and audit
    – Manage confidentiality and data protection risks better
 AI becomes a potential single point of failure – the "all eggs in one basket" problem

www.dlapiper.com                                                                8 September 2016   11
AI in use - Manufacturing

 Industry 4.0 – Data driven manufacturing

                                                              Agile production lines
  IoT and sensors

                                                                    Customisation

Digital models
                                   Industry 4.0
                                                              AI / Cognitive systems
                   Data

             1.0 - Mechanisation     2.0 – Assembly lines   3.0 – Robotics
www.dlapiper.com                                                      8 September 2016   12
AI in use - Digital modelling

                   Sensors              Containers traditionally loaded on
                                         ships according to stated weight
                                        Weight often incorrect by +/-10% or
                                         more
                             Sensors    Ships therefore list and this leads
                                         to fuel inefficiency
                                        Adding sensors (IoT) to detect the
                                         real weight of containers / listing of
                                         the ship connected to an AI that
                                         plans the loading leads to greater
                                         flexibility and efficiency gains
                                        ~15% fuel savings

www.dlapiper.com                                                8 September 2016   13
AI in use - FinTech

                                                   7653 4535 5544 1234      7653 4535 5544 1234

                                                                                            
                   Automated Investment                      AI-powered fraud detection
                   & Wealth Management                           and credit checking

                                                         Transfer £100
                                                          to my Mum

          Mint          GoodBudget        Wally+

        Customer spend analysis and advice              'Zero UI' financial transactions

www.dlapiper.com                                                                   8 September 2016   14
AI in use – Consumer web

                    Trend analysis
                    Recommendations
                    "Customers also bought"
                    Related searches / related images / related videos

www.dlapiper.com                                                          8 September 2016   15
AI in use – Digital Assistants

www.dlapiper.com                 8 September 2016   16
AI in use – Smart products

                                                               Weather
                                                             service data

                                                  Internet
    GPS-enabled
    smartphone
       app
                                            Internet-
                                           connected
                                           thermostat
                    Temperature
                      sensor

                                                                     A.I. based
                                                                     processing
                                                                      on server
          Heating                 Aircon

www.dlapiper.com                                                  8 September 2016   17
Legal considerations - Contracting

 Current contracting models assume failure modes based on human error
    – Service level models incentivise suppliers to avoid 'low grade' issues that might arise
      if staff don't follow proper processes
    – Liability limits and exclusions
    – Confidentiality, data protection, security and audit provisions all assume human
      fallibility can be avoided by oversight
 AI delivered services have different failure modes, and contracts need to
  reflect this
    – Lower probability of 'low grade' issues that SLAs could correct
    – Greater risks associated with 'catastrophic failure' = higher liability cap?
    – Ownership of the 'trained' AI? Risk of 'pollution' of the AI with bad data?
    – Oversight of how the AI 'mind' works is more difficult

www.dlapiper.com                                                                     8 September 2016   18
Legal considerations – Contracting & HR
       Normal transfer-in / transfer out TUPE model for outsourced services
       Customer    Customer's                                                    Customer's (or replacement
   1               employees                                                       supplier's) employees
       Supplier
                                       Supplier's employees

                         Service start date                           Service end date

       AI-based service provision – transfer in, gradual redundancy
       Customer
                   Customer's                                                   What IP / knowledge does the
                   employees                                                           customer get?
   2
       Supplier
                                       Supplier's employees

                         Service start date                           Service end date

 Labour arbitrage commercial justifications diminished
 Cost of redundancy spread throughout the term – how is that managed?
 "Corporate memory" is held by staff (Exit management / TUPE etc.)
 No staff = no TUPE on exit, but how is exit knowledge transfer managed?

www.dlapiper.com                                                                            8 September 2016   19
Legal considerations – Data privacy and security

 AI-based systems especially useful in data-rich environments
    – Rapid processing of large volumes of data
    – Pattern matching in 'Big Data' datasets
    – Data from new sources – IoT sensors etc.
    – New data types – photo/video/social network feeds
 Much of this is 'personal data' within the meaning of Directive 95/46/EC
 Possible to derive / generate additional data via processing
 Geolocation data, photos, social media – where you are, what you do, who you know,
  what you like and dislike, what you think?
 Tightening legal framework – General Data Protection Regulation
 Compliance mindset – design in compliance from the start
 AI represents a huge concentration of data – hacking target

www.dlapiper.com                                                             8 September 2016   20
Legal considerations - Discrimination

 EU Charter of Fundamental Rights
 Equality Act 2010 protected characteristics:
    – age
    – disability
    – gender reassignment
    – marriage or civil partnership
    – race
    – religion or belief
    – sex
    – sexual orientation
 Need to consider indirect discrimination as well – in many cities addresses in particular
  areas, postcodes or streets may effectively 'encode' for race, religion etc. Certain job
  types tend to 'encode' for gender etc.

www.dlapiper.com                                                              8 September 2016   21
Legal considerations – Liability and ethics
                                                                   Who is liable for the acts of an AI?
                                                                    The owner/operator? The vendor?
                                                                    The manufacturer?
                                                                   On what basis is liability to be
                                                                    decided? Vicarious? Strict?

                                       Switch                      Applying old cases to new problems.
                                                                    Is case law about liability for a bolting
                                                                    horse applicable to a haywire self-
                                                                    driving car?
                                                                   Directive 85/374/EEC on liability for
                                                                    defective products, covering
        A                                                           damages caused by a robot's
                                                                    manufacturing defects / Strict liability
                     B
                                                                   As AIs become more sophisticated,
The Trolley Problem – In its classic formulation, a trolley is      how do they weigh and 'value' one
rolling out of control down hill toward five people on the line     person against another? In extremis,
(A). If it hits them they will be killed. You can switch the        could your social media profile decide
trolley onto a second line where only one person would be
                                                                    whether your car 'saves' you?
killed (B). Is switching the track the correct action or not?

  www.dlapiper.com                                                                         8 September 2016   22
New regulations?

 European Parliament - Draft report with recommendations to the Commission on
  Civil Law rules on Robotics - 31 May 2016
 Definition of classification of "smart robots"
    – Interconnectivity and data analysis / learning through experience
    – Physical support / adapting to environment
 Registration of "smart robots"
 Civil law liability
    – Possible restrictions only for damages to property
    – "Strict liability" rule
    – Compulsory insurance ("producer")
 Interoperability and harmonisation
 Disclosure of use of robots and artificial intelligence by undertakings

www.dlapiper.com                                                            8 September 2016   23
New regulations?

 "Charter on robotics" - ethical code to foster responsible innovation
    – Main principles ("Beneficence" and "Non-maleficence", etc.) / Fundamental rights
      Precautions / Inclusiveness / Accountability / Safety / Reversibility / Privacy / Harm
      minimisation
    – Research Ethics Committee (REC)
    – Licence for designers
    – Licence for users
 Compensation fund
 New legal status for robots? - "electronic personality" when they interact autonomously
  with third parties / make autonomous decisions

www.dlapiper.com                                                                8 September 2016   24
Any questions?

                   Giangiacomo Olivi                Gareth Stokes
                   Partner                          Partner
                   Technology & Sourcing Group      Technology & Sourcing Group
                   Italy                            UK
                   T: +39 02 80 618 515             T +44 (0)121 262 5831
                   M +39 335 53 26 994              M +44 (0)7968 559 210
                   giangiacomo.olivi@dlapiper.com   gareth.stokes@dlapiper.com

www.dlapiper.com                                                                  8 September 2016   25
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