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Artificial Intelligence (AI),
the liberal professions
and the law
with a special focus on the medical profession
Professor Dr. Karl Stöger, MJur (Oxford)
Institute of Public Law and Political Science
University of Graz
Day of the liberal professions 2019
• "free" from the state, but also from third parties
• Performing an intellectual task
• due to special competence
• personally (predominantly in a special relationship of
trust)
• self-reliant
• and professionally independent.
Definition of "liberal" professions
• "free" from the state, but also from third parties
• Performing an intellectual task
• due to special competence
• personally (predominantly in a special relationship of
trust)
• self-reliant
• and professionally independent.
Definition of "liberal" professions
Differentiation of AI, ML und DL
(picture source: Singh, Cousins of AI <https://towardsdatascience.com/cousins-of-artificial-
intelligence-dda4edc27b55>)
McCarthy: „[…]
every aspect of
learning or any
other feature of
intelligence can
in principle be so
precisely
described that a
machine can be
made to
simulate it.“
McCarthy: „[…]
Jeder Aspekt des
Lernens oder jedes
andere Merkmal der
Intelligenz kann im
Prinzip so genau
beschrieben werden,
dass eine Maschine
zur Simulation
eingesetzt werden
kann.“
Translated by DeepL
• Recognition of patterns/rules
• in a large amount of data
• Transfer of these rules to
• unknown situations
 ML works with statistical tools
 a form of pattern recognition
 correlation instead of causality
Core aspects of Machine Learning (ML)
• Image evaluation as a leading field of application for ML
• Decision support as the most promising market
State of development - medicine
Lymph node biopsy, identification
of a tumor (marked green) by
means of AI
(picture source: Google AI
<https://ai.google/healthcare/>)
• "Standard applications" for trainees will become less
frequent (e.g. data checking)  reduction of training
positions?
• (Expenditure-based) fee calculation will have to
adapt - but there will be new costs for AI infrastructure
• Partial replacement and further delegation of tasks:
e.g. health apps, contract preparation on the Internet
• Potential dependency on a few providers
AI changes the job profiles of the liberal
professions
Who can regulate the use of AI?
• European law: if there is a reference to the internal market
and thus a need for legal harmonisation: e.g. differences
between national AI regulations make cross-border activities
more burdensome
• International law (e.g. "European Ethical Charter on the
use of AI in judicial systems and their environment" of the
Council of Europe)
• National law
• Professional codes - self-regulation as a "privilege" of the
liberal professions
• No comprehensive codification, only single provisions
(e.g. Art 22 GDPR – automated individual decision-
making, including profiling)
• Awareness: given, implementation: work in progress
 High-Level Expert Group on AI: AI Definition, Ethics
Guidelines (currently in practical pilot phase until early
2020), Policy and Investment Recommendations
 Announcement von der Leyen: legislative proposals
within 100 days
 No specific rules for the liberal professions
Where does the European law stand?
High-Level Expert Group on AI: Ethics Guidelines
4 ethical principles: 7 core requirements:
1. Respect for human autonomy
2. Prevention of harm
3. Fairness
4. Explicability
(source: https://ec.europa.eu/futurium/en/ai-
alliance-consultation/guidelines)
1. Human agency and oversight
2. Technical robustness and safety
3. Privacy and Data Governance
4. Transparency
5. Diversity, non-discrimination and
fairness
6. Societal and environmental
wellbeing
7. Accountability
Responsibility for the consequences of innovation:
• The state guarantees protection from negative effects of
technological innovation
• Principle of non-discrimination – Attention: correlation
instead of causality
Freedom of innovation: Securing the freedom for technical
development - Freedom to conduct business, right to (intellectual)
property
Necessary standard of medical treatments: Obligation to use
AI? (e.g. ECHR 30.8.2016, 40448/06 Aydoğdu/Turkey: functioning
hospital system)
AI and human rights:
Charter of Fundamental Rights, ECHR, constitutions
Quality of the database of AI-applications:
European Union Agency for Fundamental Rights
Source: FRA, Data quality and artificial intelligence – mitigating bias and error to
protect fundamental rights (2019)
• Explainable AI is an important topic, in particular for medical
professionals in the area of informed consent
• Justification of decisions is particularly essential for members
of the liberal professions as they work as „advisors and
companions“
Blueprint of a black box in systems theory
(picture source: Krauss, Blackbox3D <https://de.wikipedia.org/wiki/Black_Box_
(Systemtheorie)#/media/Datei:Blackbox3D.png> CC BY-SA 4.0)
„Personal intellectual care“ as a core element
of all liberal professions  Mutual trust
(picture source: Elliott, AI Cartoons
<https://timoelliott.com/blog/cartoons/artificial-intelligence-cartoons>)
• Protection of privacy of patients
• “[…] Black-box medicine — the use of big data and sophisticated
machine-learning techniques for health-care applications — could be
the future of personalized medicine. Black-box medicine promises to
make it easier to diagnose rare diseases and conditions, identify the
most promising treatments, and allocate scarce resources among
different patients. But to succeed, it must overcome two separate, but
related, problems: patient privacy and algorithmic accountability. […]”
Source: Ford/Price, Privacy and Accountability in Black-Box Medicine, Michigan
Telecommunications and Technology Law Review 2016, 1 (1)
Personal and trustful provision of services
(I)
• “[…] Privacy is a problem because researchers need access to
huge amounts of patient health information to generate useful
medical predictions. And accountability is a problem because
black-box algorithms must be verified by outsiders to ensure
they are accurate and unbiased, but this means giving outsiders
access to this health information. […]”
Source: Ford/Price, Privacy and Accountability in Black-Box Medicine, Michigan
Telecommunications and Technology Law Review 2016, 1 (1)
Personal and trustful provision of services
(II)
• Duty to declare potential conflicts of interest (duty of
loyalty)
•  Client/patient can search for alternatives
However:
• Financial and not altruistic motives shape the
development of AI: „hunger” for more data
• To renounce AI-support is not a viable alternative
because AI is to useful (e.g. diagnostic accuracy)
• Dominance of few providers prevents realistic alternatives
Professional independence: conflicts of
interest
Thank you for
your attention!
Professor Dr. Karl Stöger, MJur (Oxford)
Institute of Public Law and Political Science
University of Graz

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presentation_karl_stoger.pptx

  • 1. Artificial Intelligence (AI), the liberal professions and the law with a special focus on the medical profession Professor Dr. Karl Stöger, MJur (Oxford) Institute of Public Law and Political Science University of Graz Day of the liberal professions 2019
  • 2. • "free" from the state, but also from third parties • Performing an intellectual task • due to special competence • personally (predominantly in a special relationship of trust) • self-reliant • and professionally independent. Definition of "liberal" professions
  • 3. • "free" from the state, but also from third parties • Performing an intellectual task • due to special competence • personally (predominantly in a special relationship of trust) • self-reliant • and professionally independent. Definition of "liberal" professions
  • 4. Differentiation of AI, ML und DL (picture source: Singh, Cousins of AI <https://towardsdatascience.com/cousins-of-artificial- intelligence-dda4edc27b55>) McCarthy: „[…] every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it.“ McCarthy: „[…] Jeder Aspekt des Lernens oder jedes andere Merkmal der Intelligenz kann im Prinzip so genau beschrieben werden, dass eine Maschine zur Simulation eingesetzt werden kann.“ Translated by DeepL
  • 5. • Recognition of patterns/rules • in a large amount of data • Transfer of these rules to • unknown situations  ML works with statistical tools  a form of pattern recognition  correlation instead of causality Core aspects of Machine Learning (ML)
  • 6. • Image evaluation as a leading field of application for ML • Decision support as the most promising market State of development - medicine Lymph node biopsy, identification of a tumor (marked green) by means of AI (picture source: Google AI <https://ai.google/healthcare/>)
  • 7. • "Standard applications" for trainees will become less frequent (e.g. data checking)  reduction of training positions? • (Expenditure-based) fee calculation will have to adapt - but there will be new costs for AI infrastructure • Partial replacement and further delegation of tasks: e.g. health apps, contract preparation on the Internet • Potential dependency on a few providers AI changes the job profiles of the liberal professions
  • 8. Who can regulate the use of AI? • European law: if there is a reference to the internal market and thus a need for legal harmonisation: e.g. differences between national AI regulations make cross-border activities more burdensome • International law (e.g. "European Ethical Charter on the use of AI in judicial systems and their environment" of the Council of Europe) • National law • Professional codes - self-regulation as a "privilege" of the liberal professions
  • 9. • No comprehensive codification, only single provisions (e.g. Art 22 GDPR – automated individual decision- making, including profiling) • Awareness: given, implementation: work in progress  High-Level Expert Group on AI: AI Definition, Ethics Guidelines (currently in practical pilot phase until early 2020), Policy and Investment Recommendations  Announcement von der Leyen: legislative proposals within 100 days  No specific rules for the liberal professions Where does the European law stand?
  • 10. High-Level Expert Group on AI: Ethics Guidelines 4 ethical principles: 7 core requirements: 1. Respect for human autonomy 2. Prevention of harm 3. Fairness 4. Explicability (source: https://ec.europa.eu/futurium/en/ai- alliance-consultation/guidelines) 1. Human agency and oversight 2. Technical robustness and safety 3. Privacy and Data Governance 4. Transparency 5. Diversity, non-discrimination and fairness 6. Societal and environmental wellbeing 7. Accountability
  • 11. Responsibility for the consequences of innovation: • The state guarantees protection from negative effects of technological innovation • Principle of non-discrimination – Attention: correlation instead of causality Freedom of innovation: Securing the freedom for technical development - Freedom to conduct business, right to (intellectual) property Necessary standard of medical treatments: Obligation to use AI? (e.g. ECHR 30.8.2016, 40448/06 Aydoğdu/Turkey: functioning hospital system) AI and human rights: Charter of Fundamental Rights, ECHR, constitutions
  • 12. Quality of the database of AI-applications: European Union Agency for Fundamental Rights Source: FRA, Data quality and artificial intelligence – mitigating bias and error to protect fundamental rights (2019)
  • 13. • Explainable AI is an important topic, in particular for medical professionals in the area of informed consent • Justification of decisions is particularly essential for members of the liberal professions as they work as „advisors and companions“ Blueprint of a black box in systems theory (picture source: Krauss, Blackbox3D <https://de.wikipedia.org/wiki/Black_Box_ (Systemtheorie)#/media/Datei:Blackbox3D.png> CC BY-SA 4.0) „Personal intellectual care“ as a core element of all liberal professions  Mutual trust
  • 14. (picture source: Elliott, AI Cartoons <https://timoelliott.com/blog/cartoons/artificial-intelligence-cartoons>)
  • 15. • Protection of privacy of patients • “[…] Black-box medicine — the use of big data and sophisticated machine-learning techniques for health-care applications — could be the future of personalized medicine. Black-box medicine promises to make it easier to diagnose rare diseases and conditions, identify the most promising treatments, and allocate scarce resources among different patients. But to succeed, it must overcome two separate, but related, problems: patient privacy and algorithmic accountability. […]” Source: Ford/Price, Privacy and Accountability in Black-Box Medicine, Michigan Telecommunications and Technology Law Review 2016, 1 (1) Personal and trustful provision of services (I)
  • 16. • “[…] Privacy is a problem because researchers need access to huge amounts of patient health information to generate useful medical predictions. And accountability is a problem because black-box algorithms must be verified by outsiders to ensure they are accurate and unbiased, but this means giving outsiders access to this health information. […]” Source: Ford/Price, Privacy and Accountability in Black-Box Medicine, Michigan Telecommunications and Technology Law Review 2016, 1 (1) Personal and trustful provision of services (II)
  • 17. • Duty to declare potential conflicts of interest (duty of loyalty) •  Client/patient can search for alternatives However: • Financial and not altruistic motives shape the development of AI: „hunger” for more data • To renounce AI-support is not a viable alternative because AI is to useful (e.g. diagnostic accuracy) • Dominance of few providers prevents realistic alternatives Professional independence: conflicts of interest
  • 18. Thank you for your attention! Professor Dr. Karl Stöger, MJur (Oxford) Institute of Public Law and Political Science University of Graz

Notes de l'éditeur

  1. Bildquelle: https://towardsdatascience.com/cousins -of-artificial-intelligence-dda4edc27b55
  2. Bildquelle: https://ai.google/healthcare/ https://ec.europa.eu/digital-single-market/en/news/eu-invests-eu35-million-develop-artificial-intelligence-solutions-cancer-prevention-and
  3. https://www.coe.int/en/web/cepej/cepej-european-ethical-charter-on-the-use-of-artificial-intelligence-ai-in-judicial-systems-and-their-environment
  4. Bildquelle: https://de.wikipedia.org/wiki/Black_Box_ (Systemtheorie)#/media/Datei:Blackbox3D.png
  5. Bildquelle: https://image-store.slidesharecdn.com/e19171e4-fab0-4bb6-bdf4-f794ca4a0c58-original.jpeg
  6. Dazu gäbe es noch weitere Artikel von Price II: Black Box Medicine, Harvard Journal of Law & Technology 2015, 420 und Regulating Black-Box Medicine, 116 Mich. L. Rev. 421 (2017).
  7. Dazu gäbe es noch weitere Artikel von Price II: Black Box Medicine, Harvard Journal of Law & Technology 2015, 420 und Regulating Black-Box Medicine, 116 Mich. L. Rev. 421 (2017).