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What regulation for Artificial Intelligence?

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What regulation for Artificial Intelligence?

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Should we regulate Artificial Intelligence? What are the challenges to face bias in data and algorithms? What is trustworthy AI? AI HLEG (European Commission) and AIGO (OECD) feedback experiences and recommendations. Example in precision medicine: AI/ML for medical devices

Should we regulate Artificial Intelligence? What are the challenges to face bias in data and algorithms? What is trustworthy AI? AI HLEG (European Commission) and AIGO (OECD) feedback experiences and recommendations. Example in precision medicine: AI/ML for medical devices

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What regulation for Artificial Intelligence?

  1. 1. Faut-il réguler l’intelligence artificielle ? www.mediantechnologies.com Chief Scienceand InnovationOfficer– MedianTechnologies Nozha BOUJEMAA IBM Cloud Academy
  2. 2. Data & Algorithms • Data are everywhere in personal and professional environment • Algorithms making sense and value from these data are pervasive in more and more digital services. • Algorithmic-based decisions are embedded from the processing of personal data to sensitive data in critical industrial systems such : health-care, personalized medicine, autonomous cars, precise agriculture, conversational agents or public services • Big Data Technologies, agnostic to applications, are enablers for AI capabilities in real-life services « 2 sides of the same coin » www.mediantechnologies.com- Nozha Boujemaa2
  3. 3. Data & Algorithms • Rising benefits from Big Data and AI technologies have wide impact on our economy and social organization ; • Transparency and trust of such Algorithmic Systems(data & algorithms) becoming competitivenessfactors for Data-driven economy ; • Data analytics is changing from description of past to predictive and prescriptive analytics for decision support ; • Importance of remedying the information asymmetry between the producer of the digital service and its consumer, be it citizen or professional – B2C or B2B => civil rights, competition, sovereignty. « 2 sides of the same coin » www.mediantechnologies.com- Nozha Boujemaa3
  4. 4. Algorithmic systems in every day life • Some dominant platforms on the market play a role of "prescriber” by directing a large share of user traffic: • Ranking mechanisms (search engine), • Recommendation mechanisms and contentselection Product or service recommendation: is it most appropriate for the consumer (personalization) or the most appropriate to the seller (given the stock)? • Opacity of the use made of sensitive data and how they are processed, • What about the consent? Is it always respected? • Credit scoring, recruitment, how fair is this? • Predictivejustice? • “Free” Business models ? ⇒New discrimination between those who know how algorithms work ad who do not www.mediantechnologies.com- Nozha Boujemaa4
  5. 5. • Decision explanation and tractability: Trust and Transparency of computer- aided decision-making process (decision responsibility):what are the different criteria/data/settings that have led to the specific decision in order to understand the global path for the reasoning? • “How Can I trust Machine Learning prediction?” it happens to build the model of the object context rather the object itself • Robustness to bias/diversion/corruption Transparent and Accountable Data Management and Analytics Nozha Boujemaa - 5 www.mediantechnologies.com- Nozha Boujemaa5
  6. 6. Explanation: Ribeiro et al. 2016, LIME: Why should I trust you? Explaining the predictions of any classifier Safe AI: Robustness and Explanation Robustness: Goodfellow, Shlens and Szegedy 2015,“Explaining and Harnessing Adversarial Examples” Nozha Boujemaa - 6 www.mediantechnologies.com- Nozha Boujemaa6
  7. 7. Algorithmic Systems Bias Mastering Big Data Technologies: Bias problems could impact data technologies accuracy and people’s lives Challenges 1: Data Inputs to an Algorithm – Poorly selected data – Incomplete, incorrect, or outdated data – Data sets that lack disproportionately represent certain populations – Malicious attack Challenges 2: The Design of Algorithmic Systemsand Machine Learning – Poorly designed matching systems – Unintentional perpetuation and promotion of historical biases – Decision-making systems that assume correlation implies causation www.mediantechnologies.com- Nozha Boujemaa7
  8. 8. Challenges • It is a mistake to assume they are objective simply because they are data-driven. Algorithms are encapsulatedopinions through decision parameters and learning data • Implementing the “Transparent-by-Design”: fairness/equity, loyalty, neutrality => “Value-by-Design” • Mastering the accuracy and robustness of Big Data & AI techniques: bias, diversion/corruption, reproducibility, source of unintentional discrimination Nozha Boujemaa - 8 www.mediantechnologies.com- Nozha Boujemaa8
  9. 9. Challenges : Trustworthy AI  Responsible: Compliance with Regulation/Policy and Social Values/Ethics  Robust and safe: against bias, corruption, noise, reproducibility, repetability etc  Auditability and Responsible-by-Design tools and algorithms for socio-economic empowerment  AI is part of the solution and not only the law! Algorithmic tools to monitor the behavior of AI technologies(traceability, interpretability etc)  Algorithmic tools to empower regulation bodies for law execution efficiency  Governance of Data is key, ML algorithms are shared in open-source but NOT Data  Available Data ≠ Exploitable Transparency Tools vs GDPR vs Cloud Act (Clarifying Lawful Overseas Use of Data Act) ? www.mediantechnologies.com- Nozha Boujemaa9
  10. 10. Challenges / Efforts  Complex concepts, Dependent on cultural context, law context, etc. Transparency, Accountability, Loyalty, Fairness, Equity, Intelligibility, Explainability, Traceability, Auditability, Proof and Certification, Performance, Ethics, Responsibility  Pedagogy and explanation, awareness rising, uses-cases, (all public! Including scientists) Ethical ≠ Responsible, Transparent ≠ Make available the source code International collaboration is key (AI HLG- EC, OECD, UNESCO etc)  Interdisciplinary co-conception of solutions, How responsible is a ML algorithm?  Interdisciplinary training for Data Scientists:law, sociology and economy, Careful software reuse => mastering information leaks (SRE) Nozha Boujemaa - 10 www.mediantechnologies.com- Nozha Boujemaa10
  11. 11. International Efforts – AI HLEG EC https://ec.europa.eu/digital-single-market/en/news/ethics-guidelines-trustworthy-ai Artificial Intelligence - High Level Expert Group of the European Commission (AI HLEG Chair: Pekka Ala- Pietilä, 2 Vice-Chairs: Nozha Boujemaa D1 & Barry O’Sullivan D2) Requirements: 1.Human agency and oversight (fundamental rights) 2.Technical robustness and safety 3.Privacy and data governance 4.Transparency (Including traceability) 5.Diversity, non-discrimination and fairness 6.Societal and environmental wellbeing (Including sustainability and democracy 7.Accountability => Living assessment list through key economic sectors www.mediantechnologies.com- Nozha Boujemaa11
  12. 12. International Efforts – AI HLEG EC https://ec.europa.eu/digital-single-market/en/news/ethics-guidelines-trustworthy-ai Artificial Intelligence - High Level Expert Group of the European Commission (AI HLEG Chair: Pekka Ala- Pietilä, 2 Vice-Chairs: Nozha Boujemaa & Barry O’Sullivan) www.mediantechnologies.com- Nozha Boujemaa12 Realising Trustworthy AI throughout the system’s entire life cycle
  13. 13. Living documents throught Assessment List (sectorial pilots): 1.Human agency and oversight (fundamental rights) 2.Technical robustness and safety : 1.Resilience to attack and security: 2.Fallback plan and general safety: 3.Accuracy 4.Reliabilityand reproducibility: 3.Privacy and data governance 1.Respect for privacy and data Protection: 2.Qualityand integrityof data: 3.Access to data: 4.Transparency (Including traceability) 1.Traceability: 2.Explainability 3.Communication 5.Accountability through Auditability 6. Societal and environmental well-being www.mediantechnologies.com- Nozha Boujemaa13
  14. 14. International Efforts – AIGO https://www.oecd.org/going-digital/ai/principles/ Artificial IntelligenceExpert Group at the OECD Principles released May 23 2019, Book June 11 2019 1.AI should benefit people and the planet by driving inclusive growth, sustainable development and well-being. 2.AI systems should be designed in a way that respects the rule of law, human rights, democratic values and diversity, and they should include appropriate safeguards – for example, enabling human intervention where necessary – to ensure a fair and just society. 3.There should be transparency and responsible disclosure around AI systems to ensure that people understand when they are engaging with them and can challenge outcomes. 4.AI systems must function in a robust, secure and safe way throughout their lifetimes, and potential risks should be continually assessed and managed. 5.Organizations and individuals developing, deploying or operating AI systems should be held accountable for their proper functioning in line with the above principles. www.mediantechnologies.com- Nozha Boujemaa14
  15. 15. FDA consultation AI/Machine Learning Software As Medical Device Nozha Boujemaa - 15 www.mediantechnologies.com- Nozha Boujemaa15 Responsible AI in HealthCare Purpose: Patient safety and security => Master side effects: potential errors and conditionsof correctalgorithmicoutcome
  16. 16. The traditional paradigm of medical device regulation was not designed for adaptive AI/ML technologies  In the current framework, FDA would require a new premarket submission when the AI/ML software modification significantly affects: o device performance o safetyand effectiveness. o device’s intendeduse o major change to the software algorithm.  The new proposed framework addresses the critical question of regulating: o What is the AI/ML software modification? o How does it affect Product Lifecycle RegulatoryApproach? o How are Premarket Assurance ofSafety and Effectiveness assessed? 16 Responsible AI in HealthCare www.mediantechnologies.com- Nozha Boujemaa
  17. 17. Take away messages: TrustworthyAI => Proof of Trust Should we regulate more AI? ⇒ Commitment to Traceability foster Self-Regulation Do we need explainability? Which explainability? ⇒Enable Technical Accountability & Auditability ⇒Insure Robustness – Data selection & life cycle monitoring, – Algorithmic repeatability, reproducibility, interpretability – Risk assessment and management www.mediantechnologies.com- Nozha Boujemaa17
  18. 18. www.mediantechnologies.com- Nozha Boujemaa18
  19. 19. Thank you! Our Core Values Leadinginnovationwith purpose Combine the spirit of innovationwith our passion and convictionto help cure cancer and other debilitatingdiseases. Committing toqualityin all we do Be dedicated to qualityin everythingwe do. Qualitybegins with us and we are committed to it. Supporting our customersin achieving theirgoals Listen to the needs of our customers and help make their goals our goals through our innovation,imagingexpertise,superior services and qualitysolutions. Putting the patientfirst There is a person at the other end of the images we analyze who is countingon us to do everythingwe can to help make them healthier. www.mediantechnologies.com19

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