The Rise of AI and Data Science: Ethics and Governance Challenges
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Dr Kelvin Ross discussed the rise of AI and data science and its impact on ethics and governance. He has over 25 years of experience in advanced technology and has participated in both successful and unsuccessful startups. He discussed how data is used in areas like driverless cars, precision medicine, and ICUs. Topics around AI included machine learning, deep learning, datasets, and the "super model." Challenges around scaling, bias, overfitting, and privacy were also covered. The talk concluded with a discussion of governance issues like oversight, explainability, appeal, and the need for regulation as AI impacts more areas of society.
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The Rise of AI and Data Science: Ethics and Governance Challenges
- 1. ACS EMERGING & DEEP TECH WEBINAR: THE RISE OF AI AND DATA SCIENCE AND ITS IMPACT ON ETHICS AND GOVERNANCE Dr Kelvin Ross kelvin.ross@kjr.com.au
- 2. Dr Kelvin Ross is an entrepreneur, technologist and researcher. He currently holds a number of roles, including Founder & Chairman of KJR, a mid-tier IT consultancy, Adjunct Associate Professor in Intelligent and Integrated Systems at Griffith University, and Director at IntelliHQ, a non-profit innovation Centre focused on Artificial Intelligence in healthcare at Gold Coast University Hospital, and a medical research data platform startup, Datarwe. He has over 25 years of experience in advanced technology commencing with safety-critical systems in the military, then moving on to transportation, banking, financial markets, government and healthcare systems. He has participated in several successful and unsuccessful technology startups, as well numerous successful and unsuccessful technology implementation programmes in medium and large enterprises.
- 5. ICU – a data science treasure trove Data Models Outcomes
- 7. 0 kph 00 arterial Stopped at traffic light straight day PREDICT Fatigue +15 mins +30 mins +1 hr +2 hr Stress Heart Arythmia Stroke Risk Diabetes Risk
- 8. Davos 2017 "You have to think about how you develop trust for any new technology” Ginni Rometty, IBM CEO "There are some things that machines can do that perhaps they shouldn't do” Satya Nadella, Microsoft CEO
- 10. What is Machine Learning
- 11. Source: Sinovation Ventures, https://pandaily.com/kai-fu-lee-9-years-of-sinovation-ventures-parallel-tech-universes-and-vc-ai/
- 12. Deep Learning Class Truth Infer Loss Chihuahua 1.0 0.214 +0.786 Labrador 0.0 0.769 -0.769 inference back propagation
- 20. Source: ARTIFICIAL INTELLIGENCE THE NEXT DIGITAL FRONTIER?, McKinsey Global Institute, June 2017, http://www.mckinsey.com/mgi
- 21. AI Titans
- 23. Key to AI Competitiveness
- 26. https://www.forbes.com/sites/kashmirhill/2012/02/16/how-target-figured- out-a-teen-girl-was-pregnant-before-her-father-did How Target Figured Out A Teen Girl Was Pregnant Before Her Father Did
- 27. Training Bias • Algorithm reflecting underlying training data bias • Institutionalised behaviour captured in training data • Reinforced in feedback loop • Hidden variables • Less explicit correlations, e.g. postcode http://wpj.dukejournals.org/content/33/4/111.full.pdf+html
- 29. Social Impacts Entry Qualification HR Selection Credit Scoring Sentencing
- 30. https://www.reuters.com/article/us-amazon-com-jobs-automation-insight/amazon- scraps-secret-ai-recruiting-tool-that-showed-bias-against-women-idUSKCN1MK08G Amazon scraps secret AI recruiting tool that showed bias against women
- 31. Young Women Leaders in AI www.womeninai.com.au
- 32. RoboDebt
- 33. 1. Oversight 2. Explainability 3. Appeal http://www.innovationaus.com/2017/09/ Medcraft-on-ethics-and-algorithms “Businesses and government must be held accountable for their use of computer algorithms and big data and transparency should be the first priority” Greg Medcraft ASIC Chairman
- 34. The app says you require surgery in the next 24 hours. How did it decide that? It doesn’t say, but it does have an AUC of 0.94!
- 35. The app says you require surgery in the next 24 hours as your blood pressure is low, ECG rhythm is irregular, and protein test returned positive.
- 38. https://www.nytimes.com/2019/03/30/technology/mark-zuckerberg-facebook-regulation-explained.html 1. Harmful Content 2. Election Integrity 3. Privacy 4. Data Portability
- 46. https://keenlab.tencent.com/en/whitepapers/Experimental_Security_Research_of_Tesla_Autopilot.pdf Small stickers on the ground trick Tesla autopilot into steering into opposing traffic lane
- 47. Researchers design patch to make people ‘virtually invisible’ to AI detectors https://www.computerworld.com.au/article/660283/researchers-design-patch-make-people-virtually-invisible-ai-detectors/
- 49. General Data Protection Regulation • Effective 25 May 2018 • Automated individual decision-making, including profiling (Article 22) is made contestable. Citizens now have the right to question and fight decisions that affect them that have been made on a purely algorithmic basis. https://en.wikipedia.org/wiki/General_Data_Protection_Regulation
- 50. Australian Data Sharing • Privacy Acts (Federal and States) • Consumer Data Rights • https://consumerdatastandards.org.au • https://www.oaic.gov.au/consumer-data-right/about-the- consumer-data-right/ • Office of the National Data Commissioner • https://www.datacommissioner.gov.au/resources/discus sion-paper
- 54. Australian Human Rights Commission https://tech.humanrights.gov.au/sites/default/files/2019-
- 55. Medical Big Data & AI • Challenging • TGA SAMD Classification • Health Acts / Privacy Acts • Human Research Ethics Committee – NHMRC • Site Specific Agreements with Hospitals https://www.tga.gov.au/sites/default/files/presentation-proposed-reforms- regulation-software-including-software-medical-device-consultation-results.pdf https://www.tga.gov.au/sites/default/files/submission-consultation-regulation- software-including-software-medical-device-samd-ranzcr.pdf
- 58. https://www.fda.gov/media/122535/download FDA: Proposed Regulatory Framework for Modifications to AI/ML based SAMD
- 59. UK NHS - Code of conduct for data-driven health and care technology 1. Understand users, their needs and the context 2. Define the outcome and how the technology will contribute to it 3. Use data that is in line with appropriate guidelines for the purpose for which it is being used 4. Be fair, transparent and accountable about what data is being used 5. Make use of open standards 6. Be transparent about the limitations of the data used 7. Show what type of algorithm is being developed or deployed, the ethical examination of how the data is used, how its performance will be validated and how it will be integrated into health and care provision 8. Generate evidence of effectiveness for the intended use and value for money 9. Make security integral to the design 10. Define the commercial strategy https://www.gov.uk/government/publications/code-of-conduct-for-data-driven-health-and-care-technology/ initial-code-of-conduct-for-data-driven-health-and-care-technology
- 60. Software 2.0 Stack Andrej Karpathy, Tesla https://vimeo.com/274274744 Supporting developers to learn optimal rules (ML architectures and weights) from example inputs and outputs 2 main areas supporting teams: - Label - Maintain surrounding “Data Infrastructure” - Visualise datasets - Create/edit labels - Bubble up likely mislabeled examples - Suggest data to label - Flag labeler disagreements - ... Software 1.0 Software 2.0 Supporting developers to write rules (programs) to produce outputs from inputs E.g. IDEs, Test Automation
- 63. Modelling Evaluation Deployment Surveillance TRAIN TEST Model A/B Test Apply To Production Data Monitor Production Interactions Initial Data Further production data iteratively evolves model Phase 1 Phase 2 Phase 3 Phase 4 Lab Single Site Multi Site Market Surveillance Clinical Trial Model
- 64. Closing Remarks 1. Extend human capability using big data and AI 2. New classes of risk 3. Regulation is inevitable 4. Assurance, Test & Evaluation will be key
- 67. Lyrebird.AI
- 68. http://www.abc.net.au/catalyst/stories/4718816.htm Video: 40:45 - 44:10 Catalyst – Meet The Avatars
- 69. Uncanny Valley The concept of the uncanny valley suggests humanoid objects which appear almost, but not exactly, like real human beings elicit uncanny, or strangely familiar, feelings of eeriness and revulsion in observers. https://en.wikipedia.org/wiki/Uncanny_valley
Editor's Notes
- The opportunity
- Where data can be used – emphasise collaboration with ANZICS data group, and Melbourne institutions
- Andrew Ng, ex Baidu head of AI https://www.youtube.com/watch?v=21EiKfQYZXc 33:00