Artyushina_Guest lecture_YorkU CS May 2024.pptx
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Guest lecture "Regulating Artificial Intelligence in Canada: Key Challenges and Policy Options" by Dr. Anna Artyushina. York University, May 2024. Topics: understanding different types of AI systems; economic potential of AI applications; privacy issues; human rights violations; existing legislation
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Presentation Title: AI & Labor Markets
Presenter: Joaquim Jorge
Description:
Explore the transformative impact of Artificial Intelligence (AI) on labor markets in this comprehensive presentation by Joaquim Jorge. This insightful slideset delves into the opportunities and challenges that AI integration brings to various industries, highlighting key AI techniques and their real-world applications.
Bias in Hiring and Firing:
The presentation critically examines biases in AI systems used for hiring and firing decisions:
Hiring Bias: Instances where AI systems, like LinkedIn’s recommendation system and OpenAI's GPT, have shown biases in résumé ranking and job advertisements, including gender bias and cost-efficiency algorithms inadvertently favoring male candidates.
Firing Bias: AI's role in monitoring productivity and making termination decisions, with examples from Amazon’s “Time off Task” system and Uber’s driver performance metrics, highlighting unfair terminations affecting minority groups.
Mitigation Strategies:
Bias Audits: Regularly auditing AI systems to identify and mitigate biases.
Diverse Training Data: Ensuring training data are diverse and representative of all demographic groups.
Human Oversight: Implementing human oversight to review and validate AI decisions.
Explainable AI (XAI): Making AI decisions transparent and accountable to detect and correct biases.
Future of Labor Markets:
The presentation explores potential futures of labor markets with AI, presenting both utopian and dystopian scenarios:
Utopian Scenario: AI could lead to increased worker satisfaction by automating repetitive tasks, creating new career opportunities, and reducing physical labor demands, resulting in better work-life balance and economic opportunities.
Dystopian Scenario: AI could widen the economic divide, increase job precarity, and erode worker rights. Risks include increased surveillance, loss of autonomy, and the social and psychological impacts of job displacement.
Key Takeaways:
Understand the role and impact of different AI technologies in various sectors.
Recognize and address biases in AI systems, especially in hiring and firing decisions.
Explore potential futures of labor markets with AI integration.
Learn strategies for ensuring ethical and fair AI applications.
This presentation is essential for professionals, researchers, and policymakers interested in the intersection of AI and labor markets, providing a detailed analysis of current trends, challenges, and future possibilities.
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Extracts and addendums from an earlier talk, for those interested in ethics and related issues in regard to crowdsourcing, particularly research uses. Slides updated Sept. 2, 2013.
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The document discusses how artificial intelligence (AI) will impact the future of work and how human resource (HR) professionals should respond. It notes that while some studies have found that up to 47% of jobs may be automated, fears of widespread job losses are exaggerated. The document outlines 7 ways AI could impact the future, such as improving healthcare through personalized treatment plans. It argues that HR should view AI as an opportunity rather than a threat, and that HR can help ensure AI is implemented to benefit both companies and employees through retraining and viewing machines as assistants rather than replacements. The document concludes by questioning whether advances in AI and technology more broadly could be pushing humans to evolve to their next stage.
AI Governance – The Responsible Use of AI
Contemporary AI engenders hopes and fears – hopes of harnessing AI for productivity growth and innovation – fears of mass unemployment and conflict between humankind and an artificial super-intelligence. Before we let AI drive our hopes and fears, we need to understand what it is and what it is not. Then we need to understand how to implement AI in an ethical and responsible manner. Only then can we harness the power of AI to our benefit.
Privacy in AI/ML Systems: Practical Challenges and Lessons Learned
How do we protect the privacy of users when building large-scale AI based systems? How do we develop machine learning models and systems taking fairness, accuracy, explainability, and transparency into account? Model fairness and explainability and protection of user privacy are considered prerequisites for building trust and adoption of AI systems in high stakes domains. We will first motivate the need for adopting a “fairness, explainability, and privacy by design” approach when developing AI/ML models and systems for different consumer and enterprise applications from the societal, regulatory, customer, end-user, and model developer perspectives. We will then focus on the application of privacy-preserving AI techniques in practice through industry case studies. We will discuss the sociotechnical dimensions and practical challenges, and conclude with the key takeaways and open challenges.
Eric van tol
Data gedreven service innovatie kan alleen maar mislukken! Wat kan er allemaal mis gaan?
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AI+Labor Markets Presentation to CSM-16-may-2024
Presentation Title: AI & Labor Markets
Presenter: Joaquim Jorge
Description:
Explore the transformative impact of Artificial Intelligence (AI) on labor markets in this comprehensive presentation by Joaquim Jorge. This insightful slideset delves into the opportunities and challenges that AI integration brings to various industries, highlighting key AI techniques and their real-world applications.
Bias in Hiring and Firing:
The presentation critically examines biases in AI systems used for hiring and firing decisions:
Hiring Bias: Instances where AI systems, like LinkedIn’s recommendation system and OpenAI's GPT, have shown biases in résumé ranking and job advertisements, including gender bias and cost-efficiency algorithms inadvertently favoring male candidates.
Firing Bias: AI's role in monitoring productivity and making termination decisions, with examples from Amazon’s “Time off Task” system and Uber’s driver performance metrics, highlighting unfair terminations affecting minority groups.
Mitigation Strategies:
Bias Audits: Regularly auditing AI systems to identify and mitigate biases.
Diverse Training Data: Ensuring training data are diverse and representative of all demographic groups.
Human Oversight: Implementing human oversight to review and validate AI decisions.
Explainable AI (XAI): Making AI decisions transparent and accountable to detect and correct biases.
Future of Labor Markets:
The presentation explores potential futures of labor markets with AI, presenting both utopian and dystopian scenarios:
Utopian Scenario: AI could lead to increased worker satisfaction by automating repetitive tasks, creating new career opportunities, and reducing physical labor demands, resulting in better work-life balance and economic opportunities.
Dystopian Scenario: AI could widen the economic divide, increase job precarity, and erode worker rights. Risks include increased surveillance, loss of autonomy, and the social and psychological impacts of job displacement.
Key Takeaways:
Understand the role and impact of different AI technologies in various sectors.
Recognize and address biases in AI systems, especially in hiring and firing decisions.
Explore potential futures of labor markets with AI integration.
Learn strategies for ensuring ethical and fair AI applications.
This presentation is essential for professionals, researchers, and policymakers interested in the intersection of AI and labor markets, providing a detailed analysis of current trends, challenges, and future possibilities.
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- 1. Regulating Artificial Intelligence in Canada: Key Challenges and Policy Options Anna Artyushina, Ph.D. School of Urban and Regional Planning Toronto Metropolitan University
- 2. What is AI? • OECD (2023): AI system is a machine-based system that can, for a given set of human-defined explicit or implicit objectives, infer, from the input it receives, how to generate outputs such as make predictions, content, recommendations, or decisions that can influence physical or virtual environments
- 3. What is AI? OECD AI Policy Observatory (Nov 2023)
- 4. What is AI Good for? • Breast cancer research (April Khademi, Toronto Metropolitan University) • AI fraud detection (RBC, TD) • Machine learning in astrophysics (Center for Astrophysics Harvard&Smithsonian): clean noise, analyzes terabytes of data • AlphaFold 3 (Google DeepMind): visualizes and predicts (?) molecular structures
- 5. Popular AI Myths • AI systems are sentient • AI systems are smarter than humans • AI systems can replace human cognition or human compassion
- 6. On the AI Mechanics and Sentence • Narayanan & Kapoor (2024) AI Snake Oil: What AI Can Do, What it Can’t, and How to Spot the Difference. Princeton University Press • David Gunkel (2023) Person, Thing, Robot. MIT Press • Bender et.al. (2021) “On the Dangers of Stochastic Parrots. Can Language Models be Too Big?” FAccT’ Proceedings • Frank Pasquale (2020) New Laws of Robotics. Harvard University Press
- 7. The reputation business is exploding. Having eroded privacy for decades, shady, poorly regulated data miners, brokers and resellers have now taken creepy classification to a whole new level. They have created lists of victims of sexual assault, and lists of people with sexually transmitted diseases. Lists of people who have Alzheimer’s, dementia and AIDS. Lists of the impotent and the depressed. Typically sold at a few cents per name, the lists don’t have to be particularly reliable to attract eager buyers — mostly marketers, but also, increasingly, financial institutions vetting customers to guard against fraud, and employers screening potential hires. (Pasquale, 2014, p. 10) Privacy is the least of our concerns
- 8. Privacy is the least of our concerns
- 9. AI as Machine Knowledge Capital
- 10. The AI Patent Curve The US Patent and Trademark Office, 2022
- 11. AI as Machine Knowledge Capital
- 12. Key Benefits and Challenges for Canada • Economic benefits • Influx of imported AI technologies • Labour market suffers as more jobs get automated • An acute shortage of AI professionals to develop, research and regulate AI • Privacy violations • Fundamental rights violations • Threats to democracy
- 14. My analysis of AIDA
- 15. Canada’s AI Regulations • Directive on Automated Decision-Making (impact assessments; due process for the citizens) • Artificial Intelligence and Data Act (impact assessments; clear language; human in the loop; external audits; mechanisms of redress) • AI and Data Commissioner (compliance, audit, shut down) • AI Safety Institute (guidelines for the safe deployment of AI; changing regulations)
- 16. Slow Governance in the Field of AI