Download as PDF, PPTX
Uploaded byJason Tamara Widjaja
Model bias in AI
The document discusses model bias in artificial intelligence, emphasizing the implications of biased AI models and their potential to perpetuate discrimination. It presents a four-part framework for mitigating model bias, which includes developer choice, model interpretability, management policy, and user interpretation. It also highlights the importance of aligning AI systems with human values and ethics to promote fairness and transparency in AI decision-making.
More Related Content
Model bias in AI
- 1. MODEL BIAS INAI 17 Aug 2017 Jason Tamara Widjaja
- 2. A biased samplesize of one I am: • Made in Singapore, 8 years overseas • Plugging into deep tech: TNB Ventures and Lauretta.io I work: • Data science and AI at MSD • IT, IS, HR, MBA, Analytics, AI I like: • Building world class data science and AI teams • “Tell me like I’m five” accessible communication
- 3. Agenda 3 1 Introduction: WhatIs AI in MSD? 2 The Problem: Model Bias in Practice 3 The Solution: A Framework for Mitigating Model Bias 4 Looking Ahead: Towards A Safer AI-enabled Future
- 4. Acting Humanly “Turing Test” Makingsense of the AI space 4 Thinking Humanly “Cognitive modeling” Thinking Rationally “Logical laws of thought” Acting Rationally “Intelligent agents”
- 5. Acting Humanly “Turing Test” Makingsense of the AI space 5 Thinking Humanly “Cognitive modeling” Thinking Rationally “Logical laws of thought” Acting Rationally “Intelligent agents”
- 6. Descriptive analytics Predictive analytics Prescriptive analytics AI as anextension of data science focused on replicating the capabilities of intelligent agents Human agency Decision support “Tool” Back office automation / RPA Human interaction automation Intelligent agent “Worker” Autonomous execution
- 7. Agenda 7 1 Introduction: AIin MSD 2 The Problem: Model Bias in Practice 3 Fixing Projects: Pressure Points 4 Fixing Systems: Broader Considerations 1 Introduction: What Is AI in MSD? 2 The Problem: Model Bias in Practice 3 The Solution: A Framework for Mitigating Model Bias 4 Looking Ahead: Towards A Safer AI-enabled Future
- 8. A thought experiment:What does being on the receiving end of a model feel like? “How can I assign my people to different training programs?” 40,000 people 4 categories of 100 features each 4 tiers of performance: low, medium, high, v.high Different development tracks Data collection Supervised learning Clustering within each tier
- 9. A thought experiment:What does being on the receiving end of a model feel like? “How can I assign my people to different training programs?” 40,000 people 4 categories of 100 features each Different development tracks Data collection Supervised learning Clustering within each tier 4 tiers of performance: low, medium, high, v.high
- 10. A thought experiment:What does being on the receiving end of a model feel like? “How can I assign my people to different training programs?” 40,000 people 4 categories of 100 features each 4 tiers: low, medium, high, talent Different development tracks Data collection Supervised learning Clustering within each tier Big questions: • Did the model favour one ‘group’ over another? • Did the model use information besides real performance in its decision? • Is the model “fair”?
- 11. 11 Now what ifthe model was fully automated? No appeals. No explanations. No human intervention.
- 12. Multiple studies havefound evidence that automated, opaque models systemically disadvantage certain demographic groups at scale
- 13. Google’s search resultsfor ‘CEO’ – 14 August 2017
- 14. If we deploymodels trained on existing, biased data sources, we may be unknowingly perpetuating discrimination
- 15. 15 Models do notautomatically remove bias - used carelessly they may systematize it
- 16. Agenda 16 1 Introduction: AIin MSD 2 The Problem: Model Bias in Practice 3 Fixing Projects: Pressure Points 4 Fixing Systems: Broader Considerations 1 Introduction: What Is AI in MSD? 2 The Problem: Model Bias in Practice 3 The Solution: A Framework for Mitigating Model Bias 4 Looking Ahead: Towards A Safer AI-enabled Future
- 17. A 4-part frameworkfor mitigating model bias 2. MODEL INTERPRETABILITY 1. DEVELOPER CHOICE 3. MANAGEMENT POLICY 4. USER INTERPRETATION More transparent tool “Make the black box less black” More skillful users “Make black box users more proficient”
- 18. A 4-part frameworkfor mitigating model bias 2. MODEL INTERPRETABILITY 1. DEVELOPER CHOICE 3. MANAGEMENT POLICY 4. USER INTERPRETATION More transparent tool “Make the black box less black” More skillful users “Make black box users more proficient”
- 19. A 4-part frameworkfor mitigating model bias MODEL INTERPRETABILITY DEVELOPER CHOICE MANAGEMENT POLICY USER INTERPRETATION “Build models thoughtfully for safer, more understandable models” Choose your features consciously
- 20. A 4-part frameworkfor mitigating model bias Choose your features consciously Trade predictive power for more interpretable models “Build models thoughtfully for safer, more understandable models” MODEL INTERPRETABILITY DEVELOPER CHOICE MANAGEMENT POLICY USER INTERPRETATION
- 21. A 4-part frameworkfor mitigating model bias “Add forensics to models to build trust” QII for understandable features (Quantitative Input Influence) LIME for non-understandable features (Local Interpretable Model-agnostic Explanations) MODEL INTERPRETABILITY DEVELOPER CHOICE MANAGEMENT POLICY USER INTERPRETATION Key lime (QII – LIME) pie
- 22. A 4-part frameworkfor mitigating model bias QII for understandable features MODEL INTERPRETABILITY DEVELOPER CHOICE MANAGEMENT POLICY USER INTERPRETATION Personal Transparency Reports Why was person X classified this way? Group Impact Diagnostics When I add feature X, what changes? Model Diagnostics Which features drive model X? “Add forensics to models to build trust”
- 23. A 4-part frameworkfor mitigating model bias LIME for non-understandable features: GOOD MODEL “Add forensics after modeling so approaches are less constrained” MODEL INTERPRETABILITY DEVELOPER CHOICE MANAGEMENT POLICY USER INTERPRETATION
- 24. A 4-part frameworkfor mitigating model bias LIME for non-understandable features: BAD MODEL “Add forensics after modeling so approaches are less constrained” MODEL INTERPRETABILITY DEVELOPER CHOICE MANAGEMENT POLICY USER INTERPRETATION
- 25. A 4-part frameworkfor mitigating model bias 2. MODEL INTERPRETABILITY 1. DEVELOPER CHOICE 3. MANAGEMENT POLICY 4. USER INTERPRETATION More transparent tool “Make the black box less black” More skillful users “Make black box users more proficient”
- 26. A 4-part frameworkfor mitigating model bias “Have a management conversation – what is a fair model? Optimise for more than ROI” Demographic Parity X% of men get a chance X% of women get a chance Equal Opportunity X% of qualified men get a chance X% of qualified women get a chance MODEL INTERPRETABILITY DEVELOPER CHOICE MANAGEMENT POLICY USER INTERPRETATION
- 27. A 4-part frameworkfor mitigating model bias “Train users of AI systems in model diagnostics and AI quality assurance” MODEL INTERPRETABILITY DEVELOPER CHOICE MANAGEMENT POLICY USER INTERPRETATION
- 28. Agenda 28 1 Introduction: AIin MSD 2 The Problem: Model Bias in Practice 3 Fixing Projects: Pressure Points 4 Fixing Systems: Broader Considerations 1 Introduction: What Is AI in MSD? 2 The Problem: Model Bias in Practice 3 The Solution: A Framework for Mitigating Model Bias 4 Looking Ahead: Towards A Safer AI-enabled Future
- 29. 5 VALUES FROMTHE ASILOMAR AI PRINCIPLES 7) Failure Transparency: If an AI system causes harm, it should be possible to ascertain why. 9) Responsibility: Designers and builders of advanced AI systems are stakeholders in the moral implications of their use, misuse, and actions, with a responsibility and opportunity to shape those implications. 10) Value Alignment: Highly autonomous AI systems should be designed so that their goals and behaviors can be assured to align with human values throughout their operation. 11) Human Values: AI systems should be designed and operated so as to be compatible with ideals of human dignity, rights, freedoms, and cultural diversity. 15) Shared Prosperity: The economic prosperity created by AI should be shared broadly, to benefit all of humanity. Jaan Tallinn, Co- founder, Skype Viktoriya Krakovna, AI Safety, DeepMind Nick Bostrom, Director, Oxford Future of Humanity Institute Erik Brynjolfsson, Director, MIT Center for Digital Business, MIT Stephen Hawking, Director of Research, Centre for Theoretical Cosmology, Cambridge University Stuart Russell, Professor of AI, UC Berkeley Elon Musk, Founder, SpaceX and Tesla Motors
- 30. Appendix and references 1.Ribeiro, M. T., Singh, S., & Guestrin, C. (2016, August). Why should I trust you?: Explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 1135-1144). ACM. 2. Datta, A., Sen, S., & Zick, Y. (2017). Algorithmic transparency via quantitative input influence. In Transparent Data Mining for Big and Small Data (pp. 71-94). Springer International Publishing. 3. Hardt, M., Price, E., & Srebro, N. (2016). Equality of opportunity in supervised learning. In Advances in Neural Information Processing Systems (pp. 3315-3323). 4. Gunning, D. (2017). Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web. 5. AI Principles. (n.d.). Retrieved August 10, 2017, from https://futureoflife.org/ai-principles/ 30
- 31. 31 Our tools needto be sharper, but they also need to be safer Industry 4.0 needs to grow up alongside ethics 4.0