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AI Governance – The Responsible Use of AI

The document outlines a comprehensive agenda on AI governance, focusing on the responsible use of AI and its various applications across industries. Key considerations for developing AI models include transparency, fairness, bias, audit, confidentiality, and ethics. It highlights the challenges of AI, such as potential biases in models and the need for effective oversight to ensure accountability and trust.

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AI Governance The Responsible Use of AI Brian Ang Nicholas Tan #ISSLearningFest
Agenda • Introduction to AI and AI Applications • Considerations for Developing AI Models • The Good, Bad and Ugly of AI • AI Governance #ISSLearningFest
Branches of AI #ISSLearningFest Computer Vision Natural Language Processing Robotics Speech Recognition and Processing Learning Algorithms Planning & Optimisation Reasoning Systems Knowledge Representation Evolutionary Algorithms Artificial Emotional Intelligence
AI Applications #ISSLearningFest Retail Banking & Finance Manufacturing Security Automotive Logistics F&B Healthcare Social and Lifestyle Small Medium Enterprises Multinational Corporations Government Citizens ConsumersOrganisations Application Domains Example Users
#ISSLearningFest https://futurism.com/microsofts-speech-recognition-is-now-as-good-as-a-human- transcriber https://www.straitstimes.com/tech/tapping-ai-to-battle-covid-19 AI Applications https://www.popularmechanics.com/technology/robots/a22148464/chinese-ai-diagnosed-brain- tumors-more-accurately-physicians/
AI Current State #ISSLearningFest • Artificial Narrow Intelligence Perform tasks within a specific domain and for certain use cases. Often within certain boundaries and constraints. • Artificial General Intelligence Able to carry out tasks across several uses cases. Generalize it’s ability across various domains. Almost human-like intelligence with ability to perform acceptably in several general tasks. Is AI able to: - Perform tasks without supervision and monitoring? - Think for cases not seen before and reacts accordingly? - Generalize across various domains and tasks? - Perform self-maintenance and upgrade? - Provide the human touch? https://www.jpost.com/jpost-tech/can-artificial-intelligence-understand-human-humor-635073 What is achievable and not achievable by AI?
Considerations for Developing AI Models #ISSLearningFest In the Context of: • Transparency • Fairness and Bias • Audit • Confidentiality • Ethics
Transparency in AI Models #ISSLearningFest When would human have trust in AI models? When they know: • What is the decision taken by the model at each step ? • Why did it make the decision? • Will it make the same decision next time? • What is the confidence of the decision made? • How to tweak or correct the decision if it is not correct?
Transparency and Explainable AI (XAI) #ISSLearningFest Deep Learning versus Rule- Based Systems
Bias in AI #ISSLearningFest What is Bias in AI? Bias in AI happens when the AI model or algorithm discriminates against certain group. It produces undesirable outcomes and does not provide a fair assessment and judgement. Bias in AI may be detected at the output stage. However, it may not always be apparent unless enough cases were presented and analysis made. Is bias caused at the output stage or much earlier stages? It can be due to much earlier stages, e.g., at the data collection stage where data collected may not be representative of the population and lacks diversity.
What could have gone wrong? #ISSLearningFest https://www.reuters.com/article/us-newzealand-passport-error/new- zealand-passport-robot-tells-applicant-of-asian-descent-to-open- eyes-idUSKBN13W0RL
AI Audit #ISSLearningFest AI Audit reinforce Trust, Transparency, Accountability and Responsibility Audit provides an independent and unbiased view on the AI model developed and provides certain level of assurance to users. It also serves to address questions such as: • Is there bias in the model? • Are there any ethical issues in the modelling(e.g., one that may cause harm)? • Is there a structure and process in place for data access? • Is there governance in place on who can modify the model? • Does the model perform within a certain level of confidence?
AI Confidentiality #ISSLearningFest From AI User Perspective From AI Owner Perspective • Informed of data collected and has the option to opt in or opt out • Usage of data is for specific purpose only • AI model to benefit the user • AI model will not harm the user • Expect certain level of clarifications to be provided at request • Gain trust from the users of AI • AI is a valuable asset and hence need to be protected • Performs AI Audit to the extent that no confidential info is being revealed • Protects AI algorithm details, to prevent info being leaked to competitors and to prevent AI algorithms from being exploited and attacked.
AI Ethics #ISSLearningFest 3 Laws of Robotics First Law: A robot may not injure a human being or, through inaction, allow a human being to come to harm. Second Law: A robot must obey the orders given it by human beings except where such orders would conflict with the First Law. Third Law: A robot must protect its own existence as long as such protection does not conflict with the First or Second Laws By Isaac Asimov https://www.flickr.com/photos/itupictures/27254369347/ Example Considerations in AI Ethics • How to prevent misuse of AI? • Is AI able to take into consideration moral values? • How much human intervention is required?
Agenda • Introduction to AI and AI Applications • Considerations for Developing AI Models • The Good, Bad and Ugly of AI • AI Governance #ISSLearningFest
The Good #ISSLearningFest
The Bad #ISSLearningFest
The Ugly #ISSLearningFest
AI Governance #ISSLearningFest Source: SG Gov, Model AI Governance Framework, 2nd Edition Source: pwc, A practical guide to Responsible AI
Operating Model #ISSLearningFest ‘An operating model is a visualisation (i.e. model or collection of models, maps, tables and charts) that explains how the organisation operates so as to deliver value to its customers or beneficiaries.’ – Andrew Campbell Marketing Quote Policy Issuance Policy Admin Claims Renewals In its simplest form, it is a value delivery chain Structure Organisational design & reporting structure Committee(s) structure & charters Oversight responsibilities Board oversight & responsibilities Management accountability & authority Committee(s) authorities & responsibilities Talent & culture Performance management & incentives Business & operating principles Leadership development & talent programmes Infrastructure Policies & procedures Reporting & communication Technology In its complete form, it is a system of practices
Elements of the Operating Model #ISSLearningFest • Process flow to deliver the value proposition (the value delivery chain) • Organisation for the people who will do the work, the structure of organisation units and support functions, the decision rights and other organisation elements • Locations for the buildings and places where the work will be done • Information for the software applications and databases needed to support the work • Suppliers for those important suppliers supporting the work who need special relationships with the organisation • Management system for the planning, budgeting, performance management, and people management processes needed to run the organisation. These underpin the other five elements
Where the Operating Model fits #ISSLearningFest Source: Julie Choo, The Strategy Journey Framework®
Organisation Development #ISSLearningFest Source: Julie Choo, The Strategy Journey Framework® Structure Organisational design & reporting structure Committee(s) structure & charters Oversight responsibilities Board oversight & responsibilities Management accountability & authority Committee(s) authorities & responsibilities Talent & culture Performance management & incentives Business & operating principles Leadership development & talent programmes Infrastructure Policies & procedures Reporting & communication Technology
AI GRC #ISSLearningFest “New products and services, including those that incorporate or utilize artificial intelligence and machine learning, can raise new or exacerbate existing ethical, technological, legal, and other challenges, which may negatively affect our brands and demand for our products and services and adversely affect our revenues and operating results.” – Alphabet, 2018 “AI algorithms may be flawed. Datasets may be insufficient or contain biased information. Inappropriate or controversial data practices by Microsoft or others could impair the acceptance of AI solutions. These deficiencies could undermine the decisions, predictions, or analysis AI applications produce, subjecting us to competitive harm, legal liability, and brand or reputational harm.” – Microsoft, 2018 AI’s complexity and growing concerns about its potentially harmful effects have inspired calls for AI Oversight.
AI Oversight #ISSLearningFest “New products and services, including those that incorporate or utilize artificial intelligence and machine learning, can raise new or exacerbate existing ethical, technological, legal, and other challenges, which may negatively affect our brands and demand for our products and services and adversely affect our revenues and operating results.” – Alphabet, 2018 “AI algorithms may be flawed. Datasets may be insufficient or contain biased information. Inappropriate or controversial data practices by Microsoft or others could impair the acceptance of AI solutions. These deficiencies could undermine the decisions, predictions, or analysis AI applications produce, subjecting us to competitive harm, legal liability, and brand or reputational harm.” – Microsoft, 2018 AI’s complexity and growing concerns about its potentially harmful effects have inspired calls for AI Oversight. AI GRC AI & Cybersecurity AI Governance Legal Governance Data Governance
Thank You! brian_ang@nus.edu.sg nicholas_tan@nus.edu.sg #ISSLearningFest https://www.linkedin.com/groups/13891625/
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