| APrIGF 2024 Session Proposal Submission Form | |||||||||||
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| Part 1 - Lead Organizer | |||||||||||
| Contact Person | |||||||||||
| Ms. Jhalak M. Kakkar | |||||||||||
| Organization / Affiliation (Please state "Individual" if appropriate) * | |||||||||||
| Centre for Communication Governance (CCG) at National Law University Delhi | |||||||||||
| Designation | |||||||||||
| Executive Director | |||||||||||
| Gender | |||||||||||
| Female | |||||||||||
| Economy of Residence | |||||||||||
| India | |||||||||||
| Primary Stakeholder Group | |||||||||||
| Academia | |||||||||||
| List Your Organizing Partners (if any) | |||||||||||
| Jason Grant Allen, Centre for AI and Data Governance (CAIDG) at Singapore Management University (SMU), jgallen@smu.edu.sg | |||||||||||
| Part 2 - Session Proposal | |||||||||||
| Session Title | |||||||||||
| Contextualising Fairness: AI Governance in Asia | |||||||||||
| Session Format | |||||||||||
| Tutorial (30 minutes) | |||||||||||
| Where do you plan to organize your session? | |||||||||||
| Online only (with onsite facilitator who will help with questions or comments from the floor) | |||||||||||
| Specific Issues for Discussion | |||||||||||
| In this tutorial session, we will discuss the challenges with fairness in AI systems and cover key components of the principle of fairness (equality, bias and non-discrimination, inclusivity, and reliability). These components are relevant globally, but are interpreted differently across jurisdictions. For eg. caste may not be relevant in the US or Europe but is a key aspect of non-discrimination in India. Understanding these components is essential to develop and deploy AI systems that uphold ethical principles and prevent potential harm, while enabling continued innovation. We will discuss how ‘fairness’ is subjective in nature and must be tailored to specific regional and social contexts. Fairness in AI and related ethical norms and regulatory frameworks haves often been examined and developed with primary focus on the US and Europe. However, there are unique socio-cultural contexts across the APAC region that affect the notion of ‘fairness’ , making it ineffective to adopt existing metrics of fairness and apply them universally. Hence, in our tutorial, we will discuss what fairness entails in India, Taiwan and Singapore to showcase how the concept varies even within various jurisdictions across Asia. Further, we will discuss case studies like the biassed AI job recommendation system in Indonesia to illustrate the complexities of fairness in AI. The tutorial will end with an open discussion to gain perspectives from the participants on fairness metrics in their own countries and analyse how fairness as a concept differs based on socio-cultural contexts. Recently, through a collaborative regional dialogue with SMU, we brought together diverse stakeholders from the APAC region to discuss the multifaceted concept of fairness in AI. This session aims to leverage the learnings from the dialogue. |
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| Describe the Relevance of Your Session to APrIGF | |||||||||||
| The session relates to the theme of ethical governance of emerging technologies in the context of AI development and deployment. The increasing use of AI in sectors such as healthcare, finance, law, etc. has raised concerns about societal risks, necessitating ethical governance. The ethical governance of AI through the use of focused principles has gained traction over the last few years- one such key principle is that of fairness. In this tutorial session, we will look at the gap between the existing understanding of fairness as a principle and its implementation, and discuss the need for an APAC focused approach towards operationalising fairness, while accounting for the variation in the conception of what fairness entails even within the region. Through a comparative analysis of fairness in India, Singapore and Taiwan, participants will explore the varying interpretations of fairness and will learn about the need for context-specific metrics of fairness to accommodate for regional, social, and local contexts. The open discussion will allow participants to discuss and analyse different aspects of fairness in their own countries. As an outcome of the session, the participants will have learned about what fairness entails, how the concept varies across jurisdictions and the need for context-specific fairness metrics. The participants will be able to identify bespoke metrics to evaluate fairness in AI in their socio-cultural contexts and contribute to future conversations on AI governance. Building upon the insights from the session, we will upload a post on the CCG Blog and produce a podcast on the CCG Tech Podcast which will discuss key takeaways from the session. |
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| Methodology / Agenda (Please add rows by clicking "+" on the right) | |||||||||||
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| Moderators & Speakers Info (Please complete where possible) | |||||||||||
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| Please explain the rationale for choosing each of the above contributors to the session. | |||||||||||
| Jason Grant Allen is the Director of the Centre for AI & Data Governance at the Singapore Management University. He has extensive expertise and experience in the intersection of law and emerging technology, especially in AI. As a seasoned academic, lawyer, and researcher, Jason offers valuable insights and perspectives on promoting fairness and accountability in AI systems. Isabel Hou is an experienced lawyer specializing in innovative technology since 2000. She currently serves as the Secretary General of the Taiwan AI Academy Foundation, which is dedicated to democratizing AI technology through comprehensive training programs for Taiwan's workforce. Since 2017, under her leadership, the academy has successfully trained over 11,000 engineers and managers from more than 2,000 companies. Nidhi Singh is a Project Manager at the Centre for Communication Governance (CCG), National Law University Delhi. She works extensively on AI regulation and data governance, providing policy comments to the Indian government and international organisations like OHCHR on the design of AI regulation and data governance. She is also part of the Asian Dialogue on AI Governance (collaboration between Singapore Management University and Microsoft) and engages in discussions around principle and regulatory design around AI in countries like India, Singapore, South Korea, and New Zealand. Tejaswita Kharel is a Project Officer at the Centre for Communication Governance (CCG) at National Law University Delhi. As a lawyer and researcher, her work relates to various aspects of information and technology law and policy including data protection, privacy and emerging technologies such as artificial intelligence and blockchain. Her work on the ethical governance and regulation of technology is guided by human rights based perspectives, democratic values, and constitutional principles. |
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| If you need assistance to find a suitable speaker to contribute to your session, or an onsite facilitator for your online-only session, please specify your request with details of what you are looking for. | |||||||||||
| We are currently looking for an onsite facilitator to help manage out online-only session. | |||||||||||
| Please declare if you have any potential conflict of interest with the Program Committee 2024. | |||||||||||
| No | |||||||||||
| Are you or other session contributors planning to apply for the APrIGF Fellowship Program 2024? | |||||||||||
| No | |||||||||||
| APrIGF offers live transcript in English for all sessions. Do you need any other translation support or any disability related requests for your session? APrIGF makes every effort to be a fully inclusive and accessible event, and will do the best to fulfill your needs. | |||||||||||
| In order to enable inclusive participation, we request for live transcription of our session. Additionally, since we aim to use interactive tools such as mentimeter, white-board, polling, etc. to engage with both the online and onsite participants, we request APrIGF to ensure that the online platform allows for online access for the use of these tools. | |||||||||||
| Number of Attendees (Please fill in numbers) | |||||||||||
| Gender Balance in Moderators/Speakers (Please fill in numbers) | |||||||||||
| Consent | |||||||||||
 I agree that my data can be submitted to forms.for.asia and processed by APrIGF organizers for the program selection of APrIGF 2024. |
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