Description: This collaborative workshop session (roundtable format) will focus on the principle of fairness in AI, emphasising the need for context-specific fairness metrics for ethical governance. We will unpack the multifaceted concept of fairness in AI by discussing key components of the principle of fairness (equality, bias and non-discrimination, inclusivity, and reliability). While these components are relevant globally, their interpretation varies across jurisdictions. For example, unlike western liberal democracies, factors such as caste or religion are key aspects of non-discrimination in India. Understanding these components is essential for developing and deploying AI systems that are safe, secure, and trustworthy. As the concept of fairness in AI has often been developed focusing primarily on the US and Europe, it may be difficult to adopt them in Asian countries which have unique socio-cultural contexts and may interpret fairness differently. We will discuss fairness in India and Singapore to showcase how the concept varies across Asia, and from the broader global concept of fairness. Further, we will discuss case studies like the biassed AI job recommendation system in Indonesia to illustrate the complexities of fairness in AI. We will also conduct a simulation exercise using a hypothetical model to illustrate the potential for bias to manifest through data points such as age, gender, address, etc. Finally, we will conduct an open discussion to gain perspectives from participants on fairness metrics in their own countries and to analyse how fairness as a concept differs based on their socio-cultural contexts. This session will leverage learning from an Asia-level dialogue conducted by SMU and CCG which brought together diverse stakeholders from the APAC region to discuss the multifaceted concept of fairness in AI. We will also have a speaker from UNESCO who has experience with AI norms and can speak to global perspectives on Fairness in AI.
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