With their complex connections and dynamics, social networks play a pivotal role in shaping opportunities and outcomes. However, inherent biases within these networks can perpetuate inequality in algorithms and society. While fairness research has made significant progress, it often neglects the influence of personal networks on decision-making and focuses narrowly on distributive justice. This leaves critical gaps: the limited understanding of bias mitigation within network effects, the overemphasis on a single moral lens, and the challenge of balancing multiple, potentially conflicting, moral goals.

This interactive talk explores these gaps, challenging the “what you see is what you get” (WYSIWYG) worldview and the “we are all equal” (WAE) perspective. Through a series of scenarios, participants will make decisions in situations where meritocracy, effort, fairness, and personal choice intersect. This exercise is not about prescribing fairness but about empowering you with tools for informed decision-making — in hiring, collaborations, awarding opportunities, and even algorithms for networked data. By the end, participants will have a fresh outlook on fairness, recognizing the complexities and tensions that arise when balancing different, equally compelling, moral priorities.

Important Note: Participants will require internet access and a laptop or mobile phone to engage with the decision-making scenarios.