Hannah Highfill
Hannah is a Senior User Experience Researcher at Google who works primarily on the humans and the data behind AI/ML deployment
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"Everyone wants to do the model work, not the data work": Data Cascades in High-Stakes AI
Nithya Sambasivan
Shivani Kapania
Praveen Kumar Paritosh
SIGCHI, ACM (2021)
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AI models are increasingly applied in high-stakes domains like health and conservation. Data quality carries an elevated significance in high-stakes AI due to its heightened downstream impact, impacting predictions like cancer detection, wildlife poaching, and loan allocations. Paradoxically, data is the most under-valued and de-glamorised aspect of AI. In this paper, we report on data practices in high-stakes AI, from interviews with 53 AI practitioners in India, East and West African countries, and USA. We define, identify, and present empirical evidence on Data Cascades---compounding events causing negative, downstream effects from data issues---triggered by conventional AI/ML practices that undervalue data quality. Data cascades are pervasive (92% prevalence), invisible, delayed, but often avoidable. We discuss HCI opportunities in designing and incentivizing data excellence as a first-class citizen of AI, resulting in safer and more robust systems for all.
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