Data Science, Database, AI Startups and Domain Learning's (Video-Image-Text-Data-Database): Data Quality - Algorithm Fairness

September 25, 2021

Key Notes

Poor data quality in high-stakes domains can have outsized effects on vulnerable communities and context
Data Cascades: compounding events causing negative, downstream effects from data issues, resulting in technical debt over time
Many researchers have pointed to the undervalued human labour that powers AI models
Practitioners often work with a set of assumptions about their data during analysis and visualisation
Other frameworks to discover data bugs and clean data include ActiveClean and BoostClean
Data cascades are complex, long-term, occur frequently and persistently
Under-valuing of data work is common to all of AI development
Practitioners viewed data as operations, moved fast, hacked model performance (through hyperparameters rather than data quality)
Everyone wants to do the model work, not the data work
It was difficult to get buy-in from clients and funders to invest in good quality data collection and annotation work
Lack of adequate training on AI data quality
Cascades triggered by ‘hardware drifts’
Cascades triggered by ‘environmental drifts’

Key Notes

While Indians are part of the AI workforce, a majority work in services, and engineers do not entirely represent marginalities,limiting re-mediation of distances
While other axes of discrimination and injustices such as disability status
Algorithmic powerful in India, where the distance between models and oppressed communities is large
“rich people problems like cardiac disease and cancer, not poor people’s Tuberculosis, prioritised in AI"

Data Science, Database, AI Startups and Domain Learning's (Video-Image-Text-Data-Database)