The term "data bias" is correct and usable in written English.
You can use it when talking about a phenomenon that occurs in data collection and management, in which certain data points may be overlooked or given greater attention due to internal processes or outside influences. For example, "The report's findings were affected by data bias, as the sample size wasn't large enough to accurately capture the true population."
For example, can the benchmarking data bias the performance of any specific type of algorithm?
They will also develop approaches to mitigate data bias and to ensure that AI systems behave ethically when deployed.
Klein and Onuoha said that since data informs policy decisions, working to combat data bias has immediate real-world implications.
As Cathy O'Neil explains in Weapons of Math Destruction, we often don't understand the data we feed into our systems and data bias is becoming a massive problem.
We also present a new kind of data bias stemming from API attacks with both algorithms, data, and validation results.
Oct. 31, 2018 2:48 p.m. Mimi Onuoha, left, and Lauren Klein speak about data, bias and power at the Center for Digital Humanities at Princeton.
However, measuring local fibrillation intervals requires an extensive period of time when using visual methods, and such methods are also subject to data bias.
If a model is fitted to empirical data, bias can arise from terms which are not incorporated in the model assumptions.
The theme of data bias runs through several events in the CDH Year of Data including a keynote talk by Safiya Noble, author of "Algorithms of Oppression: How Search Engines Reinforce Racism," on Dec. 6.
You can test for data bias by using more than one set of data — for example, a loan-application system based on historical data will only perpetuate the biases inherent in that data, which will likely show that members of certain groups in the population have not qualified for loans.
Training data bias is not the only economic issue, however.
