Hanna Wallach
Researcher, Microsoft Research covering Big Data and data-driven products. She presented…. On 1 show, 1 appearance. The Shows tab opens the full record on each.
1shows
1appearances
13statements
2resolved
1supported
1contradicted
50%fully supported
1said about them ↓ Everything Hanna Wallach said on any show that made the record, most notable first. Each card names its show and opens the statement there.
Wallach: Data-first research approaches amplify issues with algorithmic bias and fairness
“Although this is kind of conducive to fast-paced work, these data-first or method-first approaches can actually amplify issues related to bias, fairness, and inclusion of minorities.”
Wallach: Tech and government players must hire social scientists to address bias
“So as a result, if technology companies and government organizations, the biggest players in the big data game, Are going to take issues like bias and fairness and inclusion seriously. They need to hire social scientists, the people with the best training and …”
Wallach: Big data sets differ from physics data by documenting human behavior
“Unlike the data sets arising in physics, the data sets that typically fall under the big data umbrella are about people, Their attributes, their actions, and their interactions. That is to say these are social data sets that document people's lives, behaviors …”
Wallach: Big data's defining feature is granularity, not size
“The issue is granularity. In other words, not only do these data sets document social phenomena, they do so at the granularity of individual people and their second-to-second activities.”
Wallach: Research shows diverse teams best facilitate rapid breakthrough innovation
“In fact, there's even substantial research in social psychology and sociology indicating that rapid breakthrough innovations are best facilitated by bringing together people with really diverse backgrounds.”
Wallach: Addressing algorithmic bias requires focusing on big data's granular nature
“When it comes to addressing issues like bias, fairness, and inclusion, perhaps we should instead be focusing our attention on the granular nature of big data.”
Wallach: The big data game will be won by asking interesting questions
“Ultimately, the big data game will be won by those who know how to ask and answer interesting questions.”
Wallach: Convenience datasets bias analytical models toward demographic majorities
“But the problem with these convenience data sets is that they typically reflect only a particular segment of society. For example, young people with smartphones. And so, as a result, many of the methods developed to analyze these data sets end up prioritizing …”
Wallach: ML fairness research overwhelmingly focuses on predictive over exploratory models
“So much of the existing work on fairness and transparency in machine learning, not that there's very much of it, focuses on predictive models rather than models for exploratory or explanatory analyses”
Wallach: Bias in exploratory models distorts subsequent predictive models
“As a result, the models used to perform these previous analyses and any kind of bias or unfairness in them will necessarily influence the resultant findings, and hence the representations that we then choose to use in our predictive models.”
Wallach: Predictive social data models must maintain and report uncertainty
“When building and using predictive models for social data, whether resultant decisions can actually affect real-world people, representing, maintaining, and reporting uncertainty, along with any subsequent decisions, should be standard practice.”
Hanna Wallach: Most machine learning methods will be deployed without creator involvement
“It's really likely that most machine learning methods or data science methods will at some point be used by some people, end users, without the involvement of those people who created them.”
Wallach: Investigating correct model predictions helps contextualize how models treat certainty
“I don't know if there are necessarily any sort of general purpose, like this is going to fix everything kind of solutions, but I would say yes, digging into why your model is making certain predictions, even when those predictions are correct, can kind of help…”
The other half of the tape: Hanna Wallach's own voice is left out of every number here. Other people bring the name up 1 time in 1 episode across the shows. every mention, with the transcript →
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One line per show, most statements first. The link opens Hanna's full record on that show: the calibration, argument clarity, speaking style and every statement made there.
| Show | Role there | Eps | Statements | Record | |
|---|---|---|---|---|---|
| MADLEDGER | Researcher, Microsoft Research covering Big Data and data-driven products. She presented… | 1 | 13 | 50% 1/2 | full record on the MAD Podcast → |