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Hanna Wallach

Researcher, Microsoft Research covering Big Data and data-driven products. She presented her work during the January 2015 edition of Data Driven NYC. · 1 appearance on the record.

computed by AI from the episodes · how this works → · full disclaimer →

13statements → 4claims → 2claims resolved → 3.62/5average certainty → 2.08/5average debate potential → 1said about them ↓

1 supported 0 partly supported 1 contradicted 2 not checkable as stated how the 4 claims stand · each chip opens the sources

2 predictions · 2 assertions · 9 insights · every statement was checked. The predictions and assertions are the 4 claims: statements the public record can support or contradict. 2 are resolved, and 2 name no date, number or outcome precise enough to check. Everything else (opinions, insights, what ifs, disclosures) can never be settled by the record, so it carries no assessment.

The record, in short

What the tape says about how Hanna argues and how the claims held up. Everything they said, and everything said about them, is in the tabs below.

Their most notable supported claim

Assertion Supported
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”
Hanna Wallach Jan 15, 2015 ▶ 12:22 Hanna Wallach, Microsoft Research // Data Driven #33 // Jan 2015 (Hosted by FirstMark Capital)

Their most notable contradicted claim

Assertion Contradicted
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.”
Hanna Wallach Jan 15, 2015 ▶ 6:13 Hanna Wallach, Microsoft Research // Data Driven #33 // Jan 2015 (Hosted by FirstMark Capital)

How they sound: speaking style how? →

194 words/min while actually speaking · 5.4 um and uh per 1k words

No argument clarity score for Hanna Wallach: no usable question→answer exchanges on raw tape (a fair score needs 8+). We do not score a sample that small. Roundtable and news formats yield far fewer direct exchanges than interviews.

Measured by listening to the audio itself: 3,160 words across 1 episode of raw-level tape, transcribed verbatim with every um and uh kept, each one attributed only where the alignment onto our timed stream is unambiguous. These are measurements of speaking style. We do not rank them: across this corpus, fluency and argument quality are nearly uncorrelated (ρ≈0.2), and smooth talking does not signal clear thinking. How it's measured →

Everything Hanna Wallach said on the MAD Podcast that made the record, most notable first. Filter by type, assessment or year in the ledger →

Insight
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.”
Hanna Wallach Jan 15, 2015 ▶ 10:25 Hanna Wallach, Microsoft Research // Data Driven #33 // Jan 2015 (Hosted by FirstMark Capital)
Insight
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 …”
Hanna Wallach Jan 15, 2015 ▶ 6:27 Hanna Wallach, Microsoft Research // Data Driven #33 // Jan 2015 (Hosted by FirstMark Capital)
Insight
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 …”
Hanna Wallach Jan 15, 2015 ▶ 3:44 Hanna Wallach, Microsoft Research // Data Driven #33 // Jan 2015 (Hosted by FirstMark Capital)
Insight
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.”
Hanna Wallach Jan 15, 2015 ▶ 4:18 Hanna Wallach, Microsoft Research // Data Driven #33 // Jan 2015 (Hosted by FirstMark Capital)
Assertion Contradicted
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.”
Hanna Wallach Jan 15, 2015 ▶ 6:13 Hanna Wallach, Microsoft Research // Data Driven #33 // Jan 2015 (Hosted by FirstMark Capital)
Insight
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.”
Hanna Wallach Jan 15, 2015 ▶ 7:20 Hanna Wallach, Microsoft Research // Data Driven #33 // Jan 2015 (Hosted by FirstMark Capital)
Prediction Not checkable as stated
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.”
Hanna Wallach Jan 15, 2015 ▶ 9:23 Hanna Wallach, Microsoft Research // Data Driven #33 // Jan 2015 (Hosted by FirstMark Capital)
Insight
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 …”
Hanna Wallach Jan 15, 2015 ▶ 11:04 Hanna Wallach, Microsoft Research // Data Driven #33 // Jan 2015 (Hosted by FirstMark Capital)
Assertion Supported
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”
Hanna Wallach Jan 15, 2015 ▶ 12:22 Hanna Wallach, Microsoft Research // Data Driven #33 // Jan 2015 (Hosted by FirstMark Capital)
Insight
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.”
Hanna Wallach Jan 15, 2015 ▶ 13:12 Hanna Wallach, Microsoft Research // Data Driven #33 // Jan 2015 (Hosted by FirstMark Capital)
Insight
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 Jan 15, 2015 ▶ 15:45 Hanna Wallach, Microsoft Research // Data Driven #33 // Jan 2015 (Hosted by FirstMark Capital)
Prediction Not checkable as stated
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.”
Hanna Wallach Jan 15, 2015 ▶ 18:01 Hanna Wallach, Microsoft Research // Data Driven #33 // Jan 2015 (Hosted by FirstMark Capital)
Insight
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…”
Hanna Wallach Jan 15, 2015 ▶ 21:06 Hanna Wallach, Microsoft Research // Data Driven #33 // Jan 2015 (Hosted by FirstMark Capital)

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 on the MAD Podcast. every mention, with the transcript →

Who brings them up most Chris Wiggins 1

Every mention by year

tap a year for its mentions
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0112015episodes it came up in
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2015 1 mention in 1 episode

Appearances (1)

EpisodeDateSpeaking time
Hanna Wallach, Microsoft Research // Data Driven #33 // Jan 2015 (Hosted by FirstMark Capi Jan 15, 2015 18m
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