Representation of Female Inventors on Patent Teams
Jordi Goodman
Equity would be achieved in 2092 if current trends continue.
Group dynamics: is everyone equally likely to be on a team? E.g., if women are
20% of STEM workforce, and teams are equally likely, then two person teams
should be all female .2x.2 of the time, or 4%. In small groups, though, women
are underrepresented; in larger groups of 5 all-male and all-female teams are
overrepresented. DEI initiatives may be pushing women together to work and not
promoting working together? In university groups, the most underrepresented group
is a mixed one (e.g., 2 men and 2 women).
Curating Black Music: Copyright, Ownership &
Commodification
Olufunmilayo Arewa
Book project: How recording business has shaped identities
through curation, including of sexuality and of profits. Copyright incentives
to create v. incentives to exploit. Black musical forms are at the core of pop
music, not just in the US but globally. African-American artists are
particularly affected by sharecropping model in music where it is difficult if
not impossible to get out of debt: widespread manipulation of accounting and
lack of transparency, which remain dominant today even after major technical
change. Lots of discrimination, including by gender; differential treatment of
African-American artists. Racial scripts about creativity are the basis for
denigrating artists.
Example: Lil Nas X, Billboard removed Old Town Road from Hot
Country chart, claiming it didn’t embrace enough elements of “today’s country
music to chart in its current version.” Billboard said it wasn’t race-related,
which is not particularly credible; shows the historical curation of country
music category as for white people. Other genres are considered white still: folk,
rock and roll. “Urban,” R&B, etc. are Black. That limits opportunities and
denies historical connections, e.g. country’s roots in Black musical forms. [Banjo/fiddle
crossover, history of interracial playing, “Black hillbillies” who couldn’t get
record contracts.] This is about curation: we decide Black is a category, what
fits in it, and what rights people in that category have. Shaped by conceptions
of what culture is or should be, but has an economic impact.
Another example: sculptures. We think of them as white but
Greeks and Romans painted them. What does it mean to paint those sculptures? Resistance
to doing so reflects imagined histories of whiteness. Current assumptions about
race shape our visions of the past, present, and future. They can change—they did
in the past, which didn’t think in the same way.
African-American artists traditionally got lower royalty
rates and paid out in different ways. Incentives to exploit marginalized groups—record
companies can treat them worse.
The Gender Gap in Academic Patenting
Miriam Marcowitz-Bitton, Michael Schuster, and Deborah R.
Gerhardt
Mary Cade & June Davis: two women very important in
making Gatorade drinkable—saving the project—but not credited in any patent,
whereas husband Bob Cade (who vomited when he drank the first version) gets the
credit. When Stokely got the rights, it assigned chemist Davis to make it more palatable,
and she did, but that didn’t lead to public contemporaneous credit.
In 2015, 29% of patent applications name at least one woman
inventor—up from 17% in 1997. 7.2% of named inventors are women. There is also
evidence of bias in the examination process against people with identifiably
female names. About 63% of academic patents filed by all-men groups; under 2%
filed by all-women groups. 64% of inventor teams made up only of women are
single-inventor teams; not true of inventor teams made up only of men, where
many are 2-person teams. Also seeing citation rates in later patents that favor
men.
Computer Software Patents and the Gendered View of Computer
Programming as Labor or Innovation
Nina Srejovic
Lots of key women programmers in early computing history.
When only women were programming, men thought it was sexier to build hardware
and no one thought the software was important. Women were viewed as mere operators
of machines built by men. Gov’t rating was SP, for “sub-professional.”
Change in identity of profession: to ensure that computer
work would not be “handmaiden,” private companies/ newly established professional
organizations/academic disciplines/gov’t funding sought to create programming
as a discipline in its own right—software engineering.
Lawyers, scholars and judges both fostered and accepted this
view. Now the problems were difficult, intellectually challenging, resulting in
innovation/invention. Programming became part of the machine: patentable as “instant
hardware.” Creator v. user stereotype: what was unpatentable as labor when women
did it became patentable when men did it and were perceived to need incentives/rewards.
Challenges the notion that women’s own activities determine the number of
patents they’re granted. So patent-related metrics themselves encode biases.
Q: about class: also happens with workers who aren’t
credited with things they learn from using the machines.
Q: is the answer more software patents or fewer? Maybe the
male model isn’t one to emulate. Credit/recognition doesn’t have to be turned
into patents.
A: agree we don’t want more software patents of the sort now
being issued. But software patents are better than software copyright.
The Innovation Glass Ceiling: How Women are Penalized for
Boundary Spanning Research
Ryan Whalen, Tara Sowrirajan, Sourav Medya, and Brian Uzzi
Key premises: there is a STEM gender gap, including in
patenting; atypical boundary-spanning inventions are particularly important
because they introduce new product categories and expand the tech/scientific
universes by making novel knowledge recombinations. Higher probability of being
high-value. [Also high-risk: they are more likely to fail too; but they have
higher maintenance percentages when granted suggesting higher value overall.]
But: women face barriers doing this type of research that
men don’t. Consistent gap in success when there are multiple CPC subclasses for
the claimed invention; where the algorithm can’t determine gender, the claimed
invention does as well as it does when the inventor is male. Persists through various
explanatory variables like inventor/examiner experience, examiner gender, team
size, entity size, etc. Some subclass combinations are typical and others are extremely
atypical.
The more atypical the combination and the more of the team
that is female, the less the chance of a grant. But the more atypical the
combination and the more of the team is male, the better their chances. So men
are rewarded for atypicality, and women are penalized. This also holds for first listed inventor.
Similar findings from UKIPO and Canadian IPO.
When women are granted boundary spanning patents, they take over
a month longer (with controls), they have fewer independent claims than men,
they lose more independent claims—about a quarter—on prosecution than men do.
There is no clarity on causality. Questions: examiner bias,
employer bias (e.g., excluding women from teams with great boundary-spanning
inventions), patent agent roles, art unit/technical area analysis. Assumption:
quality is randomly distributed but we don’t know whether it’s true. [Also I
wonder which way that cuts: if women’s low-quality inventions are screened out
and men’s aren’t, that could be an even worse problem.]
Q: have you looked for whether men make different atypical
combinations than women? A: No.
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