Tag Archives: Cornell University

Cornell University study: Faster robots demoralize co-workers

13 Mar

Mojtaba Arvin wrote in the Machine Learning article, The robot that became racist:

AI that learnt from the web finds white-sounding names ‘pleasant’ and …
Humans look to the power of machine learning to make better and more effective decisions.
However, it seems that some algorithms are learning more than just how to recognize patterns – they are being taught how to be as biased as the humans they learn from.
Researchers found that a widely used AI characterizes black-sounding names as ‘unpleasant’, which they believe is a result of our own human prejudice hidden in the data it learns from on the World Wide Web.
Researchers found that a widely used AI characterizes black-sounding names as ‘unpleasant’, which they believe is a result of our own human prejudice hidden in the data it learns from on the World Wide Web
Machine learning has been adopted to make a range of decisions, from approving loans to determining what kind of health insurance, reports Jordan Pearson with Motherboard.
A recent example was reported by Pro Publica in May, when an algorithm used by officials in Florida automatically rated a more seasoned white criminal as being a lower risk of committing a future crime, than a black offender with only misdemeanors on her record.
Now, researchers at Princeton University have reproduced a stockpile of documented human prejudices in an algorithm using text pulled from the internet.
HOW A ROBOT BECAME RACIST
Princeton University conducted a word associate task with the popular algorithm GloVe, an unsupervised AI that uses online text to understand human language.
The team gave the AI words like ‘flowers’ and ‘insects’ to pair with other words that the researchers defined as being ‘pleasant’ or ‘unpleasant’ like ‘family’ or ‘crash’ – which it did successfully.
Then algorithm was given a list of white-sounding names, like Emily and Matt, and black-sounding ones, such as Ebony and Jamal’, which it was prompted to do the same word association.
The AI linked the white-sounding names with ‘pleasant’ and black-sounding names as ‘unpleasant’.
Princeton’s results do not just prove datasets are polluted with prejudices and assumptions, but the algorithms currently being used for researchers are reproducing human’s worst values – racism and assumption… https://www.artificialintelligenceonline.com/19050/the-robot-that-became-racist-ai-that-learnt-from-the-web-finds-white-sounding-names-pleasant-and/

See, The robot that became racist: AI that learnt from the web finds white-sounding names ‘pleasant’ and black-sounding names ‘unpleasant’ http://www.dailymail.co.uk/sciencetech/article-3760795/The-robot-racist-AI-learnt-web-finds-white-sounding-names-pleasant-black-sounding-names-unpleasant.html

Science Daily reported in Faster robots demoralize co-workers:

It’s not whether you win or lose; it’s how hard the robot is working.
A Cornell University-led team has found that when robots are beating humans in contests for cash prizes, people consider themselves less competent and expend slightly less effort — and they tend to dislike the robots.
The study, “Monetary-Incentive Competition Between Humans and Robots: Experimental Results,” brought together behavioral economists and roboticists to explore, for the first time, how a robot’s performance affects humans’ behavior and reactions when they’re competing against each other simultaneously.
Their findings validated behavioral economists’ theories about loss aversion, which predicts that people won’t try as hard when their competitors are doing better, and suggests how workplaces might optimize teams of people and robots working together.
“Humans and machines already share many workplaces, sometimes working on similar or even identical tasks,” said Guy Hoffman, assistant professor in the Sibley School of Mechanical and Aerospace Engineering. Hoffman and Ori Heffetz, associate professor of economics in the Samuel Curtis Johnson Graduate School of Management, are senior authors of the study.
“Think about a cashier working side-by-side with an automatic check-out machine, or someone operating a forklift in a warehouse which also employs delivery robots driving right next to them,” Hoffman said. “While it may be tempting to design such robots for optimal productivity, engineers and managers need to take into consideration how the robots’ performance may affect the human workers’ effort and attitudes toward the robot and even toward themselves. Our research is the first that specifically sheds light on these effects….”
After each round, participants filled out a questionnaire rating the robot’s competence, their own competence and the robot’s likability. The researchers found that as the robot performed better, people rated its competence higher, its likability lower and their own competence lower.
The research was partly supported by the Israel Science Foundation. https://www.sciencedaily.com/releases/2019/03/190311173205.htm

Citation:

Faster robots demoralize co-workers
Date: March 11, 2019
Source: Cornell University
Summary:
New research finds that when robots are beating humans in contests for cash prizes, people consider themselves less competent and expend slightly less effort — and they tend to dislike the robots.

Journal Reference:
Alap Kshirsagar, Bnaya Dreyfuss, Guy Ishai, Ori Heffetz, Guy Hoffman. Monetary-Incentive Competition Between Humans and Robots: Experimental Results. In Proc. of the 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI’19), IEEE, 2019 (forthcoming); [link]

Here is the press release from Cornell University:

PUBLIC RELEASE: 11-MAR-2019

Faster robots demoralize co-workers

CORNELL UNIVERSITY

ITHACA, N.Y. – It’s not whether you win or lose; it’s how hard the robot is working.
A Cornell University-led team has found that when robots are beating humans in contests for cash prizes, people consider themselves less competent and expend slightly less effort – and they tend to dislike the robots.
The study, “Monetary-Incentive Competition Between Humans and Robots: Experimental Results,” brought together behavioral economists and roboticists to explore, for the first time, how a robot’s performance affects humans’ behavior and reactions when they’re competing against each other simultaneously.
Their findings validated behavioral economists’ theories about loss aversion, which predicts that people won’t try as hard when their competitors are doing better, and suggests how workplaces might optimize teams of people and robots working together.
“Humans and machines already share many workplaces, sometimes working on similar or even identical tasks,” said Guy Hoffman, assistant professor in the Sibley School of Mechanical and Aerospace Engineering. Hoffman and Ori Heffetz, associate professor of economics in the Samuel Curtis Johnson Graduate School of Management, are senior authors of the study.
“Think about a cashier working side-by-side with an automatic check-out machine, or someone operating a forklift in a warehouse which also employs delivery robots driving right next to them,” Hoffman said. “While it may be tempting to design such robots for optimal productivity, engineers and managers need to take into consideration how the robots’ performance may affect the human workers’ effort and attitudes toward the robot and even toward themselves. Our research is the first that specifically sheds light on these effects.”
Alap Kshirsagar, a doctoral student in mechanical engineering, is the paper’s first author. In the study, humans competed against a robot in a tedious task – counting the number of times the letter G appears in a string of characters, and then placing a block in the bin corresponding to the number of occurrences. The person’s chance of winning each round was determined by a lottery based on the difference between the human’s and robot’s scores: If their scores were the same, the human had a 50 percent chance of winning the prize, and that likelihood rose or fell depending which participant was doing better.
To make sure competitors were aware of the stakes, the screen indicated their chance of winning at each moment.
After each round, participants filled out a questionnaire rating the robot’s competence, their own competence and the robot’s likability. The researchers found that as the robot performed better, people rated its competence higher, its likability lower and their own competence lower.
###
The research was partly supported by the Israel Science Foundation.
Cornell University has dedicated television and audio studios available for media interviews supporting full HD, ISDN and web-based platforms.
Disclaimer: AAAS and EurekAlert! are not responsible for the accuracy of news releases posted to EurekAlert! by contributing institutions or for the use of any information through the EurekAlert system.

Evan Selinger and Woodrow Hartzog wrote about robots in The dangers of trusting robots.

According to Selinger and Hartzog:

We also need to think long and hard about how information is being stored and shared when it comes to robots that can record our every move. Some recording devices may have been designed for entertainment but can easily be adapted for more nefarious purposes. Take Nixie, the wearable camera that can fly off your wrist at a moment’s notice and take aerial shots around you. It doesn’t take much imagination to see how such technology could be abused.
Most people guard their secrets in the presence of a recording device. But what happens once we get used to a robot around the house, answering our every beck and call? We may be at risk of letting our guard down, treating them as extended members of the family. If the technology around us is able to record and process speech, images and movement – never mind eavesdrop on our juiciest secrets – what will happen to that information? Where will it be stored, who will have access? If our internet history is anything to go by, these details could be worth their weight in gold to advertising companies. If we grow accustomed to having trusted robots integrated into our daily lives, our words and deeds could easily become overly-exposed…. http://www.bbc.com/future/story/20150812-how-to-tell-a-good-robot-from-the-bad

We have to prove that digital manufacturing is inclusive. Then, the true narrative will emerge: Welcome, robots. You’ll help us. But humans are still our future.
Joe Kaeser

Resources:

Artificial Intelligence Will Redesign Healthcare                             https://medicalfuturist.com/artificial-intelligence-will-redesign-healthcare

9 Ways Artificial Intelligence is Affecting the Medical Field https://www.healthcentral.com/slideshow/8-ways-artificial-intelligence-is-affecting-the-medical-field#slide=2

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Cornell University study: Women preferred for tenure-track STEM positions

22 Apr

Many educators have long recognized that the impact of social class affects both education achievement and life chances after completion of education. There are two impacts from diversity, one is to broaden the life experience of the privileged and to raise the expectations of the disadvantaged. Social class matters in not only other societies, but this one as well.
A few years back, the New York Times did a series about social class in America. That series is still relevant. Janny Scott and David Leonhardt’s overview, Shadowy Lines That Still Divide describes the challenges faced by schools trying to overcome the disparity in education. The complete series can be found at Social Class http://www.nytimes.com/2005/05/15/national/class/OVERVIEW-FINAL.html?pagewanted=all&_r=0 and http://www.nytimes.com/2005/05/15/national/class/OVERVIEW-FINAL.html   Jason DeParle reported in the New York Times article, For Poor Strivers, Leap to College Often Ends in a Hard Fall http://www.nytimes.com/2012/12/23/education/poor-students-struggle-as-class-plays-a-greater-role-in-success.html?hpw&_r=0

Social class and background may not only affect an individual student’s choice of major, but their completion of college in that major. Nick De Santis reported in the Chronicle of Higher Education article, Report Examines College Students’ Attrition From STEM Majors:

Twenty-eight percent of bachelor’s-degree students who began their postsecondary education in the 2003-4 academic year chose a major in science, technology, engineering, or mathematics at some point within six years, but 48 percent of students who entered those fields during that period had left them by the spring of 2009, according to a report released on Tuesday by the National Center for Education Statistics, the U.S. Education Department’s statistical arm.
The report, which addresses attrition from the so-called STEM fields, also includes information on students pursuing associate degrees. It says that 20 percent of such students had chosen a STEM major within that six-year period and notes that 69 percent of them had left the STEM fields by the spring of 2009.
Of the students who left STEM fields, the report says, roughly half switched their major to a non-STEM field, and the rest left college without earning a degree or certificate. The report notes that fields such as the humanities and education experienced higher levels of attrition than did the STEM disciplines.
The report identifies several factors associated with a higher probability of switching out of STEM majors, such as taking lighter STEM course loads or less-challenging math classes in the first year, and earning lower grades in STEM courses than in others….
http://chronicle.com/blogs/ticker/report-examines-college-students-attrition-from-stem-majors/69705?cid=pm&utm_source=pm&utm_medium=en

A Cornell University study found that should women remain in STEM programs they might be preferred for tenure-track faculty positions.

Allie Bidwell reported in the U.S. News article, Report: Faculty Prefer Women for Tenure-Track STEM Positions:

In a nationwide study from the Cornell Institute for Women in Science – published Monday in the Proceedings of the National Academy of Sciences – professors Wendy Williams and Stephen Ceci found tenure-track faculty in engineering, economics, biology and psychology fields generally favored hiring female candidates over otherwise identical male candidates by a 2-to-1 margin. A series of five experiments were conducted on 873 faculty members at 371 colleges and universities from all 50 states and the District of Columbia.

The stark underrepresentation of women in math-intensive STEM fields, the authors suggest, is more a result of obstacles at the front end that prevent women from applying for faculty positions in the first place. Meanwhile, it appears gender diversity has become more valued among college faculty…

In the first experiment, the researchers presented the faculty decision-makers with two highly qualified candidates who were equal other than their gender, as well as a third, slightly less-qualified male candidate. Overall, 67.3 percent of faculty ranked the female candidate first, which was consistent across varying lifestyles such as being married or single or having or not having young children.

But other variations showed some lifestyle choices may influence how hiring decisions are made.

A second experiment presented male and female candidates with nonmatching lifestyles: a divorced mother with two young children and an absent ex-spouse competing with a married father with two young children and a stay-at-home wife, for example. In that scenario, female faculty strongly preferred divorced mothers over married fathers (71.4 percent compared with 28.6 percent), while male faculty showed the opposite trend, just not as strongly (42.9 percent compared with 57.1 percent).

When focusing on whether candidates took parental leave during graduate school, male faculty members by a 2-1 margin preferred female candidates who took a one-year leave over those who did not. Male and female faculty showed no preference between male candidates who did or did not take leave, but female faculty members tended to prefer female candidates who did not take leave.

“Women’s perceptions that an extended maternity leave will cause them to be viewed as less committed to their profession may influence some women to opt out entirely,” the study said.
A fourth experiment was conducted to determine whether faculty decision-makers would still rank female candidates higher if they were presented with full CVs, as opposed to narrative summaries with notes from a search committee, and the researchers found similar results. Finally, a fifth experiment presented faculty with one applicant to rate – to see if they would still prefer a female if they couldn’t choose among men and women – and found the faculty members still favored female applicants….

Still, other studies have found evidence of gender bias in STEM related fields.
“When looking at gender bias in science, it’s very important to look at what particular context,” says David Miller, a graduate student at Northwestern University who has studied gender representation in STEM. “The fact there was a preference for female candidates is perhaps not that surprising if you consider many of these faculty hiring boards are looking to diversify their group of faculty. There are other contexts that do show gender bias against females.”

In 2012, Corinne Moss-Racusin, an assistant professor of psychology at Skidmore College, published research that showed strong gender bias in hiring for a lab manager position. Moss-Racusin and her colleagues asked more than 100 STEM professors to assess fictitious resumes that only differed in the name of the applicant (John vs. Jennifer). Despite being otherwise identical in qualifications, the female applicant was seen as less competent – and the scientists were less willing to mentor the candidate or hire her for the position, and recommended paying her a lower salary.

Williams and Ceci argue in an appendix to their study that Moss-Racusin’s research differs from their own because it focuses on biases against female undergraduate students, rather than those who have already earned a doctorate. The results of Moss-Racusin’s study likely doesn’t explain the underrepresentation of women in academia, Williams and Ceci wrote, because few lab managers go on to tenure-track positions later in their careers…. http://www.usnews.com/news/stem-solutions/articles/2015/04/13/report-faculty-prefer-women-for-tenure-track-stem-positions

Citation:

National hiring experiments reveal 2:1 faculty preference for women on STEM tenure track

1. Wendy M. Williams1 and
2. Stephen J. Ceci

Significance

The underrepresentation of women in academic science is typically attributed, both in scientific literature and in the media, to sexist hiring. Here we report five hiring experiments in which faculty evaluated hypothetical female and male applicants, using systematically varied profiles disguising identical scholarship, for assistant professorships in biology, engineering, economics, and psychology. Contrary to prevailing assumptions, men and women faculty members from all four fields preferred female applicants 2:1 over identically qualified males with matching lifestyles (single, married, divorced), with the exception of male economists, who showed no gender preference. Comparing different lifestyles revealed that women preferred divorced mothers to married fathers and that men preferred mothers who took parental leaves to mothers who did not. Our findings, supported by real-world academic hiring data, suggest advantages for women launching academic science careers.

Abstract

National randomized experiments and validation studies were conducted on 873 tenure-track faculty (439 male, 434 female) from biology, engineering, economics, and psychology at 371 universities/colleges from 50 US states and the District of Columbia. In the main experiment, 363 faculty members evaluated narrative summaries describing hypothetical female and male applicants for tenure-track assistant professorships who shared the same lifestyle (e.g., single without children, married with children). Applicants’ profiles were systematically varied to disguise identically rated scholarship; profiles were counterbalanced by gender across faculty to enable between-faculty comparisons of hiring preferences for identically qualified women versus men. Results revealed a 2:1 preference for women by faculty of both genders across both math-intensive and non–math-intensive fields, with the single exception of male economists, who showed no gender preference. Results were replicated using weighted analyses to control for national sample characteristics. In follow-up experiments, 144 faculty evaluated competing applicants with differing lifestyles (e.g., divorced mother vs. married father), and 204 faculty compared same-gender candidates with children, but differing in whether they took 1-y-parental leaves in graduate school. Women preferred divorced mothers to married fathers; men preferred mothers who took leaves to mothers who did not. In two validation studies, 35 engineering faculty provided rankings using full curricula vitae instead of narratives, and 127 faculty rated one applicant rather than choosing from a mixed-gender group; the same preference for women was shown by faculty of both genders. These results suggest it is a propitious time for women launching careers in academic science. Messages to the contrary may discourage women from applying for STEM (science, technology, engineering, mathematics) tenure-track assistant professorships. http://www.pnas.org/content/early/2015/04/08/1418878112

Here is the press release from Cornell University:

April 13, 2015

Women preferred 2:1 over men for STEM faculty positions

By   Ted Boscia

For decades, sexism in higher education has been blamed for blocking women from landing academic positions in STEM (science, technology, engineering and mathematics) fields.
But a new study by Cornell psychologists suggests that era has ended, finding in experiments with professors from 371 colleges and universities across the United States that science and engineering faculty preferred women two-to-one over identically qualified male candidates for assistant professor positions.

Published online April 13 in the Proceedings of the National Academy of Sciences, the paper, “National Hiring Experiments Reveal 2:1 Faculty Preference For Women on STEM Tenure Track,” by Wendy M. Williams, professor of human development, and Stephen J. Ceci, the Helen L. Carr Professor of Developmental Psychology, both in Cornell’s College of Human Ecology, argues that the academic job market has never been better for women Ph.D.s in math-intensive fields.

Williams and Ceci conducted five randomized controlled experiments with 873 tenure-track faculty in all 50 U.S. states to assess gender bias. In three studies, faculty evaluated narrative summaries describing hypothetical male and female applicants for tenure-track assistant professorships in biology, economics, engineering and psychology. In a fourth experiment, engineering faculty evaluated full CVs instead of narratives, and in a fifth study, faculty evaluated one candidate (either a man or identically qualified woman) without comparison to an opposite-gender candidate. Candidates’ personalities were systematically varied to disguise the hypotheses.

The only evidence of bias the authors discovered was in favor of women; faculty in all four disciplines preferred female applicants to male candidates, with the exception of male economists, who showed no gender preference.

In some conditions, Williams and Ceci also matched applicants on job qualifications and lifestyle characteristics such as marital and parental status and used contrasting lifestyles in others. They examined attributes such as being a single mother, having a stay-at-home partner and past choices about taking parental leave. These experiments revealed that female faculty preferred divorced mothers over married fathers and male faculty preferred mothers who took leaves over mothers who did not.

“Efforts to combat formerly widespread sexism in hiring appear to have succeeded,” Williams and Ceci write. “Our data suggest it is an auspicious time to be a talented woman launching a STEM tenure-track academic career, contrary to findings from earlier investigations alleging bias, none of which examined faculty hiring bias against female applicants in the disciplines in which women are underrepresented. Our research suggests that the mechanism resulting in women’s underrepresentation today may lie more on the supply side, in women’s decisions not to apply, than on the demand side, in anti-female bias in hiring.”

“Women struggling with the quandary of how to remain in the academy but still have extended leave time with new children, and debating having children in graduate school versus waiting until tenure, may be heartened to learn that female candidates depicted as taking one-year parental leaves in our study were ranked higher by predominantly male voting faculties than identically qualified mothers who did not take leaves,” the authors continue.

Real-world academic hiring data validate the findings, too. The paper notes recent national census-type studies showing that female Ph.D.s are disproportionately less likely to apply for tenure-track positions, yet when they do they are more likely to be hired, in some science fields approaching the two-to-one ratio revealed by Williams and Ceci.
The authors note that greater gender awareness in the academy and the retirement of older, more sexist faculty may have gradually led to a more welcoming environment for women in academic science.

Despite these successes, Williams and Ceci acknowledge that women face other barriers to entry during adolescence and young adulthood, in graduate school and later in their careers as academic scientists, particularly when balancing motherhood and careers. They are currently analyzing national data on mentorship, authorship decisions and tenure advice, all as a function of gender, to better understand women and men’s decisions to apply to, and persist in, academic science. Ted Boscia is director of communications and media for the College of Human Ecology.
http://www.news.cornell.edu/stories/2015/04/women-preferred-21-over-men-stem-faculty-positions

The Cornell study points to the need for good science education to prepare a diverse population for opportunities. K-12 education must not only prepare students by teaching basic skills, but they must prepare students for training after high school, either college or vocational. There should not only be a solid education foundation established in K-12, but there must be more accurate evaluation of whether individual students are “college ready.”

Related:

Girls and math phobia
https://drwilda.com/2012/01/20/girls-and-math-phobia/

Study: Gender behavior differences lead to higher grades for girls

https://drwilda.com/2013/01/07/study-gender-behavior-differences-lead-to-higher-grades-for-girls/

University of Missouri study: Counting ability predicts future math ability of preschoolers https://drwilda.com/2012/11/15/university-of-missouri-study-counting-ability-predicts-future-math-ability-of-preschoolers/

Is an individualized program more effective in math learning?
https://drwilda.com/2012/10/10/is-an-individualized-program-more-effective-in-math-learning

Where information leads to Hope. © Dr. Wilda.com

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