The document discusses reasons for the underrepresentation of women in science, technology, engineering, and mathematics (STEM) fields. It identifies three key areas that shape girls' and women's achievement and interest in these fields: social and environmental factors, the climate of university STEM departments, and the influence of implicit bias. The document provides research findings on how stereotypes, mindsets, spatial skills training, and work-life balance policies can impact gender disparities in STEM.
Catalant CEO and Co-Founder, Rob Biederman, presented at the Future of Work Austin event in March of 2017. In this presentation, he shares his thoughts on the history of work and what changes we can expect in the coming years. Work is being reimagined; learn how your company can get ahead of this shift.
This is the slides from a webinar I gave to the senate of Universiti Padjajaran, Inodonesia as part of the activities in discussing on AI implications in education at their institution.
Catalant CEO and Co-Founder, Rob Biederman, presented at the Future of Work Austin event in March of 2017. In this presentation, he shares his thoughts on the history of work and what changes we can expect in the coming years. Work is being reimagined; learn how your company can get ahead of this shift.
This is the slides from a webinar I gave to the senate of Universiti Padjajaran, Inodonesia as part of the activities in discussing on AI implications in education at their institution.
Artificial intelligence and Education, Planning education in the AI Era: Lead...eraser Juan José Calderón
Artificial intelligence and Education, Planning education in the AI Era: Lead the leap
Report International conference @UNESCO.
The current report is an exhaustive account of the
discussion and debate at the International Conference
on Artificial Intelligence and Education (hereafter
referred to as ‘the conference’) held in Beijing from
16 to 18 May 2019. Under the overarching theme
of ‘Planning Education in the AI Era: Lead the Leap’,
the conference was structured into seven plenary
sessions and 16 breakout sessions complemented by
a live exhibition and study tours to facilitate forwardlooking debates, share cutting-edge knowledge and
AI solutions, and deliberate on sector-wide strategies.
The executive summary captures the five key areas of
take-aways and seven main trends in AI in education
emerging from the conference discussions
Technology and Humanity, AI and The Future: Bratislava Keynote by Futurist Ge...Gerd Leonhard
Are humans computable? Can AI actually 'think'? What will happen to humans when machines do 'all the work'? This presentation was delivered along with the launch of free Slovak edition of my book Technology vs Humanity see www.techvshuman.com
What regulation for Artificial Intelligence?Nozha Boujemaa
Should we regulate Artificial Intelligence? What are the challenges to face bias in data and algorithms? What is trustworthy AI? AI HLEG (European Commission) and AIGO (OECD) feedback experiences and recommendations. Example in precision medicine: AI/ML for medical devices
This power point pres will be useful for all the budding PhD aspirants who are preparing for their viva irrespective of their subject. Good Luck & All the Best !
URL: https://professionalschool.eitdigital.eu/generative-ai-essentials
Course on Generative Al
Description:
Generative AI is a world-changing power tool that is getting better by the day. So now is the time to get truly inspired, climb up the learning curve, and unleash more of your creative potential.
Learning Topics:
* Inspiration: What is Generative AI in the context of AI's history, present, and future
* Climbing Up: Ways to accelerate your learning trajectory
* Unleashing Creativity: Ways to stay future-ready in the AI era
What You'll Take Away:
By the end of this session, you'll understand the importance of upskilling with today's generative AI tools to get more work done, both faster and at higher quality, as well as some pitfalls to avoid, all within the broader context of the past, present, and future of Artificial Intelligence (AI) and Intelligence Augmentation (IA).
Learning Topics
Inspiration: What is Generative AI in the context of AI's history, present, and future.
Climbing Up: Ways to accelerate your learning trajectory.
Unleashing Creativity: Ways to stay future-ready in the AI era.
Deep dive into ChatGPT's features.
Techniques for basic and advanced prompting and real-world applications.
AI and automation is all the rage nowadays - but what’s the history of these technologies, innovations and ideas?
AI and automation is all the rage nowadays - but what’s the history of these technologies, innovations and ideas? This slides will discuss the brief history of the current interesting technologies and their development to society and mankind.
Why we all need women in tech. Despite of presence and contribution women has made in the industry, the numbers are staggering. This presentation by Vinita Rathi, Director Women Who Code London Chapter, Co-Founder Systango & CodePunt at Digibury Weekender talks about contribution gender diversity can make to the growth of the organisation, how and what women are good at, how motherhood can be boon for the firms they are working at and what can we be done to change.
Technology for everyone - AI ethics and BiasMarion Mulder
Slides from my talk at #ToonTechTalks on 27 september 2018
We all see the great potential AI is bringing us. But is it really bringing it to everyone? How are we ensuring under-represented groups are included and vulnerable people are protected? What to do when our technology is unintended biased and discriminating against certain groups. And what if the data and AI is correct, but the by-effect of it is that some groups are put at risk? All questions we need to think about when we are advancing technology for the benefit of humanity.
Sharing what I've learned from my work in diversity, digital and from following great minds in this field such as Joanna Bryson, Virginia Dignum, Rumman Chowdhury, Juriaan van Diggelen, Valerie Frissen, Catelijne Muller, and many more.
Panel: AI for Social Good - Fairness, Ethics, Accountability, and TransparencyAmazon Web Services
As we begin to harness the power of artificial intelligence, machine learning, and data science in our everyday lives, we also raise complex ethical and social questions associated with bias, fairness, and transparency of algorithmic intelligence. In this panel we get into the thick of the issue. How can we best use AI with shared responsibilities between humans and systems? How can we balance the need for efficiency and exploration with fairness and sensitivity to users? How do we ensure that individuals and communities can trust these systems? Join our discussion to enrich your understanding of human-AI interaction, and how these questions will be answered in AI research, education and policies, as we strive to improve the human condition.
Game changing AI Startups need great AI system. Learn the basic concepts and importance of AI to change the way you build a Startup. Its time to identify and develop skill sets to make better decisions.
Accelerate #AI workloads with Tesla V100 on #E2ECloud : http://bit.ly/E2EGPU
AI Readiness: Five Areas Business Must Prepare for Success in Artificial Inte...Kaleido Insights
This research report from technology research firm, Kaleido Insights introduces a framework for organizational preparedness—not only of data and infrastructure, but of people, ethical, strategic and practical considerations needed to deploy effective and sustainable machine and deep learning programs. This research is the first to market to articulate the need for readiness beyond data and data science talent. Based on extensive research and interviews of more than 25 businesses involved in AI deployments, the report identifies and examines five fundamental areas businesses must prepare for sustainable AI. Download the full report: https://www.kaleidoinsights.com/order-reports/artificial-intelligence-ai-readiness/
Talk presented at the Analytics Frontiers Conference in Charlotte on March 21. The presentation evaluates opportunities and risks of AI and how consumers, businesses, society and governments can mitigate some of the risks.
Responsible AI in Industry (Tutorials at AAAI 2021, FAccT 2021, and WWW 2021)Krishnaram Kenthapadi
[Video available at https://sites.google.com/view/ResponsibleAITutorial]
Artificial Intelligence is increasingly being used in decisions and processes that are critical for individuals, businesses, and society, especially in areas such as hiring, lending, criminal justice, healthcare, and education. Recent ethical challenges and undesirable outcomes associated with AI systems have highlighted the need for regulations, best practices, and practical tools to help data scientists and ML developers build AI systems that are secure, privacy-preserving, transparent, explainable, fair, and accountable – to avoid unintended and potentially harmful consequences and compliance challenges.
In this tutorial, we will present an overview of responsible AI, highlighting model explainability, fairness, and privacy in AI, key regulations/laws, and techniques/tools for providing understanding around AI/ML systems. Then, we will focus on the application of explainability, fairness assessment/unfairness mitigation, and privacy techniques in industry, wherein we present practical challenges/guidelines for using such techniques effectively and lessons learned from deploying models for several web-scale machine learning and data mining applications. We will present case studies across different companies, spanning many industries and application domains. Finally, based on our experiences in industry, we will identify open problems and research directions for the AI community.
As the STEM field continues to grow, more trained professionals are needed to work in areas of science, technology, engineering, and math. There are only a limited number of students, however, who are interested in studying such topics. Find out how KUKA Robotics' KORE Program is providing high school and college students with the opportunity to study robotics.
Post field seminar slides: Strategic Leadership and National Development in N...TANKO AHMED fwc
Think tanks and leadership development institutions across the world aspire to improve statecraft in the face of challenges and identified prospects in governance and development through policy research and executive training. NIPSS was established in 1979 to inculcate knowledge and skills to selected executives across and produce strategic leadership cadre for problem-solving in Nigeria’s national development. The evolution of strategy, leadership, and national development are closely associated with the history, progress, and breakthroughs in countries that were able to scale through the hurdles of human development. This study focuses on the production of strategic leadership for national development in Nigeria with particular attention to the Senior Executive Course of NIPSS.
Artificial Intelligence is increasingly playing an integral role in determining our day-to-day experiences. Moreover, with proliferation of AI based solutions in areas such as hiring, lending, criminal justice, healthcare, and education, the resulting personal and professional implications of AI are far-reaching. The dominant role played by AI models in these domains has led to a growing concern regarding potential bias in these models, and a demand for model transparency and interpretability. In addition, model explainability is a prerequisite for building trust and adoption of AI systems in high stakes domains requiring reliability and safety such as healthcare and automated transportation, and critical industrial applications with significant economic implications such as predictive maintenance, exploration of natural resources, and climate change modeling.
As a consequence, AI researchers and practitioners have focused their attention on explainable AI to help them better trust and understand models at scale. The challenges for the research community include (i) defining model explainability, (ii) formulating explainability tasks for understanding model behavior and developing solutions for these tasks, and finally (iii) designing measures for evaluating the performance of models in explainability tasks.
In this tutorial, we present an overview of model interpretability and explainability in AI, key regulations / laws, and techniques / tools for providing explainability as part of AI/ML systems. Then, we focus on the application of explainability techniques in industry, wherein we present practical challenges / guidelines for effectively using explainability techniques and lessons learned from deploying explainable models for several web-scale machine learning and data mining applications. We present case studies across different companies, spanning application domains such as search & recommendation systems, sales, lending, and fraud detection. Finally, based on our experiences in industry, we identify open problems and research directions for the data mining / machine learning community.
Artificial intelligence and Education, Planning education in the AI Era: Lead...eraser Juan José Calderón
Artificial intelligence and Education, Planning education in the AI Era: Lead the leap
Report International conference @UNESCO.
The current report is an exhaustive account of the
discussion and debate at the International Conference
on Artificial Intelligence and Education (hereafter
referred to as ‘the conference’) held in Beijing from
16 to 18 May 2019. Under the overarching theme
of ‘Planning Education in the AI Era: Lead the Leap’,
the conference was structured into seven plenary
sessions and 16 breakout sessions complemented by
a live exhibition and study tours to facilitate forwardlooking debates, share cutting-edge knowledge and
AI solutions, and deliberate on sector-wide strategies.
The executive summary captures the five key areas of
take-aways and seven main trends in AI in education
emerging from the conference discussions
Technology and Humanity, AI and The Future: Bratislava Keynote by Futurist Ge...Gerd Leonhard
Are humans computable? Can AI actually 'think'? What will happen to humans when machines do 'all the work'? This presentation was delivered along with the launch of free Slovak edition of my book Technology vs Humanity see www.techvshuman.com
What regulation for Artificial Intelligence?Nozha Boujemaa
Should we regulate Artificial Intelligence? What are the challenges to face bias in data and algorithms? What is trustworthy AI? AI HLEG (European Commission) and AIGO (OECD) feedback experiences and recommendations. Example in precision medicine: AI/ML for medical devices
This power point pres will be useful for all the budding PhD aspirants who are preparing for their viva irrespective of their subject. Good Luck & All the Best !
URL: https://professionalschool.eitdigital.eu/generative-ai-essentials
Course on Generative Al
Description:
Generative AI is a world-changing power tool that is getting better by the day. So now is the time to get truly inspired, climb up the learning curve, and unleash more of your creative potential.
Learning Topics:
* Inspiration: What is Generative AI in the context of AI's history, present, and future
* Climbing Up: Ways to accelerate your learning trajectory
* Unleashing Creativity: Ways to stay future-ready in the AI era
What You'll Take Away:
By the end of this session, you'll understand the importance of upskilling with today's generative AI tools to get more work done, both faster and at higher quality, as well as some pitfalls to avoid, all within the broader context of the past, present, and future of Artificial Intelligence (AI) and Intelligence Augmentation (IA).
Learning Topics
Inspiration: What is Generative AI in the context of AI's history, present, and future.
Climbing Up: Ways to accelerate your learning trajectory.
Unleashing Creativity: Ways to stay future-ready in the AI era.
Deep dive into ChatGPT's features.
Techniques for basic and advanced prompting and real-world applications.
AI and automation is all the rage nowadays - but what’s the history of these technologies, innovations and ideas?
AI and automation is all the rage nowadays - but what’s the history of these technologies, innovations and ideas? This slides will discuss the brief history of the current interesting technologies and their development to society and mankind.
Why we all need women in tech. Despite of presence and contribution women has made in the industry, the numbers are staggering. This presentation by Vinita Rathi, Director Women Who Code London Chapter, Co-Founder Systango & CodePunt at Digibury Weekender talks about contribution gender diversity can make to the growth of the organisation, how and what women are good at, how motherhood can be boon for the firms they are working at and what can we be done to change.
Technology for everyone - AI ethics and BiasMarion Mulder
Slides from my talk at #ToonTechTalks on 27 september 2018
We all see the great potential AI is bringing us. But is it really bringing it to everyone? How are we ensuring under-represented groups are included and vulnerable people are protected? What to do when our technology is unintended biased and discriminating against certain groups. And what if the data and AI is correct, but the by-effect of it is that some groups are put at risk? All questions we need to think about when we are advancing technology for the benefit of humanity.
Sharing what I've learned from my work in diversity, digital and from following great minds in this field such as Joanna Bryson, Virginia Dignum, Rumman Chowdhury, Juriaan van Diggelen, Valerie Frissen, Catelijne Muller, and many more.
Panel: AI for Social Good - Fairness, Ethics, Accountability, and TransparencyAmazon Web Services
As we begin to harness the power of artificial intelligence, machine learning, and data science in our everyday lives, we also raise complex ethical and social questions associated with bias, fairness, and transparency of algorithmic intelligence. In this panel we get into the thick of the issue. How can we best use AI with shared responsibilities between humans and systems? How can we balance the need for efficiency and exploration with fairness and sensitivity to users? How do we ensure that individuals and communities can trust these systems? Join our discussion to enrich your understanding of human-AI interaction, and how these questions will be answered in AI research, education and policies, as we strive to improve the human condition.
Game changing AI Startups need great AI system. Learn the basic concepts and importance of AI to change the way you build a Startup. Its time to identify and develop skill sets to make better decisions.
Accelerate #AI workloads with Tesla V100 on #E2ECloud : http://bit.ly/E2EGPU
AI Readiness: Five Areas Business Must Prepare for Success in Artificial Inte...Kaleido Insights
This research report from technology research firm, Kaleido Insights introduces a framework for organizational preparedness—not only of data and infrastructure, but of people, ethical, strategic and practical considerations needed to deploy effective and sustainable machine and deep learning programs. This research is the first to market to articulate the need for readiness beyond data and data science talent. Based on extensive research and interviews of more than 25 businesses involved in AI deployments, the report identifies and examines five fundamental areas businesses must prepare for sustainable AI. Download the full report: https://www.kaleidoinsights.com/order-reports/artificial-intelligence-ai-readiness/
Talk presented at the Analytics Frontiers Conference in Charlotte on March 21. The presentation evaluates opportunities and risks of AI and how consumers, businesses, society and governments can mitigate some of the risks.
Responsible AI in Industry (Tutorials at AAAI 2021, FAccT 2021, and WWW 2021)Krishnaram Kenthapadi
[Video available at https://sites.google.com/view/ResponsibleAITutorial]
Artificial Intelligence is increasingly being used in decisions and processes that are critical for individuals, businesses, and society, especially in areas such as hiring, lending, criminal justice, healthcare, and education. Recent ethical challenges and undesirable outcomes associated with AI systems have highlighted the need for regulations, best practices, and practical tools to help data scientists and ML developers build AI systems that are secure, privacy-preserving, transparent, explainable, fair, and accountable – to avoid unintended and potentially harmful consequences and compliance challenges.
In this tutorial, we will present an overview of responsible AI, highlighting model explainability, fairness, and privacy in AI, key regulations/laws, and techniques/tools for providing understanding around AI/ML systems. Then, we will focus on the application of explainability, fairness assessment/unfairness mitigation, and privacy techniques in industry, wherein we present practical challenges/guidelines for using such techniques effectively and lessons learned from deploying models for several web-scale machine learning and data mining applications. We will present case studies across different companies, spanning many industries and application domains. Finally, based on our experiences in industry, we will identify open problems and research directions for the AI community.
As the STEM field continues to grow, more trained professionals are needed to work in areas of science, technology, engineering, and math. There are only a limited number of students, however, who are interested in studying such topics. Find out how KUKA Robotics' KORE Program is providing high school and college students with the opportunity to study robotics.
Post field seminar slides: Strategic Leadership and National Development in N...TANKO AHMED fwc
Think tanks and leadership development institutions across the world aspire to improve statecraft in the face of challenges and identified prospects in governance and development through policy research and executive training. NIPSS was established in 1979 to inculcate knowledge and skills to selected executives across and produce strategic leadership cadre for problem-solving in Nigeria’s national development. The evolution of strategy, leadership, and national development are closely associated with the history, progress, and breakthroughs in countries that were able to scale through the hurdles of human development. This study focuses on the production of strategic leadership for national development in Nigeria with particular attention to the Senior Executive Course of NIPSS.
Artificial Intelligence is increasingly playing an integral role in determining our day-to-day experiences. Moreover, with proliferation of AI based solutions in areas such as hiring, lending, criminal justice, healthcare, and education, the resulting personal and professional implications of AI are far-reaching. The dominant role played by AI models in these domains has led to a growing concern regarding potential bias in these models, and a demand for model transparency and interpretability. In addition, model explainability is a prerequisite for building trust and adoption of AI systems in high stakes domains requiring reliability and safety such as healthcare and automated transportation, and critical industrial applications with significant economic implications such as predictive maintenance, exploration of natural resources, and climate change modeling.
As a consequence, AI researchers and practitioners have focused their attention on explainable AI to help them better trust and understand models at scale. The challenges for the research community include (i) defining model explainability, (ii) formulating explainability tasks for understanding model behavior and developing solutions for these tasks, and finally (iii) designing measures for evaluating the performance of models in explainability tasks.
In this tutorial, we present an overview of model interpretability and explainability in AI, key regulations / laws, and techniques / tools for providing explainability as part of AI/ML systems. Then, we focus on the application of explainability techniques in industry, wherein we present practical challenges / guidelines for effectively using explainability techniques and lessons learned from deploying explainable models for several web-scale machine learning and data mining applications. We present case studies across different companies, spanning application domains such as search & recommendation systems, sales, lending, and fraud detection. Finally, based on our experiences in industry, we identify open problems and research directions for the data mining / machine learning community.
Presence came together to discuss implicit bias/unconscious bias and how it impacts hiring, retention, and our experiences in the workplace. Lindsay Murdock, Inclusion Strategist discusses why we have bias, history of bias, and actionable items individuals can takeaway to combat their own biases.
In this talk I apply a product thinking approach to think through the problems at the intersection of STEM and gender. In addition, I share my thoughts and discoveries on reimagining and transforming math education.
Three theoretical approaches to gender: Implications for creating effective p...ADVANCE-Purdue
In recent decades, there have been increasing national, state, local and institutional efforts to increase the representation of women in Science, Technology, Engineering and Mathematics (STEM) disciplines and careers. For example, many Colleges and Universities have established programs to recruit female students, and increase female students’ persistence in STEM majors. While some of these programs have been successful in their efforts, a major limitation is that most of the programs do not address the underlying socio-psychological factors that may inhibit the representation of women in STEM disciplines. This presentation provides a discussion of three socio-psychological theoretical perspectives of gender and how these theories highlight some misleading societal explanations for the gender disparity in STEM majors and careers. Specifically, this presentation will discuss the essentialist approach, socialization approach and social constructionist approach, and their implications for creating effective programs to increase the participation of women in the STEM pipeline.
Essentialist perspective holds that behavioral differences between men and women result from innate biological differences that are culturally stable, and not susceptible to the influences of socio-cultural factors. Sex and gender are thought to be “isomorphic” and “conceptualized as stable, innate, bipolar property of individuals” (Howard and Hollander, p. 27). Although the essentialist perspective is no longer considered valid in recent social psychological research, this presentation will highlight some of its underlying assumptions that still prevail in the explanation of gender disparities in STEM. For example, the gender gap in STEM is often explained as a result of the biological and psychological differences between men and women. Men are thought of as “wired” for STEM disciplines while women are viewed as lacking the “intelligent capacity” needed to be successful scientists and engineers.
Socialization perspective holds that gendered behaviors are not biologically determined, but, are learned through a myriad of social learning processes. That is, children learn to be ‘male’ or ‘female’ by observing and imitating their parents and other members of the society, and by internalizing the gender norms of the society. This approach argues that children, through the process of socialization, learn and fulfill the gender stereotypes and role expectations (e.g., career choices) embedded in the norms of their societies. For example, parents are likely to encourage and stir their sons to engineering careers and girls to education and liberal arts. Similarly, boys are viewed as capable of succeeding in STEM careers because the requirements and qualities associated with these professions (e.g., strength and confidence) are consistent with societal views of male roles. This presentation will discuss how gender focused STEM programs can effectively address the effects of the socialization process on the participation of women in STEM careers.
Social constructionist perspective views gendered behaviors as the result of complex and dynamic processes, external to the individual. The basic argument of the social constructionist approach is that gendered behaviors are created by the daily lived experiences of people, the complex interactions between people and by the discourse of a culture. Social constructionists opine that men and women often act in gender-defined ways because they face different societal constraints and expectations. For example, women may not choose full time research careers in STEM disciplines because of other time demanding roles (e.g., parenting and other family responsibilities.) This presentation will provide some insights to how effective programs can address the societal constraints that hinder women’s participation in STEM careers.
In summary, this presentation will examine the impact of each of these
The Kelly Global Workforce Index (KGWI) is an annual global survey that is the largest study of its kind. In 2015, Kelly collected feedback from 164,000 workers across 28 countries across the Americas, EMEA, and APAC regions and a multitude of industries and occupations.
This study is taking a high level look at:
- Work-Life Design as it pertains to the global worker today.
- Women in STEM Talent Gap - a study that at the gap of women talent in STEM – Science, Technology, Engineering and Math – fields.
- Career Management – specifically the emerging trend of do-it-yourself (“DIY”) career development – as it pertains to the global worker seeking to be as resilient as possible in today’s uncertain environment
- Collaborative Work Environment as it pertains to the global worker today.
Here is our second global report on the topic Women in STEM.
Electronic pressure measurement contributes
to the safe, accurate and energy-saving control
of processes. Alongside temperature measurement, it is the most important and most commonly-used technology for monitoring and
controlling plants and machinery
Why are we still failing to attract and retain Women in STEM? why aren't girls learning STEM subjects at school? or entering STEM careers?
This presentation focuses on 3 things we can all do to effect change in the Science, Technology, Engineering and Mathematics fields. Men and women alike - we all have a role to play in creating opportunities and balance.
Brandemix Employer Branding, Marketing and CommunicationsJody Ordioni
Brandemix is a New York-based marketing and communications agency that focuses on employer branding for business results. We create and implement aligned advertising campaigns and interactive solutions that connect talent to cultures – and our strategies are designed to reach and influence your target audience in the most cost-effective ways.
Combining the principles of branding with award-winning creative and the latest trends in marketing and social media, Brandemix turns people into fans, followers, and advocates of your brand.
With the unbelievable success of my previous survey research lecture, I felt it only right to keep going with that theme. This presentaiton is a copy of a guest lecture I recently did for the Clinical Epidemiology course here at The University of Iowa. The slides first talk about some fundamentals of psychmetric measurement like reliability and validity, and then get practical by discussing 5 simple strategies for creating successful survey instruments. Like, favorite, share, comment, enjoy!
NATIONAL FORUM JOURNALS are a group of national and international refereed, blind-reviewed academic journals. NFJ publishes articles academic intellectual diversity, multicultural issues, management, business, administration, issues focusing on colleges, universities, and schools, all aspects of schooling, special education, counseling and addiction, international issues of education, organizational behavior, theory and development, and much more. DR. WILLIAM ALLAN KRITSONIS is Editor-in-Chief (Since 1982). See: www.nationalforum.com
What opportunities are available for girls in STEM careers? How do we increase girls’ awareness, spark their interest, and develop their confidence to pursue careers in STEM? This workshop will demonstrate how STEM disciplines are essential to our health, happiness, and safety, and will provide participants with tips and tools for talking to girls and their parents about opportunities in STEM. Interactive activities seek to reduce anxiety and bolster confidence in the GS leaders. Upon completion of the workshop, participants will be able to advocate careers in STEM to girls and their parents using correct and positive messages that appeal to students’ interests and values.
This workshop was presented as a part of the Girl Scouts STEM Conference, with the goal to create a statewide STEM initiative among the various GS regions. My task was to help the participants “get into the minds of girls,” share opportunities for girls in STEM, dispel stereotypes about STEM, and teach the most effective messages for STEM to girls.
Presented 8 April 2011 in Plano, Texas
Wilson jones, linda graduate females focus v6 n1 2011William Kritsonis
NATIONAL FORUM JOURNALS (Founded 1982 (www.nationalforum.com) is a group of national and international refereed journals. NFJ publishes articles on colleges, universities and schools; management, business and administration; academic scholarship, multicultural issues; schooling; special education; teaching and learning; counseling and addiction; alcohol and drugs; crime and criminology; disparities in health; risk behaviors; international issues; education; organizational theory and behavior; educational leadership and supervision; action and applied research; teacher education; race, gender, society; public school law; philosophy and history; psychology, sociology, and much more. Dr. William Allan Kritsonis, Editor-in-Chief.
Women in Science: numbers, challenges and ways forward. Presentation designed for the Young Women's Leadership Conference at City College of New York, March 20, 2015
Closing the Gender Gap in Engineering - Nov 2010Meagan Pollock
This presentation was designed for Education is Freedom College Counselors. This specific workshop was presented on Nov 30, 2010 by Meagan Ross (mail@meaganross.com).
Abstract:
A ninety minute interactive and engaging session where participants will learn about careers in engineering & the gender gap within this field. Participants will learn that life takes engineering, engineers help shape the future, and engineers are creative and collaborative problem-solvers. We will discuss gender bias in the classroom and how to use this awareness to help reach gender parity in engineering. Upon completion of the workshop, participants will be prepared to advocate careers in engineering to all students, and will have tools to recognize and address gender bias in their environment.
This presentation focuses on women in engineering majors, the challenges they face, and what can be done to encourage more women to enter engineering programs.
This beautiful and artistic piece of research work was presented in a webinar by YOUNG INNOVATORS Engineering Research Institute, India. This explains the major stereotypes, barriers, challenges for women, and their solution. The research work presented is based on a practical analysis of a girl's life and reasons to find herself less confident. Please share your views also.
Feel free to contact:
[haq.mairaj@hotmail.com] [mehakazeem@ieee.org]
Strategic Equity, Diversity and Inclusion (EDI) Leadership AssessmentKevin Carter
The Strategic Equity, Diversity, and Inclusion (EDI) Leadership
Assessment is both a supplement to cultural competence coaching and training, as well as a stand-alone document that will enhance your ability to achieve personal, team,
and organizational success.
For all of us, the challenge and opportunity are to grow, from where you are now to a deeper understanding of how to best deliver the organization's brand to colleagues, customers, business partners, and the community.
Keep an open mind. Enjoy the journey. Let's begin!
ManToMan #MeToo Session: Being the Imperfect AllyKevin Carter
The "ManToMan #MeToo Session: Being the Imperfect Ally" is a 90-minutes to 1/2 day session that creates an empathetic space for authentic dialogue and provides a relationship based model for men and women to gain congruence on intentions, behaviors, and impact that represents #MeToo progress in the workplace.
On Behalf of Usher's New Look Urban Game Jam Event May 21stKevin Carter
On behalf of Usher’s New Look, we would like to invite you to be our special guest at the SparkLab University videogame workshop. The workshop will feature Urban Game Jam
presented by Entertainment Arts Research Inc.
The event will include a presentation from world renowned scientist Dr. Thomas Mensah, and executives from Microsoft and TVOne. The attendees will also include the CEO from Game Builder Studios and other digital media companies.
Diversity, Inclusion & Innovation - Strategic Leadership AssessmentKevin Carter
The Diversity, Inclusion & Innovation - Strategic Leadership Assessment has been updated. Please obtain a free assessment here http://www.inclusioninnovates.com/d-i-assessment-tool.
Network of Executive Women: The NEW Male Champion blog post series (downloa...Kevin Carter
The slide share contains the three blog posts to date from the Network of Executive Women (NEW) Leadership Summit "The NEW Male Champion" and "Engaging Men Who Get It" workshops.
The articles are entitled:
> The NEW inclusion of men which summaries workshop 1
>NEW male engagement in women's advancement which summaries workshop 2 and
> Intercultural Competence and Women's Advancement which highlights the use of the Intercultural Development Inventory (IDI) to accelerate women's advancement
Intercultural Competence and Women's AdvancementKevin Carter
Slide depicts how a growth along the intercultural development continuum as measured by the intercultural development inventory (IDI) can foster women engagement and advancement.
Network of Executive Women (NEW) The NEW Male Champion Report-OutKevin Carter
Network of Executive Women (NEW) The NEW Male Champion Report-Out
Congratulations to the men and women who developed and updated the report-out from "The NEW Male Champion" session at the Network of Executive Women (NEW) Leadership Summit.
Please provide any additional feedback and thoughts!
Kevin A Carter NEW Leadership Summit 2015 Planning GuideKevin Carter
The Summit Planning Guide is being used during the Network of Executive Women (NEW) Leadership Summit in Dallas, Texas, September 30 to October 2 to help participants better themselves, advance women and aid their organizations.
My Guide is attached to assist participants of two sessions I will facilitate, "The NEW Male Champion" and "Engaging Men Who Get It," complete their Guides.
Disruptive Inclusion _ Kevin Carter BiographyKevin Carter
Disruptive Inclusion is about proactive recognition and embracing of both the similarities and differences in people with a clear objective of improving organizational environments, individual motivation, and ultimately business innovation. Disruptive Inclusion is a conscious and deliberate effort to inject and foster difference into a team work environment for business results.
Technology Adoption Lifecycle meets Intercultural Competence ContinuumKevin Carter
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2. Why So Few? Women in Science, Technology, Engineering, and Mathematics
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4. Women are underrepresented in many science and engineering occupations. Percentage of Employed STEM Professionals Who Are Women, Selected Professions, 2008 Source: U.S. Department of Labor, Bureau of Labor Statistics, 2009, Women in the labor force: A databook (Report 1018) (Washington, DC), Table 11.
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6. Girls’ achievements and interests in math and science are shaped by the environment around them.
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12. The climate of science and engineering departments at colleges and universities is especially important for women—both students and faculty.
18. Why So Few? Women in Science, Technology, Engineering, and Mathematics To download the report: www.aauw.org To contact the researchers: [email_address]
Editor's Notes
Supporting women and girls in science, technology, engineering and mathematics has been a part of AAUW’s mission since its founding in 1881. Throughout its history, AAUW has encouraged women to study and work in these traditionally male fields, investing millions of dollars in graduate fellowships and grants and engaging in research, programming and advocacy to break through the barriers for women in science, technology, engineering and mathematics. Today, I am pleased to share with you AAUW’s latest report Why So Few? Women in Science, Technology, Engineering, and Mathematics. On the cover of the report is a picture of a former AAUW fellow, Esther Ngumbi.
AAUW thanks the National Science Foundation for a grant to conduct this study. We also thank the Letitia Corum Memorial Fund and the Mooneen Lecce Giving Circle for generous contributions to this project. The Eleanor Roosevelt Fund which supports all of AAUW research activity is also an important funder. Together, these contributors made the research and production of this report possible.
Women have made tremendous progress in education and the workplace during the past 50 years, including in scientific and engineering fields. However, women are underrepresented in many science and engineering occupations. This chart shows the percentage of women in selected STEM professions, and although women make up more than half of working biological scientists, they make up less than 7% of mechanical engineers.
Based on interviews with top researchers and a review of the large body of academic research literature on gender and science, Why So Few? presents 8 separate research findings on the nurture side of the nature-nurture debate. Each of these findings demonstrates that social and environmental factors clearly contribute to the underrepresentation of women in science and engineering. The research findings are organized into three areas: How social and environmental factors shape girls’ achievements and interest in math and science; How the climate of university science and engineering departments affect women’s –both students and faculty - experience in STEM fields; and The continuing role of bias in limiting women’s success in STEM in education and the workplace. [OPTIONAL] We all know that there are biological differences between men and women, but as yet, there is no clear link between any of these differences and the underrepresentation of women in science and engineering. In contrast, we have a lot of evidence that culture can make a difference. For example, an ongoing study of mathematically precocious youth finds that thirty years ago there were 13 boys for every girl who scored above 700 on the SAT math exam at age 13; today that ratio has shrunk to about 3:1. So that’s a shift from 13:1 boys to girls to 3:1 boys to girls in just 30 years. This rapid rise in the number of girls identified as “mathematically gifted” suggests that culture, specifically how we cultivate math and science achievement in girls, makes a difference in girls’ achievement in these areas.
I will first describe the findings presented in the report that show how girls’ achievements and interest in math and science are shaped by social and environmental factors.
The first finding is research by Dr. Joshua Aronson, a psychologist at New York University that shows that negative stereotypes about girls’ and women’s abilities in math and science persist and can adversely affect their performance in these fields through a phenomenon known as stereotype threat. Stereotype threat arises in situations where a person fears that their performance will be evaluated based on a negative stereotype. For example, a female student taking a difficult math test might experience an extra cognitive and emotional burden of worry that if she performs poorly her performance will reinforce and confirm the stereotype that women are not good at math. This added burden of worry can adversely affect her performance. [Explain chart.] [Explanation of chart: In one experiment, researchers gave a math test to female and male college students with similar math abilities . Half the group was told that men generally perform better than women on the test (the “stereotype threat” condition) and the other half was told that there were no gender differences (the “no stereotype threat” condition). The results are shown in this figure. Women performed significantly worse than men in the “threat” situation, but women and men performed equally well in the non- threat condition. ] If gender differences in performance were due to innate gender differences in math, then women would perform worse than men even in the no stereotype threat condition. This result has been shown hundreds of times in other experiments, not just with gender but with race and ethnicity as well. Fortunately, because stereotype threat is linked to the learning environment there are some simple ways to lessen its negative impact by changing the environment. These include: Recommendations Exposing girls to successful role models in math and science to combat the negative stereotype, and Explicitly talking to students about stereotype threat has resulted in improved performance.
The next finding addresses beliefs about intelligence. Believing in the potential for intellectual growth, in and of itself, improves outcomes. The research of Carol Dweck, a psychologist at Stanford University, provides evidence that a “growth mindset” as opposed to a “fixed mindset” is likely to lead to greater persistence in the face of adversity and ultimately success in any realm. The table shown here lays out the differences between a fixed mindset and a growth mindset. Individuals with a fixed mindset believe that intelligence is static and inborn. In contrast, individuals with a growth mindset believe that intelligence can be developed through effort. Individuals with a fixed mindset are susceptible to a loss of confidence when they encounter challenges because they believe that if they are truly “smart,” things will come easily to them. If they have to work hard at something, they tend to question their abilities and lose confidence, and they are likely to give up because they believe they are “not good” at the task and, because their intelligence is fixed, will never be good at it. Individuals with a growth mindset, on the other hand, show a far greater belief in the power of effort, and in the face of difficulty, their confidence actually grows because they believe they are learning and getting smarter as a result of challenging themselves. These research findings are especially important for women in science and engineering, because encountering obstacles and challenging problems is in the nature of scientific work. When girls and women believe they have a fixed amount of intelligence, they are more likely to lose confidence and disengage from science and engineering when they inevitably encounter difficulties in their course work. This is true for all students, but it is particularly relevant for girls in STEM subjects, where negative stereotypes persist about girls’ abilities. There are a number of steps we can take to foster a growth mindset in children: Recommendations Parents and teachers should teach children that intellectual skills can be acquired. When girls are taught that their intelligence can expand with experience and learning, girls do better on math tests and are more likely to want to continue to study math in the future. Praise children for effort Rather than saying “Oh, you’re so smart!”, when children do something well, say “Wow, you worked really hard at that and you did it!” It is especially important to praise the most able students for their effort. These students have often coasted along, gotten good grades, and been praised for their intelligence and may be the very students who opt out when the work becomes more difficult. Highlight the struggle. Parents and teachers can communicate to students that we value and admire effort and hard work. This will teach children the values that are at the heart of scientific and mathematical contributions: love of challenge, love of hard work, and the ability to embrace and learn from our inevitable mistakes. Talented and gifted programs should send the message that they value growth and learning, not just being “gifted” with intelligence.
Another finding presented in the report is in the area of spatial skills. One of the largest gender differences in cognitive abilities is found in the area of spatial skills, with boys and men consistently outperforming girls and women, especially on measures of mental rotation, an example of which is shown here. See if you can answer this question. Does anyone want to volunteer what the answer is? D. Spatial skills are considered by many people to be important for success in engineering and other scientific fields and are often considered to be “innate”. Research conducted by Sheryl Sorby over a decade with first year engineering students at Michigan Tech, however, documents that individuals’ spatial skills consistently improve dramatically in a short time with a simple training course. If girls grow up in an environment with opportunities to develop their spatial skills, they are more likely to consider a future in a science or engineering field. Recommendation Playing with building toys as well as drawing can help children develop their spatial skills.
The final research finding profiled in the report on how social and environmental factors affect girls’ achievement and interest in science and math is by sociologist Shelley Correll at Stanford University. Dr. Correll’s research finds that women are “harder on themselves” compared to their male peers when assessing their abilities in math and science. Dr. Correll first became interested in gender differences in self-assessment when she taught chemistry to high school students. She realized that no matter how well the girls in her classes did, she had trouble convincing them that they had any scientific ability. At the same time, she found that no matter how poorly the boys in her classes did, they continued to believe that they were very good at chemistry. Once she went to graduate school, she delved into this issue, analyzing a dataset of over 16,000 high school students, and found that, in fact, girls do assess their mathematical abilities lower than boys with similar past mathematical achievements. In a lab experiment on gender differences in self-assessment, Dr. Correll found that women assess themselves as less competent in “male” fields, even when the “male” field is fictitious. Here we have an example from this experiment. See if you can answer this question: Does this rectangle have more black or more white? [Pause] We won’t spend too much time here because it’s not actually important how much black or white there is, but what the results of the study showed. The answer is that there are equal amounts of black and white in the rectangle. In Dr. Correll’s experiment, she identified this fictitious ability to detect correct proportions of black and white as “contrast-sensitivity ability”. When participants were told that men were more likely to have high levels of “contrast-sensitivity ability”, women assessed their contrast-sensitivity ability lower than men did. When this ability was described as equally strong in men and women, gender differences in self-assessment were not found.
This gender difference in self-assessment is shown here in the chart on the left. [Explain chart well.] [Explanation of chart: The chart shows women’s self-assessments in green and men’s self-assessments in purple. When subjects were told that men are better at this task, men assessed their “contrast-sensitivity” abilities much higher than women. When subjects were told that there is no gender difference in performing this task, however, there was essentially no difference between how men and women assessed their abilities.] At the same time, girls held themselves to a higher standard than boys when told that men are better at “contrast-sensitivity” but men and women’s standards were nearly identical when told that there is no gender difference. This difference in standard is shown here in the chart on the right. [Explain chart.] [Explanation of chart: The chart shows students’ standards for their own performance. Women’s standards are in green and men’s standards in purple. When subjects were told that men are better at this task and then asked “how high would you have to score to believe that you have high ability in this area”, women said they would have to score around 89%. Men, in contrast, said they would have to score around 79%. This is a full 10 percentage point difference! When subjects were told that there is no gender difference in performing this task, however, there was essentially no difference between the standard that men and women held themselves to.] If you think about this finding as it relates to math and science, fields in which men are considered to excel, it suggests that girls believe that they have to be better in math and science than boys believe they have to be in order to think of themselves as good in these fields. There are many elements to choosing a career, but researchers agree that one element is believing that you can be successful at it. Girls’ lower self-assessment of their math ability, even in the face of good grades and test scores, along with their higher standard for performance in “masculine” fields, helps explain why fewer girls than boys aspire to science and engineering careers. So what can be done to reduce gender differences in self-assessment? Recommendations First, as many of you know, extremely low average test scores are common in many college science and engineering courses. Low scores increase uncertainty in all students, but they have a more negative effect on students who already feel like they don’t belong, as many women in science and engineering majors do. The same letter or number grade on an assignment or exam might signal something different to girls than it does to boys. Female students may need to be reminded that a B in a difficult course is a grade to be proud of. The more that teachers and professors can reduce uncertainty about students’ performance, the better. And second, girls are less likely than boys to interpret their academic successes in math and science as an indication that they have the skills necessary to become a successful engineer or computer scientist. Encourage girls to see their success in high school math and science for what it is: not just a requirement for going to college but also an indication that they have the skills to succeed in a whole range of science and engineering professions.
The second theme that comes out of the research is that the climate and culture in science and engineering departments at colleges and universities is especially important for female students and faculty.
As you can see from the chart shown here, among first year college students, women are less likely than men to say that they are interested in majoring in science, technology, engineering or math. The difference is most pronounced in engineering (shown in green) and computer science (shown in red). However, women are more likely to major in the biological/agricultural sciences. Yet this does not mean that colleges and universities are off the hook when it comes to increasing the number of women in STEM majors. Although fewer women than men come to college with the intention of pursuing a STEM field, two different research projects profiled in the report find that small changes to improve the climate of STEM departments in colleges and universities can make a big difference in attracting and retaining female students. Research by Barbara Whitten comparing “successful” physics departments (those where women were 40% of graduates) to more “typical” physics departments (those where women were about 20% of the graduates) along with research by Jane Margolis and Alan Fisher studying attrition and women in computer science at Carnegie Mellon University found that small changes in recruitment, admissions, the curriculum for instance can help to improve the climate of departments, and therefore, help to attract and keep female students. They recommend that departments that want to attract and retain diverse and talented students should: Actively recruit female students. This may seem obvious, but many departments don’t actively recruit students, they simply wait for students to come to them. They also encourage departments to offer introductory courses that emphasize the broad applications of science and technology and not focus only on the technical aspects of the subjects. This approach has been found to be helpful for attracting both male and female students, but especially female students. Third, admissions policies that require experience that will be taught in the curriculum (for example, requiring computer science major applicants to have significant prior computer programming experience when computer programming will be taught to students once they are admitted) may weed out potentially successful students, especially women. Revising admissions policies to send a more inclusive message about who can be successful in a STEM majors can help departments recruit more qualified, capable women.
The second finding on college climate looks at female faculty in STEM. [Explain chart] [Explanation of chart: This chart shows the percentage of tenured and non-tenured faculty who are women in selected STEM fields. First, it shows that women make up a smaller share of faculty in engineering, the physical sciences and computer and information sciences compared to the biological/life sciences (which is shown on the bottom of the graph). The second important trend we see here is that women make up a far smaller share of the tenured faculty in all these fields. This is significant because tenured positions are the more secure, high-paying and high-status positions in higher education.] Overall, there are fewer women in tenured positions in STEM fields than one would expect given the number of women earning PhDs in these fields. In the report, research by Cathy Trower and the Collaborative on Academic Careers in Higher Education at Harvard University is presented. Dr. Trower and her colleagues found that a departmental climate was related to job satisfaction among both female and male faculty in STEM departments, but that women were less satisfied than their male colleagues with departmental climate, specifically their sense of “fit” or feeling like they belonged in their departments. Therefore, Trower recommends that: STEM departments in colleges and universities focus on “fit” to improve female faculty satisfaction. They can do this by: Providing mentoring for all junior faculty and Implementing effective work-life policies to support all faculty but especially women who often are responsible for the majority of care taking and household duties.
The third theme that comes out of our review of the literature is that bias, often unconscious, continues to limit women’s progress in scientific and engineering fields.
Research by Mahzarin Banaji, a former AAUW fellow, and her colleagues at Harvard University shows that even individuals who consciously reject negative stereotypes about women in science often still believe that science is better suited to men at an unconscious level. These unconscious beliefs or implicit biases may be more powerful than explicitly held beliefs and values simply because we are not aware of them. Banaji is a co-developer of the implicit association test (IAT) which anyone can take to learn more about their biases. The test is freely available online and is anonymous. Since the gender-science implicit association test was established in 1998, more than a half million people from around the world have taken it, and more than 70 percent of test takers more readily associated “male” with science and “female” with arts than the reverse. These tests are not an indication of what a person consciously believes, but rather an indication of what goes on unconsciously. Implicit bias may influence girls’ likelihood of identifying with and participating in math and science and contributes to bias in science and engineering fields in education and the workplace – even among people who support gender equity. So what can be done to combat these biases? Recommendations First, you can learn more about your implicit bias by taking the tests at the website shown here. And second, if you find that you do have biases (and most people do), you can take steps to address them. Simple steps such as learning more about female scientists and engineers, and having positive images of women in science in your office, classrooms and homes can help “reset” your biases.
And finally, the report presents research showing that not only do most people associate math and science with “male,” they often hold negative opinions of women in “masculine” jobs or positions, like scientists or engineers. This research by Madeline Heilman at New York University shows that people judge women to be less competent than men in “masculine” jobs unless women are clearly successful in their work. When a woman is clearly competent in a masculine job, she is considered to be less likable. Because both likability and competence are needed for success in the workplace, women in science and engineering fields can find themselves in a double bind. So what can be done? Recommendations First, raising awareness about bias against women in science and engineering is one step we can take to counteract it. Once men and women in science and engineering fields are aware that bias exists in these areas, they can work to interrupt the unconscious thought processes that lead to bias. For women in particular, knowing that gender bias exists in science and engineering fields can help them understand that if they encounter social disapproval, it is likely not personal. Additionally, research shows that clear criteria for success and transparent evaluation processes are helpful for anyone subject to bias, including women in science and engineering fields.
So Why So Few? The answer is all around us. Social and environmental factors influence us at home, at school and in the workplace. This report provides concrete recommendations based on recent research findings for what each of us can do to change our world to more fully open opportunities for girls and women in science and engineering fields. Like all AAUW research reports, this report will be influential only if we all help spread the word. Please share these findings with: Parents Teachers School principals PTAs Afterschool groups College Administrators and faculty Employers And others I encourage you to visit www.aauw.org to download the report for free by clicking on the “Research” tab or order a printed copy by clicking on “Shop AAUW”. Thank you very much.