In The World We Want we explore key themes related to the SDGs: leadership, innovation and youth. Dr. Márcia Balisciano, Global Head of Corporate Responsibility, interviews influencers and thought leaders across disciplines on key topics to achieve the global goals.
Hillis is Senior Technical Advisor at the President's Emergency Plan for AIDS Relief (PEPFAR), and Senior Research Fellow and co-chair of the Global Reference Group on Children Affected by COVID-19 at the University of Oxford, Oxford, UK.
UN's Summit of the Future 2024: Paving the Path for SDG Resources
WE Empower Pitch Night, hosted by Diane von Furstenberg, will be a dynamic evening event bringing together top business leaders, the media and other key influencers to participate in an innovative
The 10th annual HPCC Systems Community Virtual Summit will kick off the week on October 2 with an exciting line up of speakers, sessions and engaging workshops.
As we enter the ninth year since the adoption of the UN Sustainable Development Goals (SDGs) in 2015, the pressing need to meet these ambitious objectives becomes ever more evident.
The chapter advances Goals 5 and 10 by educating mental health providers on how to provide culturally sensitive care to all women.
International Day of the Girl Child 2025: Elevating Girls' Voices for Sustainable Development
As the upcoming generation occupies a larger portion of the workforce, issues around the topic of diversity will only continue to grow in importance. Not only is Generation Z more racially and ethnically diverse than previous generations, but members of this generation are also more likely to expect employers to actively work toward cultivating diversity in the workplace. The recent DIAL Global Diversity Review, co-sponsored by XpertHR, presents comprehensive data on the practices being used to promote diversity in the workplace today. This report addresses various facets of diversity from gender and ethnicity to socioeconomic status and parenthood, promoting SDGs 5, 8 and 10.
This Perspective explores the sources of bias in medical machine learning, and how these can contribute to unequal performance, for example for women. The authors discuss methods for mitigating bias, hopefully leading to more equitable use of machine learning in healthcare.
