AI News, The Building Blocks of Artificial Intelligence for Government

Who Will Design the Future?

Lovelace was an imaginative and poetic mathematician, who said that the Analytical Engine “weaves algebraic patterns just as the Jacquard loom weaves flowers and leaves,” and called mathematics “poetical science.” She arrived in the field educated but also unshackled by conventional training, and so was able to envision that this new type of computing machine could be used for far more than just numbers and quantities.

Although many of the current leaders in the AI field have been trained at the most prestigious schools and have earned advanced degrees, most have received virtually no training in the ethical ramifications of creating intelligent machines, largely because such training has not historically been a standard expectation for specialists in the field.

According to Area9 cofounder Ulrik Juul Christensen, “discussion is rapidly moving to the K-12 education system, where the next generation must prepare for a world in which advanced technology such as artificial intelligence and robotics will be the norm and not the novelty.”  Some of the biggest players presently in the AI game are the giant technology companies, such as Google, Facebook, Microsoft, Baidu, Alibaba, Apple, Amazon, Tesla, IBM (which built Watson), and DeepMind (which made AlphaGo and was acquired by Google).

Given the economic trends toward tech monopolies and against government intervention in corporate power consolidation, we have to counter not only by investing in creative AI start-ups, but also by educating the public on how important it is to infuse transparency, teamwork, and inclusive thinking into the development of AI.

With a primarily capitalistic focus on growth, expansion, and profit, the pendulum of public discourse swings away from a deeper understanding of the philosophical and human repercussions of building these tools—topics that researchers and those outside these siloed environments are freer to debate in academic institutions.

The company Unanimous AI uses swarm intelligence technology (sort of like a hive mind) inspired by swarms in nature in order to amplify human wisdom, knowledge, and intuition, optimizing group dynamics to enhance decision-making.

When attending a Recode conference on the impact of digital technology, Microsoft researcher Margaret Mitchell, looking out at the attendees, observed “a sea of dudes.” This lack of representation poses many problems, and one is that the biases of this homogeneous group will become ingrained in the technology it creates.  Generally speaking, in the U.S, 83.3 percent of “high tech” executives are white and 80 percent are male.

The U.S. Equal Employment Opportunity Commission says that amid major economic growth in the high-tech sector, “diversity and inclusion in the tech industry have in many ways gotten worse.” Women currently earn a smaller percentage of computer science degrees than they did almost thirty years ago: “In 2013, only 26 percent of computing professionals were female—down considerably from 35 percent in 1990 and virtually the same as in 1960.”  Jeff Dean, the head of AI Google, said in August 2016 he was more worried about a lack of diversity in AI than he was about an AI dystopia.

The cycle perpetuates itself when training programs are open only to those already in the circle, as opposed to the thousands from underrepresented groups that graduate with degrees in computer science and related disciplines.  As of 2018, Google reported a 69 percent male workforce, with 2.5 percent black and 3.6 percent Latinx employees.

In general, funding for more diverse tech founders and for companies that are not led by white, cisgender men4 is so low that Melinda Gates has said she no longer wants to invest in “white guys in hoodies,” preferring to focus on women- and minority-led initiatives.  The lack of diversity infiltrates every level of technology, including the people providing its financial backing.

More research, data, and action are sorely needed around confronting bias in the field of AI and the complex, structural, and deeply rooted issues surrounding the intersections of gender, ability, race, sexuality, socioeconomic status, discrimination, and power.

Without a far better public understanding of the science, we are not capable as a society of monitoring the companies and platforms, let alone the technology.  Our political leaders don’t know tech and our tech leaders haven’t decoded how to program values or to detect intersecting layers of societal bias.

We have to find ways to bridge this chasm, because our major technological advances will only truly progress through collective intelligence, which requires both human capabilities and evolving machine capabilities working together.  The machines we are training to teach themselves will become determinant in more and more outcomes, so our chance to include diverse voices has a limited horizon.

We will all become virtual humans.” In theory, such escapism is nothing new—as critics of increased TV, Internet, and smartphone usage will tell you—but as VR technology continues to blossom, the worlds that they generate will become increasingly realistic, as Kurzweil explained, creating a greater potential for overuse.

She has worked with the U.N., the U.S. federal government, international corporations, and human rights organizations, and has written about global citizenship, the future of work and purpose, political reconciliation, war crimes, genocide, human and civil rights, humanitarian issues, innovation and design for social impact, and improving access to justice and education. She lives in New York City. A Human Algorithm is her first book.

2. “As people realized how important computer programming was, there was a greater back-lash and an attempt to reclaim it as a male activity,” says Valerie Aurora, the executive director of the Ada Initiative, a nonprofit organization that arranges conferences and training programs to elevate women working in math and science.

5. “Compared to overall private industry, the high tech sector employed a larger share of whites (63.5 percent to 68.5 percent), Asian Americans (5.8 percent to 14 percent) and men (52 percent to 64 percent), and a smaller share of African Americans (14.4 percent to 7.4 percent), Hispanics (13.9 percent to 8 percent), and women (48 percent to 36 percent).” “Diversity in High Tech,” US Equal Opportunity Employment Commission, accessed September 3, 2018,

References George Anders, You Can Do Anything: The Surprising Power of a “Useless” Liberal Arts Education (New York: Little, Brown, and Company, 2017) Amy Rees Anderson, “No Man Is Above Unconscious Gender Bias in The Workplace—It’s ‘Unconscious,’” Forbes, December 14, 2016, Johana Bhuiyan, “The Head of Google’s Brain Team is More Worried About the Lack of Diversity in Artificial Intelligence than an AI Apocalypse,” Recode, August 13, 2016, Danielle Brown, “Google Diversity Annual Report 2018,” accessed September 27, 2018,!#_our-workforce Caroline Bullock, “Attractive, Slavish and at Your Command: Is AI Sexist?” BBC News, December 5, 2016, Ulrik Juul Christensen, “Robotics, AI Put Pressure on K-12 Education to Adapt and Evolve,” The Hill, September 1, 2018, Zachary Cohen, “US Risks Losing Artificial Intelligence Arms Race to China and Russia,” CNN Politics, November 29, 2017, Mark Cuban, “The World’s First Trillionaire Will Be an Artificial Intelligence Entrepreneur,” CNBC, March 13, 2017, Jorge Cueto, “Race and Gender Among Computer Science Majors at Stanford,” Medium, July 13, 2015, Ciarán Daly, “‘We’re in a Diversity Crisis’—This Week in AI,” AI Business, February 15, 2018, Katherine Dempsey, “Democracy Needs a Reboot for the Age of Artificial Intelligence,” Nation, November 8, 2017, James Essinger, Ada’s Algorithm: How Lord Byron’s Daughter Ada Lovelace Launched the Digital Age (Brooklyn: Melville House, 2014).

Plans College for Artificial Intelligence, Backed by $1 Billion,” New York Times, October 15, 2018, John Markoff, “It Started Digital Wheels Turning,” New York Times, November 7, 2011, www Claire Cain Miller, “Overlooked: Ada Lovelace,” New York Times, March 8, 2018, Oliver Milman, “Paris Deal: A Year After Trump Announced US Exit, a Coalition Fights to Fill the Gap,” Guardian, June 1, 2018, Talia Milgrom-Elcott, “STEM Starts Earlier Than You Think,” Forbes, July 24, 2018, Cade Metz, “Google Just Open Sourced Tensorware, its Artificial Intelligence Engine,” Wired, November 9, 2015, Steve O’Hear, “Tech Companies Don’t Want to Talk about the Lack of Disability Reporting,” Techcrunch, November 7, 2016, Erik Sherman, “Report: Disturbing Drop in Women in Computing Field,” Fortune, March 26, 2015, nosedive Sam Shead, “Oxford and Cambridge Are Losing AI Researchers to DeepMind,” Business Insider, November 9, 2016, Jackie Snow, “We’re in a Diversity Crisis: Cofounder of Black in AI on What’s Poisoning Algorithms in Our Lives,” MIT Technology Review, February 14, 2018, Christopher Summerfield, Matt Botvinick, and Demis Hassabis, “AI and Neuroscience,” DeepMind, accessed September 27, 2018, Nicola Perrin and Danil Mikhailov, “Why We Can’t leave AI in the hands of Big Tech,” Guardian, November 3, 2017, Maria Popova, “The Art of Chance-Opportunism in Creativity and Scientific Discovery,” Medium, accessed September 3, 2018,;

Call for Comments: Artificial Intelligence (AI) Primer

As we mentioned in a blog a few months ago, OPSI has been working to develop a “primer” on AI to help public leaders and civil servants navigate the challenges and opportunities associated with the technology to understand how it may help them achieve their missions.

Public Sector Components page, which discusses each country’s complete or forthcoming national AI strategy, or comparable guiding policies that sets forth their strategic vision and approach to AI.

In addition, our colleagues from the Digital Economic Policy Division recently published the Artificial Intelligence in Society report, and colleagues in the Digital Government team and OECD Working Party of Senior Digital Government Officials (E-Leaders) are drafting a working paper on state of the art uses of different kinds of emerging tech (including AI) in governments.

We look forward to hearing your thoughts on the draft and hope that you can help us ensure that this is a helpful tool for helping public servants to better understand AI and how it can be used in government as well as the associated challenges and implications.

Machine learning approaches such as “unsupervised learning”, “supervised learning”, “reinforcement learning”, and “deep learning”, hold significant potential for a variety of tasks, yet each has its own strengths and limitations.

The primer seeks to explain them in a way that provides civil servants with essential details, but doesn’t weigh them down with a level of technical detail that most won’t need.

They are charged with setting national priorities, investments and regulations for AI, but are also in a position to leverage its immense power to innovate and transform the public sector, redefining the ways in which it designs and implements policies and services.

OPSI has identified four primary facets to public sector innovation: AI is exciting because it is a general purpose technology with the potential cut across and touch on the multiple facets of innovation.

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