I need a two page executive summary for the attached case study.
Responsible A.I.: Tackling Tech's Largest Corporate Governance Challenges Date: October 1, 2022 KELLIE MCELHANEY GENEVIEVE SMITH ISHITA RUSTAGI OLAF GROTH Responsible A.I.: Tackling Tech’s Largest Corporate Governance Challenges AI has the potential to improve billions of lives…By ensuring it is developed responsibly in a way that benefits everyone, we can inspire future generations to believe in the power of technology. —SUNDAR PICHAI CEO, ALPHABET INC., GOOGLE1 The American dream for one Charlotte, North Carolina, family was a new four-bedroom home with a lawn, 2,700 square feet of living space, and a neighborhood pool for $375,000. Crystal Marie and Eskias McDaniels saved more than they needed for down payment, had very good credit, and easily prequalified for a mortgage. However, on the August 2019 day when they were scheduled to sign the loan documents, their loan officer told them the deal wouldn’t close. He had submitted it at least 15 times, and noted that each one got “rejected by an algorithm.” Crystal Marie said as a Black couple, “it would be really naive to not consider that race played a role in the process.” An investigation found that lenders in 2019—often using algorithms—were more likely to deny loans to people of color than to similar White applicants, even when controlling for financial factors the mortgage industry uses to explain racial disparities in lending.2 1 Pichai, S. (2020). Why Google thinks we need to regulate AI. Financial Times. Retrieved from https://www.ft.com/content/3467659a-386d-11ea-ac3c-f68c10993b04. 2 Martinez, E. & Kirchner, L. (2021, August 25). The secret bias hiding in mortgage-approval algorithms. ABC News. https://abcnews.go.com/Business/wireStory/secret-bias-hidden-mortgage-approval-algorithms-79633917. Associate Director of the Berkeley Haas Center for Equity, Gender & Leadership (EGAL), Genevieve Smith, and EGAL Analyst, Ishita Rustagi, prepared this case study with EGAL’s Founding Director, Kellie McElhaney and Professor Olaf Groth. We would like to give special thanks to the following individuals who provided critical insights at Google: Melissa Davison, Madeleine Elish, Jen Gennai, and Reena Jana. Copyright © 2022 by The Regents of the University of California. All rights reserved. No part of this publication may be reproduced, stored, or transmitted in any form or by any means without the express written permission of the Berkeley Haas Case Series. This document is authorized for use only in Jose Curto's EMBA-EN_Abr2022_ELECTIVE - Becoming a data driven organization -- IST at IE Business School from Feb 2023 to Sep 2023. https://abcnews.go.com/Business/wireStory/secret-bias-hidden-mortgage-approval-algorithms-79633917 https://www.ft.com/content/3467659a-386d-11ea-ac3c-f68c10993b04 RESPONSIBLE AI • GOOGLE 2 As of 2022, AI technology using machine learning is being implemented across industries and business functions. Such stories about bias and discrimination, being perpetuated by these tools— whether in finance, healthcare, policing, hiring, and more—are common. Yet development and adoption of AI systems have continued to increase due to rapid technological advancements, the promise of increased efficiency and productivity, and the immense profit potential of AI technologies. In 2017, the CEO of Google, Sundar Pichai, announced the company’s key conceptual shift from “mobile first” to “AI first.”3 This marked a major inflection point for Google. As he said AI, “touches every single one of our main projects, ranging from Search to Photos to Ads… everything we do!”4 Pichai saw the challenges that companies faced in ensuring ethical use of AI technologies and recognized the importance of using AI in responsible ways. Thus he set a new goal of defining an ethical AI charter for the company. Shortly after, Google—alongside other leading tech companies—adopted responsible AI principles to guide its development and use and invested millions in teams, resources, and tools to operationalize the principles. Implementation would be incredibly challenging. What does responsible AI innovation and corporate governance look like? How can business leaders at Google and elsewhere address the challenges and tradeoffs that exist to ensure AI technologies are trusted and responsible? Specifically: How can Google Cloud’s Responsible AI team assess applying time- to-market objectives, ethics safeguards, and multi-stakeholder processes to a new lending tool? Background Rapid Development & the Promise of AI Companies around the world have increasingly developed AI technologies: In a survey of US companies in 2021, 86% of respondents said AI would be a “mainstream technology” at their company that year, contributing up to US$15.7 trillion to the global economy by 2030.5 6 When companies deploy AI technologies, it is often machine learning. Machine learning systems—made up of a series of algorithms—take and learn from massive amounts of data to find patterns and make predictions.7 In 2022, AI that uses machine learning impacts most aspects of many people’s work and personal lives. It can be used in daily tasks from travel navigation to weather forecasts. It can also be used to decide, for example, who receives an interview for a job; which products are advertised to which consumers; who receives a loan; what communities are designated as having high potential for crime; which COVID-19 patients in hospitals receive life-saving resources. AI can help people 3 Zerega, B. (2017, May 19). AI weekly: Google shifts from mobile-first to Ai-First World. VentureBeat. Retrieved from https://venturebeat.com/2017/05/18/ai-weekly-google-shifts-from-mobile-first-to-ai-first-world/ 4 Chainey, R. (2017). Google co-founder Sergey Brin: I didn’t see AI coming. World Economic Forum. https://www.weforum.org/agenda/2017/01/google-sergey-brin-i-didn-t-see-ai-coming/. 5 (2021). AI predictions 2021. PwC. Retrieved from https://www.pwc.com/us/en/tech-effect/ai-analytics/ai- predictions.html. 6 (2017). Sizing the prize: What’s the real value of AI for your business and how can you capitalise? PwC. Retrieved from https://www.pwc.com/gx/en/issues/analytics/assets/pwc-ai-analysis-sizing-the-prize-report.pdf. 7 (2021). Machine learning, explained. MIT Sloan Management Review. Retrieved from https://mitsloan.mit.edu/ideas- made-to-matter/machine-learning-explained. This document is authorized for use only in Jose Curto's EMBA-EN_Abr2022_ELECTIVE - Becoming a data driven organization -- IST at IE Business School from Feb 2023 to Sep 2023. https://mitsloan.mit.edu/ideas https://www.pwc.com/gx/en/issues/analytics/assets/pwc-ai-analysis-sizing-the-prize-report.pdf https://www.pwc.com/us/en/tech-effect/ai-analytics/ai https://www.weforum.org/agenda/2017/01/google-sergey-brin-i-didn-t-see-ai-coming https://venturebeat.com/2017/05/18/ai-weekly-google-shifts-from-mobile-first-to-ai-first-world RESPONSIBLE AI • GOOGLE 3 make decisions more efficiently and cost-effectively, while also promoting higher productivity and growth in the economy. Use of AI in predictions and decision making can also reduce human subjectivity and open new possibilities and opportunities. However, AI can also embed human biases, produce discriminatory outcomes at scale, and pose immense risk to individuals and society.8 Beyond social benefits and risks, there are clear business reasons to address ethical concerns when operationalizing AI principles. A 2018 Deloitte survey found that 32% of AI-aware executives ranked ethical risks of AI as a top three AI-related concern.9 Microsoft flagged reputational harm or liability due to biased AI systems as a risk to their business in a 2020 report to the US Securities and Exchange Commission.10 Meanwhile, employees have spoken out on various ethical concerns related to AI research and development in the form of walkouts, resignations, and new unions. Responsible AI is important for more than just large companies. Venture capitalists have spurred start-ups to enhance their approach to responsible and ethical AI.11 Businesses can struggle to generate ROI from their AI projects and pilots.12 However, a global 2021 McKinsey survey found that AI’s impact on the bottom line is growing: 27% of respondents reported that at least 5% of earnings before interest and taxes (EBIT) is attributable to AI (up from 22% of respondents in 2020). Regardless