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Emerging Challenges in Securing Frontier AI Systems

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This article is based on a presentation at the Fall 2023 Institute of Business Analytics Conference on Generative AI and Cybersecurity: Navigating the New Era of Threats and Safeguards, by Mikel Rodriguez, leader of the AI Red Team at Google DeepMind.

For the past 20 years, if someone wanted to interact with machine learning (ML) systems, they would have to go to a state-of-the-art lab. Today, anyone who is interested in ML can pull out their phone and easily interact with these interfaces.

“The world is accelerating so quickly,” says Mikel Rodriguez, who leads AI assurance at Google DeepMind. “There isn’t a week that goes by without some interesting new development in artificial intelligence capabilities.” (more…)

Security and Responsibility in AI: Red Teaming in the Age of AI

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This article is based on a presentation at the Fall 2023 Institute of Business Analytics Conference on Generative AI and Cybersecurity: Navigating the New Era of Threats and Safeguards. by Tori Westerhoff, senior manager of strategy and business planning for mixed reality on the Microsoft AI Red Team

Amid the bustle of innovation in artificial intelligence, thought leaders in both the private and public sectors are more clearly seeing the importance of security measures to make sure AI technology is as powerful as possible without doing harm.

To that end, in late 2023, the Biden Administration issued a landmark Executive Order that established unprecedented standards for AI safety and security. The Executive Order was designed to more carefully regulate the development of trustworthy technology and to protect privacy while promoting innovation and competition in the AI landscape.

Alongside these efforts, companies like Microsoft are finding new ways to stay ahead of the prolific acceleration of the technology with security processes like red teaming.

Tori Westerhoff, senior manager of strategy and business planning for mixed reality on the Microsoft AI Red Team, has found that the awareness of the entire risk landscape for AI is generally low.

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Does Fractional Trading Fuel “Meme Stock” Trading Frenzies and Bubbles?

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This article is based the research of Kelley Professor of Finance and Richard E. Jacobs Chair in Finance, Vivian Fang. This work was co-authored by Zhi Da (University of Notre Dame) and Wenwei Lin (Chinese University of Hong Kong).

 

Introduction

Retail trading has sharply increased in U.S. equity markets in recent years—thanks, in large part, to advances in mobile-friendly trading technology like Robinhood, which has eased access to stock markets for Millennial and Gen Z investors. Post-pandemic policy responses have also played a role; stay-at-home restrictions and distribution of unemployment and stimulus dollars have left people with more time and spare cash to spend on trading.

Despite this, most retail investors still have limited capital. Many stocks, which can trade at a single share well above a quarter million dollars, have long been considered out of reach for most retail investors.

In recent years, in an attempt to attract new retail clients, brokers like Robinhood have allowed stock purchases by the slice for as little as a penny. In turn, retail investors with low capital have been able to trade prominent stocks that tend to have a steep price tag.

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Influencer Mix Strategies in Livestream Commerce: Impact on Product Sales

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This article is based the research of Kelley Assistant Professor of Marketing, Xian Gu, as well as Ziaoxi Zhang and P.K. Kannan. 

Introduction

The latest trend in influencer marketing is livestream commerce—a form of product sales that combines an influencer’s live video streaming and entertainment with e-commerce. So-called “social shopping” has grown exponentially in recent years with livestream commerce now prominent on TikTok, Facebook, YouTube, and Amazon.

To illustrate the point, TikTok entered the e-commerce industry in early 2018, and by 2020, transactions had reached $77 billion in gross merchandise value.

If you haven’t been “retailtained,” here’s how it works: (more…)

Bias and Fairness: Identifying and Mitigating Inherent AI Biases to Ensure Equitable Outcomes

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This article is based on a presentation at the Fall 2023 Accounting & Finance Forum on Data with Dignity by Ankit Virmani, Senior Engineering Manager of Data/ML Operations at CVS.

Even if you’re not leading data and machine learning operations for a global retailer like CVS, you’re likely aware of the risks inherent to the rapid adoption of artificial intelligence in decision-making—-bias and discrimination among the top of those concerns.

And for good reason. A lack of appropriate oversight and AI governance can have catastrophic consequences in the real world.

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MITRE ATLAS: Community-Driven Tools for AI Security and Assurance

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This article is based on a presentation at the Fall 2023 Institute of Business Analytics Conference on Generative AI and Cybersecurity: Navigating the New Era of Threats and Safeguards by Christina Liaghati, AI Strategy Execution & Operations Manager at MITRE.

Over a two-and-a-half-year period, an unsophisticated hacker duo was able to steal $77 million from the Chinese government by attacking a machine learning-enabled facial identification system that gave them access to the Shanghai Tax Authority.

The attackers created a fake shell company, which they used to acquire black market cell phones and headshots of Chinese citizens, as well as a long list of personal identifiable information. They were able to tweak the headshots and use the identifiable information to register accounts that evaded the ML-enabled facial recognition system used to verify identity.

With the privileged access into the Shanghai Tax Authority’s verified account environment, they repeatedly submitted fraudulent invoices that totaled $77 million.

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How Text Analytics Can Help Decision Making in Retail

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This article is based on a presentation at the Spring 2024 Analytics Conference on Decoding Consumer Analytics. by Sergei Ananyan, CEO of Megaputer Intelligence.

From the moment a customer walks into a store, there are dozens of invisible processes taking place behind the scenes to make the experience possible: things like inventory management, workforce management and support centers, equipment management for point-of-sale transactions, online shopping, loyalty cards and promotions, and survey feedback.

“Companies initially collected this data for operational purposes, so customers could return merchandise, for example,” explains Sergei Ananyan, CEO for Megaputer Intelligence. “Then, they discovered that if you look hard enough at the data, you can discover wonderful things that will help you to be more efficient and outpace your competition.”

As they say in Harry Potter, Ananyan jokes, “magic always leaves traces.”

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Data with Dignity: Equity and AI-based Resource Allocation

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This article is based on a presentation at the Fall 2023 Accounting & Finance Forum on Data with Dignity by Chad Dau, Associate Vice President – Decision Analytics and Optimization at Lilly.

AI-driven resource allocation is quickly becoming one of the most common applications of artificial intelligence in the business world, and for good reason. Leveraging data to make decisions about how resources are applied is tremendously attractive for organizations like pharmaceutical company Eli Lilly, that are continually looking for opportunities to make efficiency gains and optimize costs.

But AI-driven decision making isn’t exclusively upside, says Chad Dau, Associate Vice President of Decision Analytics and Optimization at Lilly. Introducing AI into decision-making processes poses an entirely new set of risks, and organizations may not be aware of the growing challenges for responsible use of AI.

“AI modeling tends to focus on short-term impacts and proximal drivers, and leaves out the complexity of interactions between decision makers,” says Dau. “When those things are ignored, you can build biases into your models that affect people’s lives and health.”

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Fixing an Internet of Broken Things: Approaches to Cybersecurity & AI Governance

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This article is based on a presentation at the Fall 2023 Accounting & Finance Forum on Data with Dignity by Scott Shakelford, the Provost Professor of Business Law and Ethics at the Indiana University Kelley School of Business, Director for IU’s Center for Applied Cybersecurity Research, and Executive Director of the Ostrom Workshop.

November 2, 1988 may not be a date you’ve assigned significance to. But it served as a key turning point in the history of the Internet and cybersecurity: the first DDoS attack.

This high-profile cybercrime, during which a graduate student at Cornell flooded a server with Internet traffic and inadvertently immobilized about 6,000 computers, caused hundreds of thousands of dollars in damages. And despite being accidental (it was meant to be a research project), the first DDoS attack nonetheless changed the landscape for cybersecurity and AI governance, says Scott Shackelford, a professor of business law and ethics for the Kelley School of Business, who also serves as the director for IU’s Center for Applied Cybersecurity Research.

From that case, the Computer Fraud and Abuse Act was established, setting a precedent that what happens in the cyber world has real-life consequences. (more…)

How Businesses are Embracing AI: The Risk Management Readiness Gap

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This article is based on a presentation at the Fall 2023 Institute of Business Analytics Conference on Generative AI and Cybersecurity: Navigating the New Era of Threats and Safeguards by Alie Fordyce, a product policy manager at Robust Intelligence.

If you ask Alie Fordyce, product policy manager for Robust Intelligence, there’s a readiness gap in artificial intelligence risk management practices for most businesses today. Even mature organizations are still working to tailor their AI risk management practices to anticipate the vulnerabilities inherent to generative AI.

“Even the largest, most forward-thinking companies are struggling to manage AI risk, regardless of their history or use cases,” says Fordyce, a product policy manager for Robust Intelligence, a startup based in San Francisco that was born out of a decade of research from Harvard University. Robust Intelligence is focused on helping companies manage the safety and security risks inherent to AI while harnessing the promise of the technology.

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