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RIMS TechRisk/RiskTech: Emerging Risk AI Bias

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On the second day of the RIMS virtual event TechRisk/RiskTech, CornerstoneAI founder and president Chantal Sathi and advisor Eric Barberio discussed the potential uses for artificial intelligence-based technologies and how risk managers can avoid the potential inherent biases in AI.

Explaining the current state of AI and machine learning, Sathi noted that this is “emerging technology and is here to stay,” making it even more imperative to understand and account for the associated risks. The algorithms that make up these technologies feed off data sets, Sathi explained, and these data sets can contain inherent bias in how they are collected and used. While it is a misconception that all algorithms have or can produce bias, the fundamental challenge is determining whether the AI and machine learning systems that a risk manager’s company uses do contain bias.

The risks of not rooting out bias in your company’s technology include:

  • Loss of trust: If or when it is revealed that the company’s products and services are based on biased technology or data, customers and others will lose faith in the company.
  • Punitive damage: Countries around the world have implemented or are in the process of implementing regulations governing AI, attempting to ensure human control of such technologies. These regulations (such as GDPR in the European Union) can include punitive damages for violations.
  • Social harm: The widespread use of AI and machine learning includes applications in legal sentencing, medical decisions, job applications and other business functions that have major impact on people’s lives and society at large.

Sathi and Barberio outlined five steps to assess these technologies for fairness and address bias:

  1. Clearly and specifically defining the scope of what the product is supposed to do.
  2. Interpreting and pre-processing the data, which involves gathering and cleaning the data to determine if it adequately represents the full scope of ethnic backgrounds and other demographics.
  3. Most importantly, the company should employ a bias detection framework. This can include a data audit tool to determine whether any output demonstrates unjustified differential bias.
  4. Validating the results the product produces using correlation open source toolkits, such as IBM AI Fairness 360 or MS Fairlearn.
  5. Producing a final assessment report.

Following these steps, risk professionals can help ensure their companies use AI and machine learning without perpetuating its inherent bias.

The session “Emerging Risk AI Bias” and others from RIMS TechRisk/RiskTech will be available on-demand for the next 60 days, and you can access the virtual event here.

RIMS TechRisk/RiskTech: Opportunities and Risks of AI

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On the first day of the RIMS virtual event TechRisk/RiskTech, author and UCLA professor Dr. Ramesh Srinivasan gave a keynote titled “The Opportunities and Downside Risks of Using AI,” touching on the key flashpoints of current technological advancement, and what they mean for risk management. He noted that as data storage has become far cheaper, and computation quicker, this has allowed risk assessment technology to improve. But with these improvements come serious risks.

Srinivasan provided an overview of where artificial intelligence and machine learning stand, and how companies use these technologies. AI is “already here,” he said, and numerous companies are using the technology, including corporate giants Uber and Airbnb, whose business models depend on AI. He also stressed that AI is not the threat portrayed in movies, and that these portrayals have led to a kind of “generalized AI anxiety,” a fear of robotic takeover or the end of humanity—not a realistic scenario.

However, the algorithms that support them and govern many users’ online activities could end up being something akin to the “pre-cogs” from Minority Report that predict future crimes because the algorithms are collecting so much personal information. Companies are using these algorithms to make decisions about users, sometimes based on data sets that are skewed to reflect the biases of the people who collected that data in the first place.

Often, technology companies will sell products with little transparency into the algorithms and data sets that the product is built around. In terms of avoiding products that use AI and machine learning that are built with implicit bias guiding those technologies, Srinivasan suggested A/B testing new products, using them on a trial or short-term basis, and using them on a small subset of users or data to see what effect they have.

When deciding which AI/machine learning technology their companies should use, Srinivasan recommended that risk professionals should specifically consider mapping out what technology their company is using and weigh the benefits against the potential risks, and also examining those risks thoroughly and what short- and long-term threats they pose to the organization.

Specific risks of AI (as companies currently use it) that risk professionals should consider include:

  • Economic risk in the form of the gig economy, which, while making business more efficient, also leaves workers with unsustainable income
  • Increased automation in the form of the internet of things, driverless vehicles, wearable tech, and other ways of replacing workers with machines, risk making labor obsolete.
  • Users do not get benefits from people and companies using and profiting off of their data.
  • New technologies also have immense environmental impact, including the amount of power that cryptocurrencies require and the health risks of electronic waste.
  • Issues like cyberwarfare, intellectual property theft and disinformation are all exacerbated as these technologies advance.
  • The bias inherent in AI/machine learning have real world impacts. For example, court sentencing often relies on biased predictive algorithms, as do policing, health care facilities (AI giving cancer treatment recommendations, for example) and business functions like hiring.

Despite these potential pitfalls, Srinivasan was optimistic, noting that risk professionals “can guide this digital world as much as it guides you,” and that “AI can serve us all.”

RIMS TechRisk/RiskTech continues today, with sessions including:

  • Emerging Risk: AI Bias
  • Connected & Protected
  • Tips for Navigating the Cyber Market
  • Taking on Rising Temps: Tools and Techniques to Manage Extreme Weather Risks for Workers
  • Using Telematics to Give a Total Risk Picture

You can register and access the virtual event here, and sessions will be available on-demand for the next 60 days.

Assessing the Legal Risks in AI—And Opportunities for Risk Managers

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Last year, Amazon made headlines for a developing a human resources hiring tool fueled by machine learning and artificial intelligence. Unfortunately, the tool came to light not as another groundbreaking innovation from the company, but for the notable gender bias the tool had learned from the data input and amplified in the candidates it highlighted for hiring.

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As Reuters reported, the models detected patterns from resumes of candidates from the previous decade and the resulting hiring decisions, but these decisions reflect that the tech industry is disproportionately male. The program, in turn, learned to favor male candidates.

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As AI technology draws increasing attention and its applications proliferate, businesses that create or use such technology face a wide range of complex risks, from clear-cut reputation risk to rapidly evolving regulatory risk. At last week’s RIMS NeXtGen Forum 2019, litigators Todd J. Burke and Scarlett Trazo of Gowling WLG pointed toward such ethical implications and complex evolving regulatory requirements as highlighting the key opportunities for risk management to get involved at every point in the AI field.

For example, Burke and Trazo noted that employees who will be interacting with AI will need to be trained to understand its application and outcomes. In cases where AI is being deployed improperly, failure to train the employees involved to ensure best practices are being followed in good faith could present legal exposure for the company. Risk managers with technical savvy and a long-view lens will be critical in spotting such liabilities for their employers, and potentially even helping to shape the responsible use of emerging technology.

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To help risk managers assess the risks of AI in application or help guide the process of developing and deploying AI in their enterprises, Burke and Trazo offered the following “Checklist for AI Risk”:

  • Understanding: You should understand what your organization is trying to achieve by implementing AI solutions.
  • Data Integrity and Ownership: Organizations should place an emphasis on the quality of data being used to train AI and determine the ownership of any improvements created by AI.
  • Monitoring Outcomes: You should monitor the outcomes of AI and implement control measures to avoid unintended outcomes.
  • Transparency: Algorithmic decision-making should shift from the “black box” to the “glass box.”
  • Bias and Discrimination: You should be proactive in ensuring the neutrality of outcomes to avoid bias and discrimination.
  • Ethical Review and Regulatory Compliance: You should ensure that your use of AI is in line with current and anticipated ethical and regulatory frameworks.
  • Safety and Security: You should ensure that AI is not only safe to use but also secure against cyberattacks. You should develop a contingency plan should AI malfunction or other mishaps occur.
  • Impact on the Workforce: You should determine how the implementation of AI will impact your workforce.

For more information about artificial intelligence, check out these articles from Risk Management:

RIMS Report: Making Sense of AI

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The risk of not adopting some form of artificial intelligence (AI) can be much greater than the potential risks of implementation according to the new RIMS Professional Report: Making Sense of Artificial Intelligence and Its Impact on Risk Management.

Authored by RIMS Strategic and Enterprise Council member and director, Microsoft Enterprise Risk Management Tom Easthope, the report explores forms of AI available to organizations, common implementations scenarios for risk professionals to consider, as well as opportunities for those professionals to advance their careers in light of the emergence of AI technologies.

“While the discussions about the long-term impacts of artificial intelligence on society are important to understand and track, the more pressing issue is to understand the impacts on your industry, your organization and, ultimately, your career,” Easthope said.

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“Risk professionals should find ways to participate in strategic discussions around AI and educate themselves on the world of possibilities it offers them and their organizations.”

The report explores AI’s foundational concepts, such as data and algorithms. It also discusses forms of AI, such as artificial general intelligence, (often referred to as “thinking machines” along the lines of C-3PO from the “Star Wars” films) and artificial narrow intelligence (ANI) which focuses on tasks that have major business impacts, including image recognition, credit card fraud detection and speech recognition. Citing research that AI-derived business value will be worth $3.9 trillion in the next three years, ANI presents risks and opportunities for risk professionals and their companies.

And while the report suggests that changes introduced by AI innovation and automation will impact jobs and tasks in the risk, compliance and insurance industry, it also presents methods to keep professionals less expendable, if they’re willing to embrace the technology.

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“But while change is inevitable, it does not mean that your risk career must end,” the report said. “Essentially, if you understand the organization’s strategy and how it can enhance its operations with ANI or the context around data, then you have something to offer.”

RIMS Strategic and Enterprise Risk Management Council (SERMC) is organized to provide leadership on strategic and enterprise risk management research, practices, topics and issues, in alignment with RIMS’ vision, affiliations and partnerships. SERMC comprises RIMS members, academics, strategists, consultants and other practitioners who are experienced with strategic and enterprise risk management and related issues.

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The report is currently available exclusively to RIMS members. To download the report, visit RIMS Risk Knowledge library at www.RIMS.org/RiskKnowledge. For more information about the Society and to learn about other RIMS publications, educational opportunities, conferences and resources, visit www.RIMS.org.