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AI Risk Assessment: A Framework for Thinking through Risk Considerations

Generative AI in Our Lives

No one will argue that AI is here to stay. It has enhanced our everyday lives as well as our business processes. Whether it’s voice-assisted smart phones, handwriting recognition, financial trading, spam filtering, language translation, or a myriad of other tasks that have been automated and streamlined, AI impacts our lives daily. And with the innovation of generative AI, this technology is rapidly evolving.

As businesses strive to meet evolving customer expectations, innovative technology is often the first stop on the road to success. However, the advancements in generative AI technology bring about challenges and risks which need consideration. Let’s look at how you can think through your AI risk assessment, across a diverse ecosystem to ensure optimization of AI technology and responsible use. By approaching generative AI with a more holistic view of risk, it allows you to balance both people and generative AI to address challenges, create impact, and innovate meaningfully.

Primary Risk Types

There are seven primary risk types that any business needs to take into consideration:

  • Brand/business
  • Customer experience
  • Ethical considerations
  • Data privacy transparency & explainability
  • Algorithmic bias mitigation
  • AI & data governance
  • Other – a group of miscellaneous items that will continue to grow

Each of these areas bring unique considerations and should be approached thoughtfully. By examining the entire risk portfolio, businesses can meet challenges head on, plan, and avoid pitfalls.

Brand/Business

Regardless of the technology being used, it’s important for your company’s brand be protected. The first consideration for any brand is complying with legal and governance. Adhering to larger regulation and governance (present and future) as well as following industry specific rules and requirements is a key element in ensuring proper use of generative AI technology.

Consistency in brand application is next on the list. Regardless of the channel your customers are engaging, your brand needs to remain consistent across all touchpoints. Website, content, digital channels, social media—anywhere a customer or the market will meet your business—the values of the business and brand must be consistent.

Another consideration in protecting your brand and business is preventing bad actors. Mitigating things like spam, content stuffing, corrupt AI, fraudulent attacks, and more for security internally and externally. Any one of these pitfalls can disrupt your business and damage your brand.

Customer Experience

Customers are the heart of any business—and customer expectations are continually evolving with technologies and processes. Companies need to be able to strike a balance between being efficient and providing a quality engagement with customers. Minimizing customer churn due to service/support inefficiencies and creating a faster path to value without sacrificing quality of output is what we call operating efficiently with quality.

People-first service ensures the technology you use leverages unique customer data to provide faster and more custom support experiences across help chats, service centers, and more. Personalizing customer interactions using generative AI and the data you’re already collecting builds a connection between your brand and your customers. Customers feel seen and heard and are more likely to return.

Ethical Considerations

When it comes to customer data and technology, protecting data privacy and PII goes hand-in-hand. Your AI risk assessment must include protecting sensitive data and ensuring proper security during collection, storage, and usage of data. It’s crucial to maintaining your brand reputation and customer base.

In alignment with privacy and PII is making sure your generative AI solutions uphold ethics. Ensuring that inputs and outputs align with ethical standards to limit bias, adhere to obligations, and maintain your corporate values will convey your company’s integrity and trustworthiness, so your brand shines in a positive light.

Data Privacy Transparency & Explainability

Ensuring customers understand your data privacy practices and are aware of not only how you handle their data, but also where they may be interacting with generative AI technology is critical in today’s fast-paced digital world. Personal information is exchanged at a rapid rate and making sure your customers feel safe is critical to maintaining a long relationship with them.

You should be transparent with customers, ensuring they know how their data is collected, utilized, and shared by an organization. These practices should be well documented for customers to access.

To ensure trust, accountability, and compliance, it’s important that stakeholders within your organization understand the AI processes and how those technologies reach their outcomes. This helps decisions-makers have clear justifications for their technology and process recommendations. Being able to properly communicate this is called explainability within data privacy and is crucial to ensure privacy rights and trust.

Algorithmic Bias Mitigation

Algorithmic bias mitigation involves implementing strategies to reduce biases present in AI algorithms. These biases may come from various sources, such as historical data or cultural stereotypes. Mitigation typically includes steps like identifying biases, assessing their impact, and implementing measures to address them. This can involve preprocessing data and responses to remove biases, adjusting algorithms for fairness across different groups, or ensuring transparency and accountability in decision-making. Ongoing human monitoring and machine learning is essential to maintain fairness over time. By actively mitigating biases, organizations can promote fairness and equity, leading to more inclusive outcomes.

AI & Data Governance

AI governance ensures that businesses deploy AI technologies responsibly and ethically. It involves developing a framework with principles, guidelines, technology controls, and regulations that address issues such as fairness, accountability, transparency, and privacy. One of the core AI technologies is large language models (LLMs), which have the capability of processing vast amounts of data, putting it in context, personalizing it, and providing answers to questions or resolution to problems in natural language.

Businesses need a programmatic approach to LLMs and data management that includes data quality, LLM quality, standards, bias mitigation, procedures, ethical use of AI, transparency, and metrics covering the entire AI life cycle. This will not only enhance LLM and data quality and security, but also ensure your data is primed for applying advanced analytics to help you accelerate decision-making while reducing risk.

Other Considerations

There are some final elements that need to be thought through to ensure your generative AI solution not only makes a positive impact on your business, but does so in a way that mitigates risks. Proper sourcing and citing is a key consideration. Attributing works and adhering to copyrights, etc. prevents legal infringement, plagiarism, and more. Minimizing errors, removing false information, and preventing bad data to have inputs and outputs that are true and correct will ensure accuracy.

And finally, those things that are yet to be discovered. Generative AI technology will continue to evolve and grow. We cannot identify all areas of potential future risk. So, you must be vigilant, continually reevaluating your AI risk assessment to identify where generative AI may pose risks to your customers, employees, processes, and business.

Learn how Concentrix innovates with generative AI, and how we can help uncover your biggest areas of risk, process gaps, and advise on the best solutions to address each problem.

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