The AI Risk Drift and its Impact on Data Privacy for Organizations
I get it! Your organization sees AI as a game-changer for boosting productivity and is eager to explore innovative ways to utilize this technology. However, this rapid adoption often unfolds without sufficient comprehension of the associated risks, especially in the realm of Data Privacy. I term this oversight as "AI Risk Drift", a phenomenon where organizations inadvertently escalate their Data Privacy risks. This occurs through various avenues: transitioning from AI as an assistive tool to automated decision-making, elevating the stakes in terms of potential harm in AI applications, and incorporating inferential analytics into business processes that may cause human harm. This article explores these three critical forms of AI Risk Drift and offers preventive strategies to help companies mitigate these escalating risks.
AI Risk Drift: Drift from Using AI as a Helper into Automated Decision-Making
Initially, organizations might implement AI to serve as a complementary tool that aids human decision-making, a practice generally considered appropriate and low-risk. However, the landscape changes significantly when AI evolves from an auxiliary "helper" to an automated decision-maker, often without human oversight or judgment. An example can be found in the employment context. Imagine a company sifting through 1,000 job applications; they might initially deploy an Automated Employment Decision Tool (AEDT) to assist human resources by sorting resumes and offering analytical insights on applicants. In this phase, AI merely augments human judgment by providing a different lens through which to evaluate candidates.
However, complications arise when an automatic update to the tool enables it to independently filter out candidates based on predetermined criteria, thereby reducing the applicant pool presented to human reviewers from 1,000 to just 200. Such a shift in functionality signifies a move toward automated decision-making and triggers compliance requirements under various data protection laws, such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA). In the U.S., the employment sector faces additional regulations, including Illinois' Artificial Intelligence Video Interview Act of January 2020 and New York City's Local Law 144 concerning AI Hiring Tools, effective as of July 2023.
This gradual transition, where AI's capabilities expand to the point of replacing human judgment, exemplifies a dangerous AI Risk Drift. Such a drift should serve as a red flag, prompting organizations to reevaluate their use of AI technologies and the corresponding legal obligations. Here are ways organizations can minimize this AI Risk Drift:
Periodically assess the features in automated tools that provide decision-making capabilities
Develop a human-in-the-loop system for critical decisions
Clearly outline the AI's role in policy documents and include the human’s responsibility in the process
Conduct Data Privacy Impact Assessments (DPIAs) for AI modules or new features in existing tools that may create automated decision-making risks
AI Risk Drift: Drift from Low Stakes to High Stakes Use Cases
AI applications can span a wide array of tasks, transitioning seamlessly from low-stakes responsibilities like organizing emails to high-stakes endeavors such as medical diagnostics or credit risk assessments. This shift from low-risk to high-risk applications often entails processing sensitive human data, thereby escalating the organization's Data Privacy Risk. For instance, a healthcare provider might initially deploy AI to efficiently manage routine customer inquiries. Encouraged by this success, the company may extend the AI's role to more critical tasks, such as communicating prognoses to terminally ill patients, potentially replacing consultations with medical professionals. Such a transition from low-stakes to high-stakes tasks exemplifies a perilous AI Risk Drift that organizations need to be vigilant about.
Here are ways organizations can minimize this AI Risk Drift:
Review the scope of AI application's use cases regularly
Train employees on the ethical considerations of high-stakes AI use
Perform regular audits of high-stakes uses to ensure compliance with privacy laws
AI Risk Drift: Drift from Correlation into Inference
In its initial stages, AI often analyzes patterns and correlations within data sets for various analytical purposes. However, as both the volume of data and the sophistication of AI algorithms increase, the potential for drawing false and harmful inferences about individuals becomes a growing concern. This progression introduces the risk of organizations using AI to make potentially harmful inferences based on mere correlations. Take, for instance, certain insurance companies that have discovered a correlation between being a college graduate and having fewer accidents. Based on this data, they infer that individuals with only a high school education are more prone to accidents and consequently charge them higher insurance premiums, even if they have a clean driving record. This is an example of AI Risk Drift, where damaging inferences could adversely affect people, even when those inferences are not universally accurate. As legal frameworks around AI bias, transparency, and explainability continue to evolve, we can expect heightened scrutiny over the kinds of inferences AI systems can make. Here are ways organizations can minimize this AI Risk Drift:
Audit algorithms for unintended data analysis
Be clear on what you want AI to do and the results you expect
Minimize data collection to gain better and more accurate insights
Regularly update privacy policies to reflect the current scope of AI uses
AI Risk Drift is a critical concern that organizations must address as they continue to integrate AI into their operations. While the deployment of AI presents opportunities for increased efficiency and novel applications, it also brings about new challenges in maintaining Data Privacy. By proactively identifying and mitigating the risks of AI Risk Drift, organizations can better protect their clients' data and maintain public trust.
The key to averting AI Risk Drift lies in continuous monitoring, robust policy frameworks, and educating stakeholders. Organizations should make a concerted effort to follow the preventive measures outlined and stay vigilant about AI systems' evolving role and capabilities in their business environments to make Data Privacy a Business advantage.
