Navigating the Ethical and Legal Landscape of AI in Business

As businesses increasingly incorporate artificial intelligence (“AI”) into their operations—from healthcare to finance and beyond—this technology offers exciting opportunities for innovation and efficiency. However, it also brings significant risks, particularly around issues of bias, fairness, and the potential harm to vulnerable groups. Recognizing these challenges, the Federal Trade Commission (“FTC”) recently published a business guidance blog to help companies navigate the ethical and legal complexities of using AI. The guidance highlights how AI systems, if not carefully developed and monitored, can inadvertently perpetuate discrimination and other harmful outcomes. For example, recent studies, such as one published in the Journal of the American Medical Informatics Association, have shown how AI models meant to benefit public health can exacerbate existing racial disparities in healthcare. This underscores the need for businesses to not only focus on the technical capabilities of AI but also consider its broader ethical and legal implications. One of the major challenges with AI is that its underlying algorithms and data models can unintentionally perpetuate discrimination. For example, AI systems designed to allocate healthcare resources during the COVID-19 pandemic may inadvertently favor certain racial or socioeconomic groups over others, leading to negative outcomes for historically underserved communities. This problem is compounded by the fact that data used to train AI models often reflect systemic biases in society. If businesses are not vigilant, these biases can become embedded in the AI’s decision-making process, perpetuating unfair outcomes and potentially violating legal protections. Fortunately, there are established frameworks that can guide businesses in navigating these challenges. The Federal Trade Commission (“FTC”), which has decades of experience in consumer protection, has highlighted several important practices for companies using AI to ensure fairness, transparency, and compliance with the law. These practices are designed to help companies avoid unintentional harm, discrimination, or deceptive practices, and include both foundational steps and ongoing checks. First, businesses must start with a solid foundation. AI’s complexity often gives the illusion that it’s a mysterious, almost magical technology, but at its core, AI is built on data. The data that powers AI models must be comprehensive, inclusive, and free from biases that could skew results. Companies should ensure their data sets are representative of all relevant populations and work proactively to identify and address data gaps. Failing to account for these gaps can lead to models that produce inequitable outcomes, particularly for underrepresented or marginalized groups. Once an AI model is built, ongoing vigilance is crucial. Companies must test their algorithms regularly to ensure that they do not inadvertently discriminate against any particular group, whether based on race, gender, or other protected characteristics. It’s essential to conduct both pre-launch and post-launch audits, and to refine the model as necessary to reduce bias. Businesses that fail to do so risk their algorithms being flagged for discriminatory practices, which can lead to legal and reputational consequences.