Commentary: Implications of causality in artificial intelligence
Luís Cavique's (2024) article, "Implications of Causality in Artificial Intelligence," presents a compelling case for the importance of causalAI. By focusing on cause-and-effect relationships rather than mere correlations, causalAI offers a pathway to more transparent, fair, and reliable AI systems. Cavique argues that causalAI is the least criticized approach compared to responsible AI, fair AI, and explainable AI, largely due to its scientific rigor and potential to reduce biases. However, despite its promise, causalAI is not without challenges. This commentary aims to assess some of these limitations and potential criticisms of causalAI as presented by Cavique, arguing that while it holds substantial promise, its implementation and practical application may be more complex and fraught with difficulties than the author suggests.One of the primary challenges with causalAI lies in its complexity. CausalAI requires a deep understanding of causal inference and advanced statistical techniques, making it less accessible to most AI developers (Cox Jr., 2023). Unlike correlation-based methods, which are widely understood and now relatively easy to implement, causal models demand a high level of expertise. Arguably, only a select group of experts can effectively design, implement, and interpret these models. This complexity can create barriers to entry for many organizations and individuals who might want to engage in developing or using causalAI for benefiting from the transparency and fairness that causalAI promises. This could exacerbate existing disparities in AI literacy, and capacitation, and epistemic justice, potentially leading to an increased form of AI elitism, where only those with advanced skills, knowledge, and wealth of resources can fully participate in or critique causalAI development. This situation could undermine the broader goal of making its benefits accessible to a wide audience.CausalAI's reliance on high-quality, detailed data presents another significant challenge. Establishing causal relationships requires data that not only captures correlations but also provides the context needed to infer causality (Vallverdú, 2024). In many real-world applications, such data is either unavailable or prohibitively expensive to obtain. Causal AI requires high-quality data that captures both correlations and context (Vallverdú, 2024). In practice, such data is often scarce or costly, posing challenges for establishing accurate causal relationships Additionally, even when data is available, it may be incomplete or biased in ways that could skew causal inferences. The assumptions underlying causal models also warrant critical examination. CausalAI models often assume that all relevant variables have been identified and correctly measured. However, in practice, unmeasured confounders-variables that influence both the cause and effect-can distort causal estimates, leading to incorrect conclusions and as Rawal et al (2024) put it there is a lack of ground truth for validation. This reliance on potentially faulty assumptions could result in AI systems that, while appearing transparent and fair, are actually based on flawed reasoning. Furthermore, the process of identifying and validating causal relationships can be resource intensive and time-consuming. This raises questions about the scalability of causalAI, particularly in dynamic environments where data is constantly evolving, and causal relationships may shift over time. The effort required to maintain accurate causal models could outweigh the benefits, especially in fast-paced industries where quick decision-making is critical.Scalability is a major challenge for causal AI, as building and validating models is complex and resource-intensive. These models often require tailored adjustments for new contexts, limiting their generalizability compared to correlation-based methods. Scalability is a crucial consideration in the deployment of AI systems, and causalAI may struggle in this area. The process of building and validating causal models is not only complex, but also resource-intensive. As Cavique rightly notes, causalAI requires meticulous identification of causal variables and relationships, which may not easily generalize across different contexts or applications, particularly in sectors requiring a major data curation effort (such as the healthcare sector). This limitation could hinder the practical application of causalAI in scenarios where scalability and adaptability are key. Specificity required by causal models may limit their ability to generalize across different datasets or environments. While correlation-based models can often be applied broadly with minimal adjustments, causal models may need to be tailored to the particularities of each new situation. This lack of generalizability could make causalAI less appealing in settings where adaptability is needed.CausalAI is lauded for its potential to improve fairness and transparency in AI systems, but these benefits are not guaranteed. The causal relationships identified by AI systems are not immune to the biases present in the underlying data. If the data reflects existing societal biases or power dynamics, the causal models derived from it may inadvertently reinforce these issues. Put more Even when accurately identifying cause-and-effect relationships, they may perpetuate societal biases, potentially reinforcing inequities if not designed inclusively.simply, a causal model trained on biased data might correctly identify a causal relationship but still perpetuate unjust outcomes. Moreover, the iInterpretation of causal models can be influenced by the subjective perspectives of those designing or using them (Mittelstadt et al., 2019)-especially if the design of CausalAI is not inclusive and transparent, allowing for the active participation of stakeholders. This subjectivity introduces another layer of potential bias, as different stakeholders may have...