For example, the application of deep neural networks, a powerful subset of artificial intelligence, has introduced unprecedented capabilities to various fields. However, the complexity and opaqueness inherent in these networks have also given rise to significant challenges, particularly in terms of accountability. In deep neural networks, data is processed through multiple layers of interconnected nodes, where each node performs complex mathematical operations on the input data. These operations transform the input data into higher-level abstractions, gradually extracting features that contribute to decision-making. However, due to the intricate nature of these transformations, it becomes challenging to discern how individual pieces of input data contribute to the final output. Additionally, the interactions between nodes and layers involve countless parameters and weights that adjust during training, further increasing the complexity.

As a result, when an AI system powered by deep neural networks produces an unexpected or erroneous outcome, understanding the exact sequence of events that led to that outcome can be exceedingly complex. Each layer’s contribution, the interplay of nodes, and the influence of specific input data become intertwined in a convoluted manner. Consequently, pinpointing where the system deviated from the intended behavior becomes a formidable task. This complexity not only affects technical experts but also legal and ethical discussions related to accountability. Traditional methods of tracing decision-making, such as analyzing code or algorithms, are insufficient due to the intricate and distributed nature of neural networks. The lack of a clear, understandable narrative about how decisions are made impedes the assignment of responsibility, as it’s challenging to establish a direct causal link between actions taken within the network’s layers and the eventual output.

This challenge becomes even more pronounced for AI algorithms with learning capabilities, as humans lack direct control over their actions, making it difficult to assume full responsibility for their outcomes (Matthias, 2004). As we navigate the vast potential of AI in diverse fields, establishing accountability frameworks that address these complexities is essential to foster trust and ensure responsible AI deployment.

The initial step in establishing accountability lies in defining its intricacies, which often prove more complex than anticipated. Santoni de Sio and Mecacci (2021) proposes distinguishing between four types of responsibility gaps in the context of AI:

The culpability gap refers to the challenge of assigning accountability when AI systems are part of decision-making, stemming from the complex network of agents within the AI system (i.e., the problem of many hands).

The moral accountability gap pertains to human agents who find it challenging to comprehend and explain their own or other agents’ behavioral logic due to opaque AI algorithms and complex AI systems.

The public accountability gap arises when the application of AI complicates the assessment of how public officials are responsible for their actions.

The active responsibility gap is a forward-looking concept, unlike the three previously mentioned types of gaps. It pertains to the values and norms that professionals, such as engineers, are anticipated to uphold to prevent and alleviate adverse impacts on other individuals or communities. This gap emerges when those involved in designing, using, and interacting with AI systems fall short of fulfilling these moral obligations due to a deficiency in awareness, skills, or motivation.

As the responsibility gap manifests in four distinct types, it becomes evident that this is a multifaceted issue. Taking a fragmented approach to addressing the responsibility gap risks distorting the overall understanding of the problem. Recognizing this complexity, the authors advocate for a more holistic and integrated approach that acknowledges the interconnectedness of the four responsibility gaps. Such an approach aims to provide a more accurate and comprehensive understanding of the challenges posed by these gaps and offers a foundation for more effective solutions.

What’s Special about the Financial Sector?

The financial sector heavily relies on AI for various purposes. Given the interconnectedness of financial institutions and the potential for systemic risks affecting society at large, the issue of accountability in the financial sector holds particular significance, surpassing concerns in other industries.

According to Svetlova (2022), systemic risks can arise when market participants and their AI algorithms start to adopt similar strategies, thus pushing the market in the same direction, which increases the likelihood of severe disruptions within the market. This scenario is more likely when a substantial number of market participants employ identical or closely resembling AI algorithms and data sources.  The challenge of the “responsibility gap” deepens in cases of AI-triggered systemic risks, as it becomes difficult to establish a direct causal link between individual decisions and the resulting joint consequences.

Given the complexities of AI, providing a prescriptive solution to the accountability challenge isn’t feasible. However, establishing a governance structure at the firm level is crucial to promote best practices and address accountability issues.

Johnson (2021) offers four key normative questions to guide the development of algorithmic accountability practices. These questions involve identifying accountable actors and appropriate forums for algorithmic accountability, developing shared beliefs and norms, determining how to perform algorithmic accountability, and defining appropriate sanctions. While responses to these inquiries may be intricate and dependent on specific situations, they provide a basis for crafting accountability approaches in the field of algorithmic decision-making.

In summary, as the influence of AI continues to shape our contemporary landscape, the need for establishing accountability becomes paramount. Navigating the intricate pathways of AI accountability necessitates a multifaceted approach that acknowledges its diverse dimensions. This holds particularly true at the dynamic intersection of business, finance, and AI-driven advancements, where the adoption of transparent and ethically grounded practices becomes pivotal. Addressing AI accountability issues is an ongoing endeavor that demands collaborative efforts, thoughtful governance, and an unwavering commitment to continuous learning.