In recent years, India has seen significant progress in the use of artificial intelligence (AI) across various sectors, including supply chain management. With a thriving economy and a tech-savvy workforce, the country’s AI advancements are flourishing and is ranked fifth globally.
Governments, academia, and industries across the globe are recognising the potential of AI in improving supply chain operations, increasing efficiency, and enabling data-driven decision-making. However, as we embrace this technology, it is crucial to recognise and address the ethical considerations that arise. In this article, we’ll delve into the ethical considerations of using AI for supply chain decision-making while emphasising the significance of implementing ethical frameworks to promote responsible and sustainable practices.
Transparency and Explainability
To make ethical decisions in the supply chain using AI, transparency and explainability are crucial. Since AI algorithms process large amounts of data to make complex decisions, it can be challenging to understand how they arrive at these conclusions. One solution to this problem is to use explainable AI models that provide clear explanations for these decisions. This helps to increase transparency and enables stakeholders to understand the decision-making process better, particularly when selecting suppliers, pricing decisions, and managing inventory. We can foster trust and accountability among employees, customers, and partners by prioritising transparency and explainability.
Bias and Fairness
To make ethical decisions in supply chain management, eliminating biases in data is critical. We must carefully review the training data, minimise biases, and regularly audit AI systems. Bias can appear in different ways, such as favouring specific suppliers or locations based on historical data. For example, a company relying heavily on AI algorithms to determine production volumes and distribution strategies may face location bias if the algorithms are predominantly trained on historical data from specific regions or customer demographics. This could lead to overproduction, excess inventory, and potential wastage in certain areas while ignoring or underestimating demand in other regions. As a result, the company’s resource allocation would be inefficient, and its ability to meet customer needs would be hampered.
By ensuring fairness and training AI models on diverse and representative data, we can create supply chains that do not perpetuate inequalities or discrimination.
Privacy and Data Security
To make ethical supply chain decisions using AI, it is crucial to prioritise privacy and data security. AI requires significant data collection and analysis, often including sensitive customer, supplier, and partner information. A significant concern is the possibility of exploiting personal information for profit. Imagine a scenario in which a company tracks consumer buying behaviour and identifies patterns of stockouts for specific products. Instead of promptly restocking these products, the company intentionally withholds them to create artificial scarcity. They then promote alternative products with higher profit margins, potentially misleading consumers and manipulating their purchasing decisions. This practice is unethical and undermines trust between businesses and consumers.
As technology leaders, we must prioritise robust data protection measures, such as secure data storage, encryption, and stringent access controls. Obtaining explicit consent from individuals before collecting and using their data is crucial. Additionally, companies should establish clear data usage and retention guidelines to prevent unauthorised access or misuse. By recognising these ethical implications, businesses can balance leveraging consumer data for operational efficiency while ensuring transparency, fairness, and responsible decision-making.
Human oversight guarantees that ethical considerations are taken into account in decision-making processes. For instance, imagine an AI system that automates routine tasks like data collection, analysis, and reporting. This automation saves supply chain professionals valuable time, allowing them to focus on more strategic roles and analysis. Instead of being bogged down with manual data processing, they can explore deeper insights, identify trends, and make well-informed strategic decisions based on AI-generated recommendations.
As technology leaders, we believe supply chain professionals should be actively involved in defining AI objectives, setting boundaries, and validating AI outcomes. By working collaboratively, we can harness the strengths of AI while ensuring that human values, ethics, and intuition remain at the forefront.
When implementing AI in supply chains, it is crucial to consider its impact on the environment. While AI can improve efficiency, we must ensure it does not harm the planet. To promote sustainability, organisations should invest in energy-efficient hardware, consider the lifecycle of AI technologies, support carbon offsetting projects, ethically source materials, and encourage collaboration between AI and environmental experts. Achieving a balance between AI convenience and environmental responsibility is crucial for creating a sustainable future for supply chains and the planet.
According to recent findings, a majority of consumers, approximately 65%, maintain their trust in businesses that incorporate AI technology into their operations, despite certain reservations surrounding its usage. As supply chain SaaS leaders, we recognise AI’s immense potential in enhancing supply chain efficiency and accuracy. However, we also understand the potential concerns that may arise from an ethical standpoint. So by prioritising transparency, fairness, data privacy, human-AI collaboration, and environmental responsibility, we can reap the benefits of AI tech to enhance supply chain decision-making while being ethical.
This article has been authored by Mrinal Rai, CPO & Co-Founder, Intugine and originally appeared in the July edition of the Logistics Insider Magazine.