Instead of restricting AI and algorithms, make them explainable

The increasing use of AI in decision-making processes, from social media feeds to legal sentencing, has raised concerns about transparency and accountability. While regulations are often proposed to curb the use of algorithms, the focus should be on ensuring explainability rather than restricting specific technologies. Every individual impacted by a software-driven decision deserves a clear understanding of the factors and logic that led to that outcome. This transparency is crucial for identifying errors, addressing biases, and enabling individuals to navigate systems effectively. The author illustrates this point with a personal anecdote about an opaque car rental fee, highlighting how a lack of explainability can lead to customer dissatisfaction and unnecessary costs for businesses. Applying this principle to AI, the author argues that social media companies should be able to explain their feed algorithms, enabling users to understand content selection. However, the current limitations of AI in providing such explanations present a valid reason for restricting their use in certain domains until explainability improves. This emphasis on explainability should not be misconstrued as a call for exhaustive justifications for every automated decision. Instead, it highlights the need for transparency when decisions are disputed or raise concerns about fairness. While AI can offer valuable suggestions, human decision-makers must retain the responsibility to explain their choices, especially when influenced by AI. Ultimately, the goal should be to leverage AI as a tool for enhancing human understanding and developing more transparent systems, rather than allowing it to create an opaque decision-making landscape.