There are millions of AI algorithms already in production. Only a small portion of them were audited for fairness. Fair AI is still only talked about in the future tense by most AI engineers. Companies putting untested, biased algorithms into production run the risk of getting into serious trouble not only from a PR perspective but by way of making bad business decisions. After all, biased data will lead to biased business decisions, underserved minority groups, and inexplicable results. From faulty pricing models in insurance to suboptimal prediction outcomes in healthcare, algorithmic fairness is a long stretch away from reality.
The current landscape of fair AI and AI explainability is marked by a stark discrepancy between the growing recognition of their importance and the actual efforts undertaken to address them. While academic conferences, think tanks, and even some regulatory bodies are putting an increasing focus on the need for AI to be both fair and explainable, these discussions often don't translate into actionable steps within organizations.
Many companies are still in the early stages of understanding what it means to implement fair and explainable AI systems. The common practice of simply deleting sensitive attributes like race, ethnicity, or religion from datasets is a glaring example of the superficial approaches that fail to address the root cause of the problem. This not only perpetuates biases through proxy variables but also obfuscates the decision-making process, making it even harder to audit and explain the AI model's behavior.
The result is a landscape where algorithmic decisions, although increasingly critical in everything from loan approvals to medical diagnoses, lack both fairness and transparency. This undermines public trust in AI systems and exposes organizations to both ethical scrutiny and legal repercussions. And while there are tools and methods available for auditing algorithms, their adoption remains woefully limited, often considered as an afterthought rather than a fundamental part of AI development. Consequently, the industry is caught in a cycle of deploying algorithms that neither the creators nor the end-users fully understand or trust, perpetuating a status quo that is increasingly at odds with societal demands for fairness, accountability, and transparency.