How We Can Keep AI on The Right Side of The Tracks

Artificial Intelligence is growing at a remarkably fast pace, and it’s high time we started amplifying discussions on ensuring safe use.

Without a doubt, the potential is undeniable. Over the past year, we saw AI technology permeate different sectors of the world, including healthcare, education, and others, automating repetitive tasks across these industries and freeing up human time and resources for more complex and creative work.

Can We Ensure Fair AI?

While the benefits are vast, concerns about its potential misuse are equally loud. To ensure AI remains a force for good, we need to keep it on the right side of the tracks, emphasizing ethical applications and robust regulatory oversight at all levels.

Unfettered AI development can pose risks – both known and unknown. Some algorithms are susceptible to biases present in the data they are trained on, which potentially lead to discriminatory outcomes, perpetuating inequalities in areas like loan approvals, employment screening, and even criminal justice. 

Ensuring Safe AI Use is a Shared Responsibility

We must actively address these biases to ensure AI operates. This is a shared responsibility between the regulators and developers. 

Regulators will need to develop clear and comprehensive regulations that outline ethical principles and best practices for AI development and deployment, addressing issues like data privacy, transparency, accountability, and biases.

The United States has already begun to walk the path with a recently issued Executive Order by the Biden administration. It establishes essential principles for AI development and use. These include prioritizing safety, security, equity, civil rights, and privacy. 

The Order emphasizes addressing algorithmic bias and ensuring transparency in AI systems to mitigate potential harms and promote fair and ethical AI applications.

The onus lies on developers to translate ethical principles into concrete action throughout the development process, which involves careful data collection, unbiased algorithm design, and robust testing for unfairness and potential harm.

Developers also need to be transparent about how their AI systems work by disclosing data sources, algorithms, and decision-making processes.