As the age of AI rapidly gathers steam, leaders must examine and address its impact on people. The narrative is shifting from AI is coming for our jobs to how do we make AI work for us? The latter is a more realistic position and one that will create space for future growth and efficiency.  

The reality is that AI will only be as good for any of us as the people inputting the data, the data itself, and our ability to “stress test” those areas to ensure that bias in the process is eliminated or mitigated as much as possible. 

In order for AI to properly support organizations, it must feast on massive amounts of data. Organizational leaders cannot be expected to be versed in every aspect of their operations but the critical nature of AI and its importance to the operations creates an opportunity for leaders to include regular updates on the quantity of information they are gathering which supports the AI models they are building, as well as, its accuracy and integrity.   

As AI pushes further and further downstream, there has to be a means to address bis in the models. Bias can occur at various points as an AI model is being built including in the data itself, the algorithms used to process the data, and the methodology used to “scrub” the data prior to implementation. In a recent study published in the Stanford Report it was found, among other things, that bias against older women is pervasive in generative AI outputs.  

The ability to mitigate bias in the AI process stream is critical to its success. The biggest potential opportunity for bias, as I see it, comes from whether or not the data has enough individuals represented across the target audience it’s designed to assist. 

Bias in the model at any point can lead to inequitable treatment of individuals and groups, which can impact outcomes and cause serious and potentially fatal ethical consequences.  

To address bias in AI models requires a multi-layered approach. This should include incorporating myriad datasets to ensure diverse perspectives, routine audits of algorithms and ensuring multidisciplinary and cross-functional teams are present to ensure wide perspectives are represented to ensure equitable outcomes.   

To be certain, organizations should not only attempt to do no harm; they must do good for the people and communities they serve.  

This issue also impacts diversity from the standpoint of recognizing and representing myriad datasets to help AI models respond to unforeseen issues and help ensure organizations fulfill their roles as good corporate citizens from a CSR and an ESG perspective. 
 
Also, in the rush to capitalize on AI, issues of timeliness, privacy, and security must be considered. 
 
As leaders digest all of this, it creates even greater emphasis on areas that team members expect of them, which include: 

• Listening attentively and actively 
• Being curious about how unintended consequences can impact people, communities, and operations 
• Hiring and developing the best talent 
• Ensuring team members are responsible, accountable, and held to the highest standards 
• Thinking big and supporting personal, professional, and organizational growth 

At its core, AI is and will always only be as good as the data itself, and the role of the leader is essential to ensuring that it is free from the myriad issues that could have detrimental effects on the population it is intended to support. When this occurs, the intended benefits of this additive tool will accrue and create a more harmonious environment for all.