In my previous article, I’ve covered some general considerations about the ethics of Artificial Intelligence in L&D as such systems are becoming increasingly present in organizations. The discussion, of course, is extensive, but my focus is mainly on employing AI algorithms for corporate talent development programs.
The first thing that is very important when a company starts using AI for talent development is to make sure that all employees are treated fairly, with no bias against certain groups, regardless of the nature of their particularities. These systems need to be genuinely inclusive and offer an even ground while identifying and analyzing new types of differentiation.
It all comes down to fairness, but there are a few other relevant principles to consider:
Ownership of the process
AI systems don’t just create themselves. I know that some say it’s just a matter of time until they do, but while I admit to not being a computer science buff, that doesn’t sound very plausible to me. When it comes to organizational L&D programs, a team will create the algorithms.
Whether that team is internal or outsourced, it’s an absolute must to get a clear understanding of who the AI owners are. The company has to completely be on board with the processes that they support and their results. Most important of all, the organization has to know how this will impact employees.
It’s best to have formal training so that each person in the organization understands how AI is used in L&D programs.
Accountability for employee data
The whole point of using AI for L&D programs is to be more efficient. AI can procure and process large quantities of data, factoring in many variables and producing spot-on evaluations and proposed courses of action. From one point of view, this is amazing.
However, there is a general and genuine worry that all this could turn into George Orwell’s “1984”.
The initial training about what AI does and why is a first step in making sure employees understand the systems. Taking it one step further, it’s essential to allow employees to access, update, and even edit their data.
It’s important to generate an environment where people are comfortable with AI and see that it is reliable and useful.
Transparency leads to trust
For people to see that AI is reliable and useful, they actually need to see it. The L&D function has to act proactively and determine which data will be collected, how it will be stored, what will be done to protect all that information. Plus, they need to know who will have access to it.
Every organization will have specific standards that state the level of control employees can have over how this data is used. For the individuals who will have to access and work with that data, information security and compliance training are compulsory. Other items to be considered are authentication protocols, access card and password protection, and secure areas.
Furthermore, there should be a comprehensive data security strategy detailing how talent data will be stored and curated, contingencies for a breach, and plans for evaluating and updating the system.
The word “revolution” has been overused in the past decade, losing some of its significance along the way. It is, however, foreseeable that the introduction of AI in organizations will have a revolutionary quality. Even if the change does not happen overnight, the L&D team will have to be in the middle of this shift.
Due to the tremendous impact that AI implementation will have on the human component of the organization, learning and development specialists must be ready to swoop into action. First, learning specialists will have to be actively involved in the design of the AI systems, then they will have to ensure that all employees are offered training and guidance for the adoption of AI.
The L&D function is in a decisive position when it comes to implementing AI and its impact on organizations. Since talent development is an integral component of HR, it’s natural to put the needs and expectations of people first and ensure that the ethical principles of fairness, transparency, and accountability are properly observed.