Governance in AI – a necessary evil or a flywheel to success?

TEXT: TIMO MANSIKKA-AHO

Artificial intelligence keeps creating opportunities for a wide variety of businesses and purposes, but a coin always has two sides. Making the right investments in this emerging tech could deliver a strategic advantage that pays massive dividends. But the wrong bets could open the door to data privacy concerns, legal liabilities, and a whole host of ethical issues. As the pressure for finding fast ways to boost businesses keeps increasing, it is no wonder that decision-makers are downright begging for someone to point them towards the right direction.

As undeniable as the opportunities are, business leaders still wrestle with various challenges in scaling AI across their organizations with full confidence”, Riku Ahlroth, Country Leader at IBM Software, explains. “While the capabilities of AI are widely recognized, there are still several ethical issues to deal with.”

Ahlroth refers to issues such as explainability – whether the concept if AI is fully understood and a relevant reason for decision-making – and bias – whether AI really solves problems or simply does things we want it to do.

“The CEO Study carried out by IBM shows that especially generative AI still lacks trust among decision-makers. Safety and ethical aspects keep raising concerns.”

Appropriate governance could help. When AI activities are monitored, directed, and managed in a universally agreed manner, the reliability would instantly rise.

The good, the bad, and the governance

Data must be governed to track lineage, understand quality, and control access to sensitive fields. Model bias, explainability, and robustness must be constantly measured, and the consistency, transparency, and compli- ance must be ensured throughout the process.

Riku Ahlroth, Country Leader at IBM Software.

“Trustworthy AI is about trusting the data, the models, and the process”, Riku Ahlroth sums up. “That way, everyone can consume data with confidence, control the risks in a smart way and create reliable AI lifecycles from finding and preparing data through building sufficient models all the way to deploying and monitoring performance.”

As self-evident as the role of governance sounds, common acceptance is still to be established.

For the AI enthusiasts that have caught the first wave and riding it at full speed, the dilemma is obvious. With all this governance in place, the cure must work – but doesn’t the patient die at the same time, as the indefinite, albeit uncontrolled, opportunities of AI are so heavily restricted?

Even when it comes to AI, the lunch is never free. To get access to the opportunities, certain obligations must be properly addressed.

The good news is, even that does not have to be complicated. Deep down, it is about restructuring the operating model, streamlining deployment, and ensuring that the business benefits are realized. While an opensource platform provides a solid foundation, an expert who can manage it – and the process – in a controlled way, also when it comes to commercialization, is required to ensure smooth progress.

“At the end of the day, adequate governance leads to better efficiency, improved control, deeper understanding and, eventually, better results”, Riku Ahlroth emphasizes. “When both obligations and opportunities are understood, new business can be created.”

Compared to the potential benefits that wait ahead, the cost is downright minimal. In the long run, governance works for everyone’s advantage. |

Read more at: ibm.com