Developing an Ethical Artificial Intelligence Strategy
Artificial intelligence is the future, but it already has a prominent standing in the present. As data science gets more sophisticated and consumers continue to demand a more personalized customer experience, AI is the tool that will help enterprises better understand their customers and audiences. But even though AI has all the potential in the world, if we cannot figure out how to address the ethical challenges that remain, this full potential may never be reached.
As this technology evolves, one question should remain in the minds of all leaders seeking to implement an AI strategy: How can I ethically and responsibly make the most of AI within my organization?
In order to implement and scale AI capabilities that result in a positive return on investment (ROI) while minimizing risk, mitigating biases, and driving speed to value with your AI, enterprises should follow these four principles:
About seven years ago, Gartner released what they referred to as the “Hype Cycle for Emerging Technologies,” which highlighted the technologies it predicted would change society and business over the next decade. Among these technologies was AI.
The release of this report sent companies into a scramble to prove to analysts and investors that they were AI savvy — and many began to implement AI strategies into their business models. However, at times, these strategies proved to be poorly executed and tacked on as an afterthought on top of existing analytics or digital objectives. This is because organizations did not have a clear understanding of the business problem that they were looking for AI to solve.
Only 10% of AI and ML models developed by enterprises are implemented. The historic disconnect between organizations with a problem and the data scientists who can use AI to solve that problem has left AI lagging. However, as data maturity has increased, organizations have begun to integrate data translators into different value chains — such as marketing — to uncover and translate business desires for outcomes.
This is why the first principle of developing an ethical AI strategy is to understand all goals, objectives, and risks, and then to create a decentralized approach to AI within your organization.
There have been many public examples of harmful AI. Organizations large and small have been left with damaged reputations and distrusting customers because they never properly developed their AI solutions to address issues of bias.
Organizations looking to create AI models must take preemptive measures to ensure their solutions do no harm. The way to do this: have a framework in place to prevent any negative impacts on algorithm predictions.
