Day by day, AI and its complexity is evolving at breakneck speed. With this growth and newfound risks, one thing is clear: We need standardized regulation for AI.
The reality? Government regulation is not keeping pace with the industry and companies can no longer afford to wait. At the same time, companies also can’t afford to develop and deploy potentially harmful AI solutions.
In the absence of AI regulation, companies can and should follow their own set of internal AI best practices to ensure that their use of AI remains ethical, transparent, and accountable. Here are a few steps you can take when developing your own.
Establish an AI governance strategy that outlines the principles, policies, and procedures for AI development, deployment, and maintenance. The framework should cover aspects such as: AI use cases, business goals, reliable KPIs, data privacy, security, bias mitigation, transparency, and accountability. When creating this strategy, be sure that the terms are clearly understood by everyone involved—avoiding unnecessary jargon will prevent future confusion and disappointment.
Although we’ve seen a number of big brands cut their AI ethics teams, these committees (or at the very least, designated individual) are critical to ensuring compliance with ethical and legal standards, while also monitoring AI performance. Trust us: Your company’s reputation and the wellbeing of your end users are worth the investment in a dedicated ethics team or champion.
AI builders—data scientists, data engineers, and developers—and AI users—employees and companies—have a shared responsibility to create trust in the models they’re designing and deploying. Whether it is cultivated by identifying the right use case for AI or improving the team’s AI literacy, this trust is what ultimately leads to a greater understanding and adoption of AI.
Oftentimes, companies and their various stakeholders have low trust in AI because they do not fully understand its capabilities. In other words, they have low AI literacy.
AI literacy involves having a basic understanding of AI technologies, being aware of their ethical and social implications, and understanding the potential impact it has on society, industries, and jobs.
To improve AI literacy, invest in AI education and training for employees to help them understand the potential benefits and risks of AI. Training can cover topics such as AI basics, ethical considerations, data privacy, and cybersecurity.
Frequently testing for bias, fairness, accuracy, and reliability should be a mandatory task throughout all stages of AI design and development. Thanks to the accessibility of third-party data and foundational models, developers can spend less time on acquiring data and building the model, and more time on stress testing a model to understand where it might fail.
As AI continues to rapidly evolve, there is an urgent need for standardized regulation. However, as we wait for these regulations to come to fruition, it’s our responsibility to build and follow best practices for ethical AI design and development.
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Contributor: Nicole McCaffrey is the CMO at Pandata.