Organizations need to focus on data quality, continuous monitoring, AI explainability, and regulatory compliance to ensure that machine learning contributes to increased efficiency and eases monotonous tasks to human employees rather than succumbing to the same pitfalls.
Machine learning is the ability to analyze and draw conclusions from data, allowing tasks to be performed without explicit instructions. This is key to autonomy that makes artificial intelligence tools so attractive to businesses, explaining why the machine learning market is expected to grow by 35% per year to over $1.4 billion by 2034, prompting the need for 1 million machine learning specialists by 2027.
This important element of AI is undoubtedly what makes it “intelligent” and mimics human learning through iterative improvement and contextual reasoning. But like the human brain, This technology is just as versatile and powerful as it is mistaken.
Like organic counterparts, machine learning can be biased, making suspicious decisions and conflicting with social expectations. These can be scary possibilities for businesses, but you should not avoid investing. When machine learning is deployed correctly and responsibly, the potential for value, efficiency and improved experiences is serious.
With companies around the world continuing to invest heavily in AI, learning how to strategically utilize machine learning is unnegotiable to stay competitive in any industry. Read the warning signs, but don't let them stop you completely. Instead, consider these strategies for sustainable and value-generating machine learning.
Avoid biasing training data and results
Machine learning models can perpetuate biases present in training data, leading to unfair or discriminatory outcomes. This is particularly important to consider in high-stake industries such as finance, healthcare and recruitment, where algorithm bias can directly lead to negative impacts for customers, patients and applicants.
For example, AI recruitment tools could systematically underestimate candidates for a particular demographic group if their training data reflects historical bias. Organizations are responsible for ensuring that algorithm-driven decision-making is not affected by enhancing these biases on large autonomy measures, and require strong recognition and prioritization of data quality.
The risk of biased training data can be reduced by improving the visibility and auditability of algorithmic decision-making. AI tools allow you to monitor critical stages of the process in real time and flag unbalanced trends or deviations from the set guardrail, thereby illuminating the loop (HITL) to prevent biased results before causing harm. Searched Generation (RAG) has also emerged as a useful tool for connecting machine learning and AI models to the right data, and connecting more reliable control outputs to the right data.
Yet, like AI deployments, the effectiveness of these strategies is largely limited by the quality of the data that drives it. Imagine a child with unlimited access to the internet. If you suddenly start using foul language or reciting suspicious information, your parents will want to check out the media and data they are exposed to. Cleaning up your data is important to prevent unwanted outcomes.
However, in order to effectively and proactively monitor these processes, innovation teams need to be aware of how machine learning models make decisions in the first place.
Check for explanability and interpretability
Many machine learning models, especially neural networks, act as “black boxes,” making it difficult to understand or justify decisions.
Imagine an AI-generated healthcare diagnosis that suggests an unconventional treatment plan without providing insight into how the decision was reached. Healthcare professionals will be distrustful and hesitant to act on its recommendations, whether it is accurate or not. Even more worrying, less careful experts may follow misguided suggestions without verifying their reliability.
When choosing an AI solution that utilizes machine learning, innovation officers must prioritize interpretable, transparent, and explainable AI (XAI) tools in their decisions. Connecting a decision to the data that informs you of a decision is key to identifying possible incorrect or biased outcomes.
Smaller, specialized AI models are often more explained than larger, more general counterparts, as they are built for a specific purpose, and therefore have more predictable processes and results.
Investing in Xai not only makes AI systems more accurate, but also makes it easier to comply with standards and regulations.
reference: 10 essential Python libraries for machine learning and data science
Maintain compliance with new regulations
The rapid evolution of AI regulations (such as the EU AI Act) creates challenges for organizations to maintain compliance across jurisdictions. For example, violations of EU AI law could result in fines of up to 7% of global sales for prohibited applications, making businesses a financial and ethical priority to avoid violations.
Many organizations may not be prepared for the strict requirements imposed by new regulations or the strict requirements that lack the resources or the personnel needed to meet them, and in a 2024 Deloitte survey, only 25% of corporate leaders feel they are “highly prepared” to deal with governance and risk issues related to AI. What makes the problem even more complicated is the fact that many individual states have their own AI laws in place. The Colorado AI Act comes into effect less than a year later, marking a significant milestone as the first US law to regulate artificial intelligence.
Machine learning is often the fundamental target of regulation due to its ability to make decisions based on generalized interpretations of data, but it is the same practice that prevents bias, allows for explanability, and accuracy also contributes to compliance. Maintaining the right amount of high quality data, investing in explanatory systems, and specializing in AI tools that are good at specific tasks reduces the risk of violations and adverse outcomes.
Innovation leaders need to conduct proactive AI risk assessments to ensure that systems can sustainably meet international standards and identify where gaps exist. If your organization does not have internal expertise, connections with third-party independent auditors can help you obtain an objective assessment of AI infrastructure and regulatory preparation. For example, Forhumanity is a nonprofit organization that can provide an independent audit of AI systems to analyze risk.
AI tools for process monitoring and improvement can also be customized to help achieve and maintain compliance by alerting businesses with non-compliant events in their workflows in real time.
Conclusion
Machine learning retains great potential for value by identifying opportunities to improve, simplify or automate key processes in your business. The ability to autonomy poses inherent risks, but these risks are often shared by humans. Humans are also vulnerable to error creation, increased bias, or deviations from established guidelines.
When deployed correctly, machine learning can be more aggressive and reliably adjusted to be better in the assigned workflow. Innovators should focus on data quality, continuous monitoring, AI explainability, and regulatory compliance to ensure that machine learning contributes to increased efficiency and eases monotonous tasks to human employees rather than succumbing to the same pitfalls.
