Organizations should focus on data quality, continuous monitoring, AI explainability, and regulatory compliance to ensure that machine learning contributes towards greater efficiency and alleviates human employees of monotonous tasks rather than succumbing to the same pitfalls.
Machine learning is a system’s ability to analyze and draw conclusions from data, enabling it to perform tasks without explicit instructions. It’s the key to the autonomy that makes artificial intelligence tools so attractive to businesses, which explains why the machine learning market is expected to grow by 35% a year to exceed $1.4 billion by 2034, prompting the need for one million machine learning specialists by 2027.
This critical component of AI is arguably what makes it “intelligent,” imitating human learning through iterative improvement and contextual inferencing. Much like the human brain, however, the technology is as versatile and powerful as it is fallible.
Like its organic counterpart, machine learning can be exposed to biases, make questionable decisions, and clash with societal expectations. These can be frightening possibilities for businesses, but they shouldn’t ward off investment; when machine learning is deployed correctly and responsibly, the potential for value, efficiency, and improved experiences is profound.
As businesses across the globe continue to invest heavily in AI, learning how to leverage machine learning strategically is a nonnegotiable for staying competitive in any industry. Read the warning signs, but don’t let them stop you entirely. Instead, consider these strategies for sustainable and value-generating machine learning.
Avoid bias in training data and outcomes
Machine learning models can perpetuate biases present in training data, leading to unfair or discriminatory outcomes. This is especially important to consider in high-stakes industries like finance, healthcare, or recruitment, where algorithmic biases can lead directly to adverse outcomes for customers, patients, and applicants.
For example, an AI recruitment tool might systematically undervalue candidates from certain demographic groups if the training data reflects historical biases. Organizations are responsible for ensuring algorithmically driven decisions are not susceptible to reinforcing these biases at a massive autonomous scale, requiring strong awareness and prioritization of data quality.
The risk of biased training data can be mitigated by improving the visibility and auditability of algorithmic decision-making. With AI tools, you can monitor key stages of processes in real time and flag disproportionate trends or deviations from set guardrails, thereby alerting a human-in-the-loop (HITL) to prevent biased outcomes before they cause harm. Retrieval-augmented generation (RAG) has also emerged as a useful tool to connect machine learning and AI models to the right data for more reliable and controlled outputs.
Still, like any AI deployment, the effectiveness of these strategies is limited primarily by the quality of the data that fuels it. Imagine a child with unrestricted access to the internet: if they suddenly begin using foul language or reciting questionable information, their parents will want to check the media and data they’re being exposed to. Cleaning up your data is crucial to prevent undesirable outcomes, ideally before they occur.
In order to effectively and proactively monitor these processes, however, innovation teams must be aware of how machine learning models are making decisions in the first place.
Ensure explainability and interpretability
Many machine learning models, particularly neural networks, operate as “black boxes,” making it difficult to understand or justify their decisions.
Imagine an AI-generated healthcare diagnosis that suggests an unconventional treatment plan without offering insights into how the decision was reached. A medical professional will likely be mistrustful and hesitant to act on its recommendation, regardless of whether it’s accurate or not. Even more worrying, a less careful professional might comply with a misguided suggestion without verifying its credibility.
When choosing an AI solution that leverages machine learning, innovation officers should prioritize explainable AI (XAI) tools that are interpretable and transparent in their decisions. Connecting a decision to the data that informed it is key in identifying the potential for erroneous or biased outcomes.
Smaller, specialized AI models are often more explainable than their larger, more general counterparts as they are built for specific purposes and, therefore, have more predictable processes and outcomes.
Investing in XAI doesn’t just make it easy to vet the accuracy of AI systems but also their compliance with standards and regulations.
See also: 10 Essential Python Libraries for Machine Learning and Data Science
Maintain compliance with emerging regulations
The rapid evolution of AI regulations (such as the EU AI Act) creates challenges for organizations in maintaining compliance across jurisdictions. Noncompliance with the EU AI Act, for example, could result in fines of up to 7% of global turnover for prohibited applications, making it a high priority both financially and ethically for businesses to avoid infractions.
Many organizations might be unprepared for the stringent requirements imposed by emerging regulations or lacking in the resources or in-house talent necessary to meet them, with a 2024 Deloitte survey revealing that just 25% of corporate leaders felt “highly prepared” to handle governance and risk issues related to AI. Making matters more complex is the fact that many individual states are introducing their own AI laws. The Colorado AI Act will come into force in less than a year, marking a significant milestone as the first US law to regulate artificial intelligence.
While machine learning is often the underlying target of regulation due to its ability to make decisions based on generalized interpretations of data, the same practices that prevent biases, enable explainability, and ensure accuracy often contribute towards compliance as well. Maintaining high-quality data in appropriate quantities, investing in explainable systems, and specializing AI tools to excel in specific tasks will reduce the risk of noncompliance as well as adverse outcomes in general.
Innovation leaders should conduct proactive AI risk assessments to ensure their systems can sustainably satisfy international standards and, if not, identify where gaps exist. If your organization lacks internal expertise, connecting with third-party independent auditors can be helpful in gaining an objective assessment of your AI infrastructure and regulatory readiness. ForHumanity, for example, is a not-for-profit organization that can provide independent auditing 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 of non-compliant events in workflows in real time.
Conclusion
Machine learning holds enormous potential for value by identifying opportunities to improve, simplify, or automate businesses’ key processes. While its capacity for autonomy carries inherent risk, these risks are often shared by humans, who are also vulnerable to making errors, reinforcing biases, or deviating from established guidelines.
If deployed correctly, machine learning can be more proactively and reliably tailored to excel in its assigned workflows. Innovation officers should focus on data quality, continuous monitoring, AI explainability, and regulatory compliance to ensure that machine learning contributes towards greater efficiency and alleviates human employees of monotonous tasks rather than succumbing to the same pitfalls.
