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Key Takeaways
- Corrupted training data is a costly threat that undermines AI’s effectiveness and can lead to poor decisions, wasted resources, loss of trust and erosion of competitive advantage.
- Corruption stems from issues like code reuse errors, incorrect data labelling and even intentional sabotage.
- For effective and safe AI systems, leaders must ensure that they are trained properly and constantly monitored for accuracy.
These days, many business leaders will punt artificial intelligence as a main source of gaining an advantage over competitors. They are willing to splurge money on investing in AI and bringing in so-called high-quality teams to develop it.
Beneath all of this is often something unaddressed — widespread data corruption in training datasets. This can compromise your entire operation from the start. It is the destruction of data integrity from the start. It is something that will typically not be featured on a financial statement. However, it can be catastrophic as it impacts ROI, the strategy of a business and the trust of investors.
What causes the corruption?
Data corruption in AI is not just due to mistyped data. It tends to start in the learning process and happens as a result of a few factors. It can happen when a code used to identify a product is used again for an item that may be unrelated. This can cause an AI system to get confused. For example, it may suggest car oil to a customer looking for toys for a baby.
In incorrect data labelling, misleading instructions or tired workers may have misleading information in the form of pictures and writing. The AI model, which is learning, will thus learn from incorrect labels, and its ability to be accurate when it comes to giving advice and instructions will be compromised.
In the competitive business environment that we are in, it’s also possible for others to put misleading information into your systems in order to sabotage your business. They can do this through corrupted information, making changes to the attributes of your images or changes to your text. This can cause confusion to the AI that will be studying your business.
Sometimes the corruption can be hard to identify, and only a skilled technology professional can identify it. When this happens, it may be overlooked as it will be deemed to be functional. The systems will then learn from compromised information. The problems will happen and only be noticed later, when hard work has been done and a considerable amount of money has been spent.
What happens when misleading information is used?
When AI learns from misleading information, a model can still be developed. The final product will not be a downright failure. However, it is bound to malfunction and cause massive costs to your business.
Those who trust the input given by the AI systems may end up investing in areas as advised in decisions created by trusting compromised data. This can lead to wasted money, an overestimation of demand and resource mismanagement.
How misinformation damages systems
Should you rely on misinformation, your teams are bound to make irrational decisions. For instance, using the data, you may make misguided sales pitches and budget plans. This can lead to demand either being underestimated or overestimated. The decisions can appear to be logical at the time when they were made, and you will only discover later that they ended up setting you up to fail, as the data used to make them was deeply flawed.
Moreover, several hours, days, weeks and months could be spent trying to experiment with different ideas and techniques to try and gain performance improvement. This will impact innovation and idea execution.
If you are using AI customer service models, the information that it gives could be misguided or unprofessional. It may give, in the worst-case scenario, harmful or dangerous information to clients. This can lead to complaints and a loss of trust in your product.
How can you make AI systems successful?
The C-suite must always strive to ensure that AI is managed properly and continuously monitored. The accuracy of data is something that should be a core business priority, which is as important as making sure that your budget is accurate. Instead of just investing in up-to-date infrastructure for your business, you must also aim to ensure that the information is regularly monitored by seasoned professionals and is up to date and reliable.
There should be a system in place that tests the quality of data training. The system should be able to ensure that there is no corruption in the data and that it is valid and not compromised. Data, like anything, is also always losing its value. As a result, it must always be cleaned, re-labelled and always learning to stay relevant.
Simply put, if you are not regularly monitoring AI systems in your workplace, it will eventually backfire and spit out garbage. Running a business based on garbage input is bound to end in disaster. By always ensuring that the training data is up to date, business leaders can ensure that the AI in their business is making decisions based on truth.
For effective and safe AI systems, you must always ensure that they are trained properly and constantly monitored for accuracy. This will ensure that your final product is of high quality to stakeholders and customers.
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Key Takeaways
- Corrupted training data is a costly threat that undermines AI’s effectiveness and can lead to poor decisions, wasted resources, loss of trust and erosion of competitive advantage.
- Corruption stems from issues like code reuse errors, incorrect data labelling and even intentional sabotage.
- For effective and safe AI systems, leaders must ensure that they are trained properly and constantly monitored for accuracy.
These days, many business leaders will punt artificial intelligence as a main source of gaining an advantage over competitors. They are willing to splurge money on investing in AI and bringing in so-called high-quality teams to develop it.
Beneath all of this is often something unaddressed — widespread data corruption in training datasets. This can compromise your entire operation from the start. It is the destruction of data integrity from the start. It is something that will typically not be featured on a financial statement. However, it can be catastrophic as it impacts ROI, the strategy of a business and the trust of investors.
