AI and machine learning tools are changing the way companies collaborate with partners and stakeholders. These tools are streamlining processes, improving worker output, and even making products safer and more effective.
It’s no wonder so many ERP vendors and other solution providers are enhancing their systems with artificial intelligence (AI). According to one recent report, 33% of technology providers plan to invest $1 million or more in AI technologies by 2022.
As organizations find more opportunities to implement machine learning capabilities, they would do well to remember that these implementations are just as, if not more, risky than ERP implementations.
Recent accounts have proven that artificial intelligence failures can be catastrophic. We’ll be discussing many of these AI failure stories in today’s blog post. You’ll also learn how to safeguard your project against the same mistakes.
We have multiple software expert witnesses available for provision of reports, depositions, and testimonies.
How Can Artificial Intelligence Fail a Company?
While AI has been a buzzword for some time now, many of its real-world applications are still in their infancy. As such, failures are both common and expected.
While some of these failures completely derailed the companies deploying the technology, others weren’t quite as detrimental. Still, any AI setback casts doubt on this technology.
By reviewing these past mistakes, business leaders planning to implement AI can learn how to do so successfully. Let’s review some top AI failures that deliver powerful, if painful, lessons.
1. Microsoft Tay
Conversations with chatbots have become increasingly lifelike and efficient. If you’ve ever interacted with a chatbot, then you’ve seen the power of AI in action. These computer systems employ natural language processing (NLP) to understand and recreate human language.
About six years ago, Microsoft decided to enter this space. Their chatbot, named Tay, debuted on Twitter on March 23, 2016.
While it sounded promising at first, something went very wrong. Basically, Twitter users preyed on the bot’s rudimentary NLP and found ways to target its design vulnerabilities, manipulating it to learn and repeat inappropriate sentiments.
It didn’t take long for Tay to start mimicking some of the remarks and phrases used on the social media platform, eventually making sexist, racist, and demeaning remarks toward other Twitter users.
In fewer than 24 hours, Microsoft turned it off for good.
2. Amazon’s AI Recruitment Tool
More companies have started to use AI to help identify qualified job candidates.
Amazon was one of the earliest adopters of this technology. In 2014, the company developed specialized software that could automate and streamline the talent acquisition process. Amazon’s software could review applicant resumes in a matter of seconds, identifying the top five candidates for each open position.
One year later, machine learning specialists at the company decided to give their AI-powered recruiting tool a closer look. When they did, they discovered that the system was routinely recommending men for technical roles and passing over resumes from women.
How did this happen? The machine learning algorithms learned the recruitment process by reviewing applications submitted to Amazon over a ten-year period. Most of those resumes came from men, as they tended to historically fill those roles.
As the algorithms learned this pattern, they naturally started to favor men. Amazon tried to edit the program to make it more gender-neutral but ultimately scrapped the tool.
3. Hong Kong Real Estate
Picture this: You’re a real estate tycoon in Hong Kong, and you need someone to handle a portion of your money so you can ramp up funds. Instead of hiring a financial advisor, you decide to purchase an AI system, instead. The robot ends up draining money from your account to the tune of $20 million USD every day.
It sounds like something out of a sci-fi novel, but that’s exactly what happened to Samathur Li Kin-kan between 2017 and 2018.
In an effort to recoup some of his losses, he sued the fintech firm behind the company for $23 million USD, claiming the firm overstated the bot’s capabilities.
What is “Explainability?”
Put simply, “explainability” is the ability to explain how an AI machine made a particular decision.
As a bot learns a new concept, it relies on key features and attributes to do so, but these attribution methods aren’t universal. While one might work on a particular neural network, it could be deemed irrelevant on another. As a result, bots are left with conflicting information that ultimately skews their judgment.
Moving forward, analysts predict that developers will need to create complex databases of accurate explanations, which they can search for answers to understand why a bot made a particular decision.
This information could help organizations more quickly recover from AI failure by understanding the logic behind each robotic decision.
Common Mistakes Behind Artificial Intelligence Failure
1. Using the Wrong Data
In the rush to implement AI at an organization, business leaders often grab any data they can find and try to use it in their machine learning application. Then, they wonder why it isn’t generating insights from that information.
For data to be actionable and AI-ready, it must be clean and accurate. Even the largest and most robust dataset in the world would be unusable if it’s outdated, incorrect, or incomplete.
Not only must the data be free of defects, but it must also be multi-faceted enough to establish readable patterns.
2. Using AI as a Quick Fix
Often, business problems exist because there’s an issue with an existing workflow or process. While AI may be able to help solve some of these roadblocks, it isn’t a band-aid.
More often, business process reengineering is required to truly understand and fix inefficiencies.
As you look for areas in which to use AI, remember that, by itself, AI can’t fix your operations. Furthermore, it can’t fix your operations overnight, even with a focus on process improvement.
3. Operating in a Silo
Sure, your data science team might be able to complete an AI project without any outside help. Yet, what would happen if the system configuration was misaligned with your business needs?
We recommend including your operations staff in the project from the very beginning. This includes process engineers, plant operators, and warehouse managers, among others. These are the people who understand the data and its business context.
4. Emphasizing Technology Over People
It’s true that AI is exciting technology. However, it’s easy to become so focused on technology that you forget there are real people using it.
An AI project can often stir up feelings of uneasiness within your departments, especially among employees who fear that the technology may lessen or even replace their role. Without a focus on organizational change management, these uneasy employees will be reluctant to learn and embrace new software.
Avoid Artificial Intelligence Failure
As organizations learn more about how AI works and the potential it holds, we can expect that it will become more common in workplaces around the world.
It’s our hope that these organizations will take steps to avoid artificial intelligence failure by developing a detailed project plan, ensuring organizational alignment, and keeping their employees engaged in the change.
Our team of enterprise software consultants can help you harness the power of AI to transform your operations. Contact us below for a free consultation.