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Artificial intelligence (AI) is opening doors to innovation and transformation in organizations across industries. At the same time, there are calls for greater regulation, oversight, and accountability related to the use of AI because how the technology works and makes decisions isn’t always explainable. In Europe, a high-level group of experts has even proposed instituting “seven requirements for trustworthy AI” as a way to address what the group calls “a major concern for society.”
The use of so-called “black box AI” is under particular scrutiny by those expressing concern about the lack of AI explainability. What is black box AI? In this post, we’ll address that question. We’ll also look at some examples of how black box AI is used and the ethical and security concerns associated with these systems. And we’ll cover a transparent option known as “white box AI” that businesses can more confidently trust.
So, let’s dive in: What is a black box in an AI context? It refers to an AI system whose internal workings or decision-making processes are opaque or not easily understandable to humans. In other words, when you put data into the black box AI system and get outputs in return, you don’t really know how the AI arrived at the conclusions or decisions that it presents to you.
Even so, black box AI is widely used to power a diverse range of applications that are designed to solve complex problems and support data-driven decision-making. Here are some examples of where you can find black box AI systems, along with some reasons why using them can be problematic.
Black box AI is integral to enabling self-driving car technologies. AI can process vast amounts of sensor data in real time and, through deep neural network learning, make split-second driving decisions.
However, it can’t be ignored that self-driving cars have been involved in twice as many accidents per million miles driven as conventional cars. Also, consumers have expressed concerns about the safety of autonomous vehicles and whether technology malfunctions can lead to accidents.
AI, in the form of robotics and automation, has been used in manufacturing for many years, especially in car and aviation assembly. Machine learning and deep neural networks used in black box AI can now optimize manufacturing processes through predictive maintenance, using equipment sensor data to predict when machine components may fail so they can be proactively repaired or replaced.
But if a black box AI model makes a faulty decision that leads to a product defect, downtime, or safety hazard, it may be challenging to identify the root cause of the error and assign responsibility due to the system’s lack of transparency and explainability.
The financial services industry generates and consumes mountains of data. Black box AI algorithms can analyze stock and commodity market data such as pricing and trading volumes to identify trends and execute trades at lightning speed. AI can also run credit models to govern lending.
However, U.S. government regulators have labeled AI “an emerging vulnerability” in the financial system, citing concerns with data security, privacy risks, and more. They also pointed to the risk of generative AI models producing erroneous or misleading outputs known as “hallucinations.”
Some of the most significant ethical concerns about the use of AI for decision-making occur in the healthcare sector, where black box AI models assist healthcare professionals in diagnosing diseases and recommending patient treatment plans. What happens if bias in the AI model results in a misdiagnosis — or worse?
What is an AI black box? It is a powerful tool, to be sure, but also a source of risk. These systems present plenty of challenges that companies should be aware of before they decide to work with them. Every organization using the technology now should ask, “What is that black box in AI doing, and how is it doing it?”
Lack of transparency is one of the greatest concerns about black box AI, and it’s the very reason that regulators and industry experts around the globe are waving the caution flag. The way that black box AI arrives at conclusions is hidden from view and unexplainable. You see what goes into the sausage factory and you see what comes out, but you don’t see how the sausage is made. That’s partly to protect intellectual property, but it also raises valid concerns about whether conclusions made by black box AI systems can be trusted.
Bias is another worry. Without visibility into the “how” and “why” of AI’s decision-making process, how can you know whether the machine learning models in the system are free from bias? This question is causing the military, car manufacturers, healthcare practitioners, and many others to ask serious questions about black box AI models. The potential for bias in black box AI also has implications for employers and hiring practices. How do employers know that the candidates selected for them are the result of unbiased assessments?
Opacity in the black box AI process also raises plenty of questions about accuracy. Lack of transparency makes it virtually impossible to test and validate results from black box AI models. And that, in turn, makes it challenging to ensure that the model is arriving at decisions that are safe, fair, and accurate.
The use of black box AI raises ethical concerns, too, especially in highly regulated industries like finance and healthcare and public-sector segments such as the criminal justice system where transparency and accountability are crucial.
Black box AI models are susceptible to attacks from threat actors who can take advantage of flaws in the models to manipulate outcomes, potentially leading to incorrect or even dangerous decisions. AI models also collect and store large data dumps that hackers can exploit.
Another security concern to be mindful of when using black box AI models is that some vendors of these systems will transfer data to another third party for analysis. The third party your vendor works with may not adhere to good security practices, and thus, your information could be at risk. However, because you are using a black box model, you wouldn’t know that your vendor is transferring your data to a potentially less secure third party as part of their process.
At Invoca, we understand how important security is to our clients. That is why we do not offer black box AI systems or send data to third parties for analysis.
The many questions and challenges surrounding the use of black box AI make the technology’s future uncertain. Do the benefits outweigh the risks? Clearly, the future of black box AI will involve ongoing efforts to mitigate its limitations and enhance its transparency, interpretability, and ethical use, bringing it more in line with explainable AI, also known as white or glass box AI.
There have already been significant regulatory moves in the United States and the European Union (EU) related to black box AI and its potential risks. In the U.S., this activity includes:
Regulatory measures taken or underway in the EU include:
While the future of black box AI is murky, the outlook for white box AI looks bright. White box AI models are explainable, secure, and transparent. The user knows exactly how the AI arrived at its conclusions. At Invoca, we think this is good practice, and it’s why our Signal AI Studio is white box AI.
Invoca has been the leader in delivering conversation intelligence AI since we launched Signal AI in 2017. Our recently introduced Signal AI Studio lets you create custom AI models that you can easily and quickly train to capture exactly the data you need from the many phone conversations your sales and customer service teams have with your customers. And because Signal AI Studio is white box AI, your users can review AI accuracy scores in the Invoca platform and see why our AI made its decisions.
Invoca also addresses security and privacy for AI by ensuring that our tools meet the most stringent compliance standards. Our conversation intelligence AI platform is SOC 2 Type 2 certified and ISO 27001 compliant. It also complies with the U.S. healthcare industry’s Health Insurance Portability and Accountability Act (HIPAA) and meets the EU’s General Data Protection Regulation (GDPR).
Invoca’s conversation intelligence platform also supports two-factor authentication and SAML and has controls on recording, data redaction, and data access. Additionally, we prioritize consumer privacy by handling local storage in both U.S. and European data centers.
What is a black box in AI? Potentially, a significant source of risk for your business. To learn more about Invoca’s white box AI and how our AI is changing businesses for the better, check out these posts:
If you’d like to find out how Invoca’s explainable and secure AI can specifically benefit your organization, schedule a customized demo today.
