The Importance of Testing AI/ML Applications

The Importance of Testing AI/ML Applications

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The evolving nature of AI models makes their products ambiguous and unpredictable. Quality assurance methods must accommodate the complexity of AI/ML applications and overcome issues related to lack of security, privacy, and trust. Let’s take a look at the approach to testing AI/ML applications and some of the important issues to be aware of.

Verifications & Validations of AI/ML Applications

The standard approach to creating AI models, known as the Standard Cross-Industry Process for Data Mining (CRISP-DM), starts with data collection, preparation, and cleaning. The resulting data is then iteratively used in multiple model approaches before the perfect model is finalized. Testing for this model first uses a subset of the information that has gone through the above process. By feeding this test data into the model and running multiple combinations of hyperparameters or variants on the model to see its correctness or accuracy, the model is supported by appropriate metrics. These test datasets are randomly generated from the original dataset and applied to the model. This process is very similar to new data simulation methods and will determine how future AI models will scale.

'Quality assurance methods must accommodate the complexity of AI/ML applications and overcome issues related to lack of security, privacy, and trust.' -Alice BabsClick To Tweet

Quality Assurance Challenges

There are countless issues that must be addressed with data-driven testing and quality assurance of AI/ML applications. Let’s take a look at a few:

Interpretability

The decision-making algorithm of an AI model has always been regarded as a black box. Recently, there has been a clear trend toward making models transparent by explaining how they arrive at a set of results based on a set of inputs. It aids in understanding and improving model performance and helps recipients understand model behavior. This is even more important in areas where complaints are common, such as insurance or healthcare systems. Some countries also require explanations for decisions made in conjunction with AI models.

Post facto analysis is the key to interpretability. By performing post-analysis on specific instances misclassified by the AI model, data scientists can understand the parts of the dataset that the model actively focuses on when making decisions.

Bias

The decision-making ability of an AI model depends mainly on the quality of the data it is exposed to. There are many cases where bias seeps into how input data or models are streamed, such as Facebook’s sexist ads or Amazon’s AI-based automated recruiting systems that expose discrimination against women.

The historical data Amazon uses for its systems has been heavily skewed over the past decade due to the dominance of men in the workforce and the tech industry. Even large models like Open AI or Code pilot suffer from world bias permeating their models as they are trained on inherently biased global datasets. To remove bias, it is important to understand what the data was selected for and which features contributed to the decision. A bias in the model can be detected by identifying the attributes that excessively impact it. Once these attributes are identified, they are tested to see if they represent the entire dataset.

Safety

According to the Deloitte State of AI in Enterprise Survey, 62 percent of respondents believe cybersecurity risk is an important issue for AI adoption. Forrester Consulting’s Emergence of Offensive AI report found that 88 percent of security industry decision-makers believe offensive AI is on the horizon.

Since AI models are built on the principle of becoming more intelligent with each iteration of actual data, attacks on such systems also tend to get smarter. Things are further complicated by the advent of adversarial hacks, which aim to attack AI models by modifying a simple aspect of the input data down to a single pixel in an image. Such small changes can introduce more severe disruptions in the model, leading to misclassification and erroneous results.

The starting point for overcoming such security issues is understanding the types of attacks and vulnerabilities in the model that hackers can exploit. It is critical to collect literature and domain knowledge on such attacks and create a repository that can predict such attacks in the future. Employing AI-based cybersecurity systems is an effective technique for deterring hackers. AI-based methods can predict how hackers will react, similar to how they predict other outcomes.

Privacy

As privacy concerns such as GDPR, CCPA, and more, become increasingly common across all applications and data systems, AI models are also under scrutiny. Not to mention that AI systems rely heavily on massive amounts of real-time data to make intelligent decisions—data that can reveal a wealth of information about a person’s demographics, behavior, and consumption attributes.

The AI model needs to be examined to assess how it discloses information to address privacy concerns. Privacy-conscious AI models take appropriate steps to de-anonymize, pseudonymize, or use state-of-the-art privacy-enhancing techniques. The model can be evaluated for privacy violations by analyzing how a privacy attacker takes training data input from the model and effectively modifies it to gain access to personally identifiable information. The two-step process of discovering derivable training data through an inference attack and then identifying the presence or absence of PII in the data helps identify privacy concerns when deploying models.

Accurate Testing

Accurate testing of AI-based applications requires extending the concept of quality assurance from the scope of performance, reliability, and stability to new dimensions of explainability, security, bias, and privacy. The international standardization community is also working on this idea by extending the traditional ISO 25010 standard to include the above aspects. As AI and ML model development continue, focusing on all of these aspects will result in more robust, always-learning, and compliant models capable of producing more accurate and realistic results.

Improving the Security of Business Systems with Computer Vision

Safeguarding business assets and information and ensuring the safety of team members should be two of the highest priorities of any business. According to BusinessWire, the value of the investigation and security services market will climb as high as $417.16 billion by 2025. But it’s still challenging for security teams to minimize losses in many different business environments, including retail, fintech, transportation, and other industries because of complex workflows and an increasing number of cyber attacks. Fortunately, thanks to evolving computer vision technologies, maintaining security can be more efficient.?

'Businesses that want to maintain security effectively need to consider adopting these technologies to reduce losses, prevent accidents, and ensure the safety of their teams and clients.' -MobiDevClick To Tweet

Understanding How Computer Vision Works

Computer vision is a discipline within artificial intelligence that aims to emulate how humans observe and understand the visual world. This technology has many applications. It requires data to train computers to understand how to recognize objects and make conclusions from those observations.?

Computer vision is made possible by the following process:

1. The computer must have access to images to analyze. In business security, these are likely taken from a surveillance camera. The higher quality of the image, the more accurate the results.?

2. Data scientists train the system to recognize certain objects within the data. If the computer’s machine learning algorithm detects a match, it flags that area of the image.?

3. The computer then makes decisions based on what it sees, depending on how it was trained to respond.

There are several challenges to this approach. Occasionally objects seen through a camera may throw a false positive. For example, a camera trained to recognize a weapon holstered to a person’s belt might be confused by someone with a holstered cell phone. The accuracy of computer vision is dependent on the quality of the camera, the amount of data used for training, and other variables. To take full advantage of computer vision, businesses need to be aware of these challenges to mitigate their effects.

For example, facial recognition is a popular example of computer vision security. However, processing facial recognition data can create heavy loads on network bandwidth. A potential solution that maintains security needs might be edge biometrics, where AI processing occurs on edge devices instead of at a centralized location. So, before starting with the process of implementing computer vision you need to remember that each case is unique and it requires the involvement of experienced AI engineers to create the most effective solution.?

Business Cases of Computer Vision for Enhancing Security

Computer vision use cases are numerous in security applications. A few examples include theft and fraud prevention, defect detection in manufacturing, traffic incident detection, safety assessment, and dangerous object detection. Let’s dive into each case in more detail.

Theft and Fraud Prevention

Shrinkage from shoplifting can be better monitored and recorded by using computer vision techniques. Businesses like Walmart are already using cameras with artificial intelligence to track theft. If a camera sees that a guest has placed an item in their bag without scanning it at self-checkout, an attendant is called to assist automatically.?

Such a solution can be implemented by adding an AI-powered camera to checkouts. When a customer scans products at the checkout, the camera captures the scanned items and the system generates a total number of items and sends it to the integrated POS system. Then the POS system compares the total number of scanned items with the number generated by the camera and if the numbers don’t match, it sends a notification to the store employee about the potential theft. This enables employees to respond quickly to potentially negative incidents and prevent fraud.

Defect Detection in Manufacturing

At first glance, defect detection doesn’t exactly fit in with other security applications. However, automatically detecting defective items at the factory can help mitigate safety concerns. It can also help prevent sabotage and tampering. These systems can help predict risk as well, which allows businesses to take action on threats before it’s too late.

Defect detection in manufacturing powered by machine learning algorithms allows for finding patterns in a data set and detecting anomalies based on them. This helps prevent human error with less time and effort, resulting in significant cost savings.

Traffic Incident Detection

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Monitoring incidents that occur on the road is extremely important in several contexts, especially logistics, event security, traffic control, and more. Computer vision-enabled cameras can detect crashes, identify suspicious moving and parked vehicles, and automatically respond to potential threats or objects of interest.?

By learning from available data and image streams from traffic cameras, such systems can continuously check the traffic to identify patterns that indicate a possible accident. If the system detects a potentially dangerous scenario, it can alert those responsible or implement pre-programmed responses to alert drivers.

Safety Assessment

Computer vision can be used to ensure safety protocols are enforced at the workplace. For example, in a manufacturing, distribution, or retail backroom environment, a camera can detect if a pallet is placed flat on the floor or is propped up on its side against a wall. Since the latter can be considered a safety hazard, the computer vision system can automatically flag the incident as a ‘near-miss,’ reporting the issue to a supervisor for correction.?

Dangerous Object Detection

Systems equipped with computer vision technologies can be used to detect dangerous objects like weapons or other unauthorized items. This is a challenging application to implement because weapons may be easy to conceal due to the lighting in the environment, the pose of the subject, the perspective of the camera system, occlusion, and much more. Although the technology may not yet be perfect, it can still be used to supplement and improve security efforts alongside humans.?

Wrapping Up – Computer Vision and Security Implications

Businesses have a variety of unique security needs that are often incompatible with a one-size-fits-all solution. Full automation may be effective for certain contexts, like detecting activity in a particular area or detecting defective items. However, a hybrid approach may be the best option for some businesses where computer vision can supplement human operators. Regardless, the technology is continuing to improve, and businesses that want to maintain security effectively need to consider adopting these technologies to reduce losses, prevent accidents, and ensure the safety of their teams and clients.?

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