Ten Stories of Black Box Model Failures

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Machine learning models have revolutionised various industries, from healthcare to finance, offering unparalleled predictive power. This AI revolution has been supported by the increasing use of black box models, which are known for their lack of interpretability and transparency.

The challenge with black box machine learning models lies in comprehending the reasoning behind an AI system’s predictions or decisions. This predicament is particularly widespread in deep learning models like neural networks, where interconnected nodes process and transform data hierarchically. The complexity of these models and the non-linear transformations they perform make it difficult to trace the reasoning behind their outputs.

History has witnessed the potential catastrophic consequences when these black-box models fail.

In this article, we explore ten notorious cases where black box models failed, leading to disastrous outcomes.

1. Google Photos’ Racist Image Tags (2015)

• Model Used: Black-box deep-learning based image recognition algorithm.
• Data Used: Large dataset of labeled images.
• Failure Impact: Google Photos’ image recognition algorithm incorrectly tagged photos of African-American individuals as “gorillas,” highlighting the risks of biased training data and the challenges of understanding complex neural network decisions (The Guardian).

2. Volkswagen Emissions Scandal (2015)

• Model Used: Black-box engine control software which used proprietary algorithm to detect emissions tests and manipulate results.
• Data Used: Emissions testing data and vehicle performance characteristics.
• Failure Impact: Volkswagen installed software in its diesel vehicles to detect emissions tests and alter the engine performance to meet regulatory standards falsely. When the manipulation was uncovered, it resulted in a global scandal, with over $30 billion in fines and settlements, tarnishing the company’s reputation, and eroding consumer trust in the automotive industry (The Guardian).

3. Microsoft’s Tay AI Chatbot (2016)

• Model Used: Black-box conversational AI which used deep learning and natural language processing algorithms.
• Data Used: User interactions on social media.
• Failure Impact: Microsoft launched Tay, an AI-powered chatbot on Twitter, with the aim of learning from user interactions. However, within hours, Tay began posting offensive and inflammatory tweets due to malicious interactions with users. The lack of interpretability in the model’s decision-making process resulted in a public relations disaster, forcing Microsoft to shut down Tay and issue public apologies for the AI’s inappropriate behavior (The Verge).

4. IBM Watson for Oncology (2017)

• Model Used: Black-box AI system for oncology treatment decisions which used natural language processing and machine learning algorithms.
• Data Used: Medical literature, clinical trial data, and patient records.
• Failure Impact: IBM’s Watson for Oncology was designed to assist oncologists in treatment decisions based on medical literature and patient data. However, it faced criticism for recommending inappropriate and potentially harmful treatments for cancer patients. The lack of transparency in the model’s decision-making process raised doubts about its reliability and impacted the trust of medical professionals (Stat News).

5. Predictive Policing Algorithms (Various Instances)

• Model Used: Black-box predictive policing models using various machine learning algorithms.
• Data Used: Historical crime data, demographic data, and law enforcement activities.
• Failure Impact: Several cities and law enforcement agencies implemented predictive policing algorithms to allocate resources and forecast crime hotspots. However, the lack of transparency in these models led to biased predictions and disproportionate targeting of specific communities, raising ethical concerns and triggering lawsuits for potential civil rights violations (Wired).

6. Tesla Autopilot Accidents (2016–2021)

• Model Used: Black-box autonomous driving system using deep learning and reinforcement learning algorithms.
• Data Used: Sensor data from cameras and radar, environmental data, and human driver inputs.
• Failure Impact: Tesla’s Autopilot, an advanced driver-assistance system, faced scrutiny after accidents involving Tesla vehicles. The lack of interpretability in Autopilot’s decision-making process made it challenging to understand the causes of collisions and fatalities, raising concerns about the safety and reliability of autonomous driving technologies (Business Insider).

7. Amazon’s Biased Hiring Algorithm (2018)

• Model Used: Machine learning-based hiring algorithm.
• Data Used: Historical hiring data and applicant profiles.
• Failure Impact: Amazon developed an AI-powered hiring tool that showed bias against female job candidates. The model was trained on historical hiring data that was predominantly male-dominated, leading to biased recommendations and reinforcing gender disparities in hiring practices (Reuters).

8. Stanford Hospital’s Mortality Prediction Model (2019)

• Model Used: Black-box mortality prediction model using deep learning and electronic health record data.
• Data Used: Patient electronic health records and clinical data.
• Failure Impact: Stanford Hospital implemented a machine learning model to predict patient mortality. However, the model demonstrated inaccuracies in predicting mortality, raising concerns about its reliability and potential consequences for patient care (Healthcare IT News).

9. Apple Card’s Gender Discrimination (2019)

• Model Used: Black-box credit card approval algorithm using machine learning-based credit scoring algorithm.
• Data Used: Financial and credit history data.
• Failure Impact: Several cases emerged where Apple Card’s credit approval algorithm discriminated against female applicants, offering lower credit limits compared to male applicants with similar financial profiles. The lack of transparency in the model’s decision-making process raised ethical concerns and resulted in an investigation by regulatory authorities (The New York Times).

10. UK’s A-Level Algorithm Debacle (2020)

• Model Used: Black-box grading algorithm using statistical models and standardisation algorithm.
• Data Used: Student performance data and historical grading data.
• Failure Impact: In response to the COVID-19 pandemic, the UK government implemented an algorithm to determine A-Level exam results for students. The algorithm used historical school performance data, resulting in a highly controversial algorithm-driven grading system that unfairly downgraded thousands of students’ results. The algorithm’s failure led to public outcry, protests, and ultimately forced the government to abandon the system and revert to teacher-assessed grades (BBC News).

The stories of these ten catastrophic failures of black box models serve as stark reminders of the potential risks associated with Black Box AI systems or Machine Learning models.

The lack of interpretability in these models can lead to severe consequences, ranging from regulatory violations and financial losses to compromising safety and public trust.

As AI continues to play a significant role in various aspects of society, it is crucial for organizations and policymakers to prioritize transparency, accountability, and interpretability to mitigate risks and ensure the responsible deployment of AI technologies.

About the Author

Ishan is an experienced data scientist with Masters + 8 years of consulting, engineering and data-science/analytics experience in diverse industries across USA, UAE, Saudi Arabia, and India, out of which 3+ years in team leadership.