Section 2.1: Amazon's Recruitment Tool

Amazon's AI-driven recruitment system was scrapped after revealing biases against female candidates. The company created an automated tool to sift through resumes collected over a decade. However, due to the male-dominated tech industry, the AI exhibited biases against women, downgrading resumes with terms like "women's" and favoring male-associated words. Although Amazon made attempts to fix these biases, they ultimately discontinued the project in early 2017, stressing that the system was never used for actual hiring decisions.

Key Lesson: Bias inherent in training data can manifest in machine learning models, leading to unintended discriminatory effects. This underscores the necessity for diverse and representative datasets.

Section 2.2: Google’s Diabetic Retinopathy Tool

Google Health's AI for detecting diabetic retinopathy performed poorly in real-world conditions compared to controlled environments. Despite showing over 90% accuracy in lab tests, the system struggled with image quality and slow internet connections, leading to a rejection of over 20% of scans. Nurses faced frustration when they felt the rejected images were valid, causing delays in diagnoses.

Key Lesson: AI tools must be adapted to real-world settings, taking into account factors such as image quality and the availability of internet connectivity.

Section 3.1: Zillow's iBuying Algorithm

Zillow faced considerable losses due to its home-flipping business, which relied on flawed machine learning models for property valuation. Their system, known as "Zestimate," inaccurately predicted home prices, leading to overpriced offers, particularly during the volatile real estate market spurred by the COVID-19 pandemic. This ultimately resulted in the shutdown of Zillow's iBuying operations with projected losses of $380 million.

Key Lesson: Ongoing monitoring, assessment, and retraining of models are essential to address data drift and ensure accurate predictions.

Section 4.1: Stable Diffusion Biases

An analysis of Stable Diffusion, a text-to-image model, revealed significant racial and gender biases in the thousands of images it generated for job titles and crime. A study found that images for high-paying roles predominantly featured lighter skin tones, while darker skin tones were more often associated with lower-paying jobs. Gender representation was also skewed, with men depicted far more frequently than women in high-paying roles.

Key Lesson: It is crucial to audit the data used for machine learning. If biased images are included in training datasets, future models may perpetuate or even amplify those biases.

Lessons Learned from Machine Learning Gone Wrong - Janelle Shane - YouTube

This video discusses various lessons learned from high-profile machine learning failures, emphasizing the importance of understanding the limitations of these technologies.

Section 5.1: ChatGPT's Fabricated Citations

A lawyer utilized ChatGPT to assist with legal research but ended up with entirely fabricated court cases that did not exist. When preparing documentation for a lawsuit regarding airline negligence, the lawyer submitted citations to non-existent cases, leading to confusion among the judge and opposing counsel. This incident highlights the risks of relying solely on generative models without human oversight.

Key Lesson: The necessity for human verification of outputs from generative models like ChatGPT is crucial, as their inaccuracies can result in significant legal repercussions.

Data and AI in the Real World - Michael Berthold - YouTube

In this video, Michael Berthold discusses the practical implications of data and AI, providing insights into their real-world applications and challenges.

Section 6.1: IBM Watson's Cancer Treatment Recommendations

IBM's Watson, once hailed as a revolutionary tool for cancer research, has been criticized for providing unsafe and incorrect treatment recommendations. A notable incident involved recommending medication for a patient with severe bleeding—a suggestion that could have worsened their condition. The training data fed into Watson, often based on hypothetical cases, led to recommendations that did not reflect real patient scenarios.

Key Lesson: Ensuring high-quality and representative training data is critical in machine learning, especially in sensitive areas like healthcare to prevent harmful outcomes.

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