December 2021 Summaries

In 2022, the field of Artificial Intelligence (AI) is expected to face challenges such as increased fairness and bias issues in AI systems, a need for better ML observability, and a rising demand for citizen data scientists. However, there are also opportunities for growth, including the adoption of ML monitoring and observability solutions, an increase in specialized ML infrastructure tools, and a potential talent crunch that could drive innovation in educational and job opportunities. To navigate these changes effectively, it is crucial to focus on making the industry more fair, inclusive, diverse, and transparent.

The growing complexity of machine learning models has made it increasingly difficult to understand why a model makes certain predictions, especially as these predictions can have significant impacts on our lives. Explainability is a technique designed to determine which features led to a specific model decision. It does not explain how the model works but offers a rationale for human-understandable responses. This piece aims to highlight different explainability methods and demonstrate their incorporation into popular ML use cases.

Click-through rate (CTR) models are critical for digital advertisers, with the average CTR in Google AdWords across all industries being 3.17% on the search network and 0.45% on the display network. However, machine learning systems can be impacted by various factors such as contextual relevance, user attributes, time of day or seasonal fluctuations, and data quality issues. To address these challenges, it's essential to implement best practices for ML monitoring and observability with CTR models, including tracking key metrics like log loss, precision recall AUC, and time series of predictions versus actuals. By identifying root causes of performance degradation and adaptingively retraining models, teams can ensure their CTR models stay relevant and effective in the ever-changing digital advertising landscape. Effective data quality assurance is also crucial to prevent "garbage in, garbage out" scenarios, where inaccurate or missing data can significantly impact model performance.

The article discusses the importance of maintaining high-quality data for machine learning (ML) models and how modern MLOps solutions need to address both code and data aspects. It highlights that ensuring good data quality is a continuous process, requiring ongoing investment. The article delves into the key dimensions of data quality, which include accuracy, completeness, consistency, privacy and security, up-to-dateness, relevance, reliability, timeliness, usability, and validity. It further explores how these dimensions can be addressed for structured and unstructured data using ML observability and Data Operations platforms respectively. The article concludes by emphasizing the benefits of investing in data quality management for unlocking the potential of an organization's structured and unstructured data.

Can AI Help Make Social Media More Accessible, Inclusive and Safe?` ShareChat, a rapidly-growing social media unicorn valued at over $3 billion, is leveraging AI to build a more inclusive and diverse platform for its 160 million active monthly users in South Asia. As the Lead AI Scientist, Ramit Sawhney, aims to democratize AI and create a safer online space by tackling issues like bias, hate speech, and preventing abuse. The company's unique vantage point on ethics and fairness in AI informs its approach, prioritizing human-centered design and considering diverse perspectives. By leveraging AI for personalization, detecting abusive content, and monitoring models, ShareChat is creating a more accessible and inclusive social media platform that balances performance with safety and ethics.

Deb Liu, President & CEO of Ancestry, shares her vision for the company which includes expanding its reach to everyone around the world who cares about their family history and making it easier for customers to craft stories by finding their history and telling their story. She also discusses her upcoming book "Take Back Your Power: 10 New Rules for Women at Work" which aims to help women thrive in an imperfect world, build allies, and take back their power where they can. Liu emphasizes the importance of building teams and overcoming biases in order to promote diversity and women in tech. She highlights the need for companies to allow failure as a learning experience and iterate quickly to improve products and services.