By Haniyeh Mahmoudian, Sarah Khatry, and Gleb Drobkov

In the past few years, the media has revealed many stories about bias in machine learning (ML) and AI, such as the hiring algorithm that favored men, targeted ads that perpetuated housing discrimination, or the widely-used healthcare algorithm that exhibited significant racial bias. Proactively identifying and mitigating these and other risks or, in other words, following Responsible AI practices, is a top priority for many AI practitioners. …

By Anni Coden, Gleb Drobkov, Jeremy Ferlic, and Emily Serazin

Health systems in developing countries will encounter great strains as they roll out their Covid-19 vaccine programs. Gravely aware of these strains, the WHO recently warned of an imminent “catastrophic moral failure” if these systems fail to provide their citizens with equal access to the COVID-19 vaccine.[1] As data science practitioners, there is something we can do to help.

Based on our experience, we believe that healthcare organizations (HCOs) with sufficient data resources can work with these health systems to integrate personalized medicine and data-driven interventions into their existing workflows…

The use of agile teams has long been a standard for software development in Big Tech. As more industries move to leverage AI and machine learning in their everyday business, the creation of agile teams with well-defined roles and responsibilities can bring similar benefits.

Analytics software development is no cake walk. Projects working with large data sets typically have varied stakeholders with complicated needs and matrixed responsibilities. This can make the process of navigating the expectations of the business, IT and various service-providers rather challenging. …

Gleb Drobkov

Senior Data Scientist, BCG Gamma

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