Posted on: February 5, 2021 | 1 min read

The Sociotechnical Effects of AI: Why Data Science Requires Governance

Artificial intelligence’s inclusion within businesses processes have made major strides over the last decade. Though with new, frequent releases and employee adoption, it can be hard to maintain your models effectively and ensure access is restricted to the right individuals.

Alex Hagen, Sr. Data Science Consultant at CCG states “In many ways, the data science process is a process of trial and error. It is an iterative process of testing new features, new algorithms, new parameters resulting in the best model for our data and for your business needs."

As we read in the eBook Steps to Effective Enterprise Data Science “Models should be subject to monitoring to ensure they continue to provide value”. Model Governance does just that. Taking into consideration regulatory standards and model maintenance, model governance ensures that your AI processes have the right access controls, assumptions settings, data inputs and model versioning, amongst other things.

Just as analytics should be supported by effective data governance, your AI initiatives should too. Microsoft recently released an article around their Responsible AI Program which discusses the sociotechnical impacts of AI systems. “The basis for our responsible program at Microsoft: a governance structure to enable progress and accountability; rules to standardize our responsible AI requirements; training and practices to help our employees act on our principles and think deeply about the sociotechnical impact of our AI systems; and tools and processes for implementation”. States Natasha Crampton, Microsoft’s Chief Responsible AI Officer.

Governance should be put in place to both support the ideation and development of insights in your business to ensure your AI models are running effectively and driving results. Governance should also be set in place to ensure that personal identifiable information and customer data is secure and restricted to only those who need access.

Read more about model maintenance and the inclusion of governance in data science within our whitepaper: Steps to Effective Enterprise Data Science.

Written by CCG, an organization in Tampa, Florida, that helps companies become more insights-driven, solve complex challenges and accelerate growth through industry-specific data and analytics solutions.

Topic(s): Featured , Data Science
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