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Responsible AI for a Sustainable Data Future

Dataiku is the basis of a responsible AI strategy, ensuring that models are accountable, architecture and infrastructure is sustainable, and data processes are governable.

Going from producing one machine learning model a year to thousands is well within the average company’s reach, and operationalization has made it possible for a single model to impact millions of decisions (as well as people). On the surface, there of course aren’t any businesses that plan to do irresponsible AI; but on the other hand, they aren’t doing anything to explicitly ensure they are responsible, either — and therein lies the problem.

Why Does Responsible AI Matter?

In practical terms, responsible AI matters because for some industries (financial services, healthcare, human resources, etc.), it’s a legal requirement and under growing scrutiny from regulators. Even if compliance with requirements asking for white-box solutions, interpretability, and proving efforts to eliminate bias isn’t required, it’s good business for anyone because it lowers risk.

I believe that when it comes to AI technology, software vendors have a responsibility here too. AI technologies should make it costly to not see bias or other problems in AI systems. Human responsibility should be explicit, and software systems should prompt this change.”

– Florian Douetteau, Dataiku CEO, What It Will Take to Make AI Likable?

But responsible AI is also important because it’s what will make organizations’ AI systems stand the test of time and ensure that they are not shattered by inevitable future developments in the space (like legislation, but also new technologies). 

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The Top 5 Myths on Responsible AI


3 Pillars of Responsible AI

Responsible AI covers three main dimensions:

1. Accountability: Ensuring that models are designed and behave in ways aligned with their purpose.

Lately, the news has been littered with stories of AI gone wrong. Children are taught from a young age that subjects like science and math are all objective, which means that inherently, people believe that data science is as well — that it’s black and white, an exact discipline with only one way to reach a “correct” solution, independent of who builds it.

Accountability comes down to eliminating potential bias and making AI human-centered — getting the right people in the room to ensure models do what they’re meant to do. Introducing unintended biases into models that spiral into PR disasters is a huge risk for enterprises who don’t take responsible AI into account.

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2. Sustainability: Establishing the continued reliability of AI-augmented processes in their operation as well as execution.

Sustainability includes introducing at a minimum:

  • Model maintenance
  • Introducing an elasticity that allows for the incorporation of the latest technologies
  • Processes to recreate and reuse work to increase efficiency

3. Governability: Centrally controlling, managing, and auditing the the Enterprise AI effort.

Today’s enterprise is plagued by shadow IT; that is, the idea that for years, different departments have invested in all kinds of different technologies and are accessing and using data in their own ways. So much so that even IT teams today don’t have a centralized view of who is using what, how. This is an issue that becomes dangerously magnified as AI efforts scale.

Dataiku: The Platform for Responsible AI

Dataiku is one of the world’s leading AI and machine learning platforms, supporting agility in organizations’ data efforts via collaborative, elastic, and responsible AI, all at enterprise scale.

At its core, Dataiku believes that in order to stay relevant in today’s changing world, companies need to harness Enterprise AI as a widespread organizational asset instead of siloing it into a specific team or role. 

For companies looking to build responsible AI, Dataiku offers:

  • Custom data protection policies and external regulations compliance features, including the ability to document data sources with sensitive information, and enforce good accountability practices.
  • Enterprise-level security and data governance features, organizing all data tasks into projects with built-in documentation.
  • Advanced monitoring and dedicated dashboards to help administrators, IT, or project managers keep track of global activity, datasets’ size and location per repository, and cluster/DB tasks activity.
  • A white-box approach to AutoML.
  • Connectors to any data storage systems to ensure a sustainable AI strategy no matter where data is stored.
  • … and more!

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