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White Paper

AI Ethics

AI holds tremendous promise for many industries. It can improve accuracy, speed, scalability, personalization, consistency, and clarity in so many business functions.

But many businesses still hesitate to move forward with AI projects. Why? On the one hand, they know that they need to embrace AI to remain competitive. On the other hand, they fear the consequences of getting it wrong after hearing stories of high profile companies making mistakes with AI that have damaged their reputations.

In our white paper, AI Ethics, we take a deeper dive into why these fears are unfounded. We look at how to overcome common blockers to ensure that you build AI that is trustworthy and remains true to your business rules and core values.

Specifically, we dive into the four principles of AI Ethics that help people trust your AI:

  • Principle 1: Ethical Purpose -- How to make sure your AI’s actions have a net good to society.
  • Principle 2: Fairness -- Ensuring your AI’s actions avoid entrenching historical disadvantage and avoid discriminating on sensitive features.
  • Principle 3: Disclosure -- Disclosing sufficient information to an AI’s stakeholders so that they can make informed decisions.
  • Principle 4: Governance -- Where there is risk, apply high standards of governance over the design, training, deployment, and operation of AIs.
  • DataRobot's platform makes my work exciting, my job fun, and the results more accurate and timely – it's almost like magic!
    Omair Tariq
    Omair Tariq

    Data Analyst, Symphony Post Acute Network

  • I think we need to take it upon ourselves in the industry to build the predictive models that understand what the needs and wants of our customers are, and go through the whole curation process, become their concierge.
    Oliver Rees
    Oliver Rees

    General Manager – Torque Data at Virgin Australia

  • At LendingTree, we recognize that data is at the core of our business strategy to deliver an exceptional, personalized customer experience. DataRobot transforms the economics of extracting value from this resource.
    Akshay Tandon
    Akshay Tandon

    VP of Strategy Analytics, LendingTree

  • We know part of the science and the heavy lifting are intrinsic to the DataRobot technology. Prior to working with DataRobot, the modeling process was more hands-on. Now, the platform has optimized and automated many of the steps, while still leaving us in full control. Without DataRobot, we would need to add two full-time staffers to replace what DataRobot delivers.
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