Data science fails: Building AI you can trust

Eight key factors for successful AI implementation

How to build trustworthy AI - whitepaper from DataRobot
whitepaper
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The game-changing potential of artificial intelligence (AI) and machine learning is well-documented. AI has the power to transform countless industries — including the healthcare, banking, insurance, and public service sectors, to name just a few — by introducing new efficiencies and revealing new opportunities for companies to solve problems.

Any organisation that is considering adopting AI must first be willing to trust in AI technology. Organisations must feel confident that human error did not inadvertently contribute to AI bias that resulted in inaccurate or misleading findings.

This new DataRobot whitepaper, "Data Science Fails: Building AI You Can Trust", outlines eight important lessons that organisations must understand to follow best data science practices and ensure that AI is being implemented successfully.

Download the report to gain insights including:

  • How to watch for bias in AI
  • Why your organisation’s values should be built into your AI
  • How human errors like typos can influence AI findings
  • The optimal level of disclosure to AI stakeholders

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