Key Insights

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  UX researchers working with ML and data science teams often lack domain expertise, requiring strong facilitation and interpretive interview skills.

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  Machine learning product users are usually small specialized expert groups, making statistical rigor and sampling difficult.

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  Trust and interpretability in ML models can be more important than peak accuracy for business adoption.

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  Collaborative shared spaces or cross-functional teams focused on common goals bridge gaps between design, engineering, and data science.

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  Designers help translate data science outputs into actionable, contextual decision support for end users.

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  Prototyping ML products requires believable, realistic data and testing boundaries like false positives early to build trust.

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  Decision culture (focusing on which decisions to support) is a more useful framing than data culture in enterprise AI/ML products.

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  The ecosystem around ML includes not only users and business stakeholders but also data labelers who impact outcomes.

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  The integration of business rules with ML models enhances contextual relevance and facilitates user trust.

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  No-code and rapid data science tools are emerging to help speed experimentation but do not fully replace model development demands.

Notable Quotes