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Last updated on 2024年7月30日

You're facing conflicting views on algorithm outcomes with stakeholders. How do you find common ground?

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When you're developing algorithms, it's not uncommon to encounter differing opinions on their outcomes from various stakeholders. These can range from the end-users who interact with the algorithm's outputs to the business leaders concerned with its strategic implications. Finding common ground among these conflicting views is essential for the successful implementation and acceptance of the algorithm. It requires a blend of technical expertise, empathy, and strategic communication to navigate the complex landscape of stakeholder interests.

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Sid M.

VP Data, Analytics & AI | Data, Digital and Technology Transformer | Strategy, AI for Healthcare | Innovation at…

1 Understand Concerns

Understanding the concerns of stakeholders is the first step towards finding common ground. Each stakeholder group may have unique priorities and perspectives on what constitutes a successful outcome. For instance, data scientists might focus on the precision and accuracy of an algorithm , while business managers could be more concerned with the algorithm's impact on operational efficiency or profitability. By actively listening and asking probing questions, you can uncover the underlying reasons for their views and begin to address them constructively.

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Sid M.

VP Data, Analytics & AI | Data, Digital and Technology Transformer | Strategy, AI for Healthcare | Innovation at Scale, System Transformation
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The fundamental issue we are trying to solve depends on whether we are addressing a known problem with a specific solution or exploring an unknown problem where the algorithm outcomes are uncertain. If it is a known problem with a known solution, we need to address the conflicting views on algorithm outcomes by listening, clarifying and validating early. On the other hand, if it an exploratory analysis, we should begin with goal definition, identifying feature sets and iterating in an agile manner with an emphasis on model explainability. In both the situations, providing clear feedback and maintaining transpire be essential in influencing stakeholders and running their confidence.

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Leonid L.
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Wat? Can I press 0 for a human curator? This question is nonsensical just like countless others that LinkedIn has posed. What algorithm outcomes? The output of an algorithm is supposed to be predictable and not subjective, if it is implemented correctly, unless there is some randomness involved. Trying to provide answers to this nonsense question is going to pollute the internet with AI-generated garbage.

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M. Scott Davis

Chief Operating Officer
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Navigating conflicting views on algorithm outcomes with stakeholders requires a strategic approach. Let’s address each of the statements: Actively listen to stakeholders. Probe deeper to uncover their underlying concerns. Engage in one-on-one discussions or workshops. Ask questions like, “What specific outcomes worry you?” or “What impact do you expect from this algorithm?”

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2 Explain Clearly

Once you've identified the concerns, it's crucial to explain the algorithm's workings and outcomes in a way that's accessible to all stakeholders. Avoid jargon and technical terms that may not be familiar to non-technical audiences. Instead, use analogies and simple language to convey how the algorithm functions and why it produces certain results. A clear explanation can demystify the algorithm and help stakeholders understand the rationale behind its outcomes.

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M. Scott Davis

Chief Operating Officer
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Translate technical details into plain language. Use relatable analogies. Instead of discussing complex model architectures, explain how the algorithm resembles a recommendation system like Netflix suggesting movies.

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3 Align Objectives

Aligning the objectives of your algorithm with the goals of your stakeholders is a pivotal step in resolving conflicts. This might involve adjusting the algorithm's parameters to better serve business needs or modifying stakeholder expectations to align with what's technically feasible. It's about finding a balance between what the algorithm can deliver and what stakeholders need, ensuring that everyone is working towards a shared vision of success.

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M. Scott Davis

Chief Operating Officer
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Bridge the gap between technical feasibility and stakeholder expectations. If stakeholders desire real-time predictions, discuss trade-offs between accuracy and speed. Set realistic expectations.

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4 Collaborate Closely

Collaboration is the key to harmonizing conflicting views. Involve stakeholders in the development process by seeking their input and incorporating their feedback where possible. This collaborative approach not only improves the algorithm's outcomes but also fosters a sense of ownership among stakeholders. When they feel their voices are heard and their contributions valued, they're more likely to support the final product.

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M. Scott Davis

Chief Operating Officer
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Involve stakeholders throughout the development lifecycle. Regularly share progress updates, involve them in testing, and seek feedback. Co-create solutions.

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5 Offer Alternatives

When consensus seems out of reach, offering alternatives can be an effective strategy. Present different scenarios with various trade-offs, such as optimizing for speed versus accuracy or short-term gains versus long-term benefits. By laying out multiple options, stakeholders can better understand the consequences of different choices and make informed decisions that reflect a compromise between competing interests.

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M. Scott Davis

Chief Operating Officer
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Present multiple scenarios with pros and cons. Option A provides high accuracy but slower processing. Option B sacrifices some accuracy for faster results. Which aligns better with our goals?

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6 Evaluate Together

Finally, evaluating the algorithm's performance together with stakeholders can help build trust and consensus. Set up metrics that reflect both the technical and business aspects of the algorithm's outcomes. Regularly review these metrics with stakeholders to assess whether the algorithm is meeting its objectives. This ongoing evaluation process not only ensures that the algorithm remains aligned with stakeholder needs but also provides opportunities for continuous improvement .

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7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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You're facing conflicting views on algorithm outcomes with stakeholders. How do you find common ground?

1

2

3

4

5

6

7

1 Understand Concerns

2 Explain Clearly

3 Align Objectives

4 Collaborate Closely

5 Offer Alternatives

6 Evaluate Together

7 Here’s what else to consider

Algorithms

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You're facing conflicting views on algorithm outcomes with stakeholders. How do you find common ground?

1

2

3

4

5

6

7

1 Understand Concerns

2 Explain Clearly

3 Align Objectives

4 Collaborate Closely

5 Offer Alternatives

6 Evaluate Together

7 Here’s what else to consider

Algorithms

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