Here's how you can provide AI professionals with objective and unbiased feedback.

Before diving into feedback, establish clear goals with the AI professional. It's essential to understand what they aim to achieve with their AI project or skill set. Once the objectives are clear, your feedback can be tailored to guide them towards those goals. This approach ensures that your input is relevant and aligns with their aspirations, making it more likely to be received positively and acted upon. Collaboratively set specific, measurable, achievable, relevant, and time-bound (SMART) goals to provide a structured framework for evaluation. By aligning feedback with these predefined goals, you can offer constructive insights that are objective and actionable, helping the professional see a clear path to improvement.

When I first started providing feedback to an AI developer, I focused on technical details without understanding their goals. They were aiming to create an AI to assist teachers, but my feedback was centered on improving algorithm efficiency. Realizing the disconnect, I asked about their objectives and learned their primary goal was user-friendliness for non-technical users. I then tailored my feedback to focus on usability and intuitive design. The developer appreciated the shift, resulting in a more practical and successful AI tool. This taught me the importance of aligning feedback with the recipient's goals.

Advocate for a process of continuous improvement, where feedback isn't just a one-time event but an ongoing dialogue that refines AI capabilities iteratively.

Establishing clear goals at the outset ensures that feedback is not only relevant but also actionable, aligning with the AI professional's specific aspirations and project objectives. This targeted approach fosters meaningful progress.

Advocate for iterative adjustments to algorithms based on empirical findings, fostering a systematic approach to optimizing AI performance through targeted refinements.

Providing specific feedback to AI professionals not only clarifies the problem but also accelerates the troubleshooting process, leading to more efficient model improvements and better overall performance.

When giving feedback to AI professionals, be specific. Instead of general comments, highlight exact parts of their AI models or algorithms that need improvement. For example, if a machine learning model isn't doing well, point out specific data features that could enhance its accuracy. This detailed approach helps them understand the issue clearly and how to fix it.

In my projects I generally provide specific feedback at the right time so that it can help team members and colleagues to take corrective action and align the project deliverables as per the objectives/ goal set , acceptance criteria's, quality standards and timeline.

Utilize specific performance metrics like precision and recall rates to substantiate feedback, offering concrete benchmarks for assessing AI system effectiveness and guiding improvement efforts.

Using data-driven metrics like precision and recall ensures feedback is objective and actionable, enabling AI professionals to identify specific areas for improvement and track progress effectively.

Data brings credibility and measurability to feedback. ??Use performance metrics such as accuracy, precision, recall, F1 score, and AUC-ROC to give specific, measurable feedback. ??Compare current performance with past data or benchmarks to provide context. ??Use confusion matrices, error rates, and misclassification reports to provide detailed feedback on specific types of errors. ??Incorporate qualitative data from user surveys, feedback forms, and usability studies to provide a more comprehensive evaluation. ??Use graphs, charts, and visual reports to make the data more understandable and accessible. ??Track and discuss performance trends over time to identify patterns and long-term improvements or issues.

In AI, data is king. When giving feedback, use data to support your points. For instance, if you're evaluating an AI system's performance, refer to metrics like precision or recall rates. This makes your feedback objective and provides clear benchmarks for the AI professional to aim for. Data-driven feedback removes ambiguity and helps focus on measurable outcomes.

I focused on technical details without understanding their goals. They were aiming to create an AI to assist teachers, but my feedback was centered on improving algorithm efficiency. Realizing the disconnect, I asked about their objectives and learned their primary goal was user-friendliness for non-technical users. I then tailored my feedback to focus on usability and intuitive design. The developer appreciated the shift, resulting in a more practical and successful AI tool. This taught me the importance of aligning feedback with the recipient's goals.

Encouraging dialogue not only fosters mutual understanding but also accelerates innovation by integrating diverse viewpoints, ultimately leading to more robust and effective AI solutions.

Effective feedback for AI professionals isn't just about one-way communication; it's about fostering a collaborative environment where ideas can be exchanged freely. By encouraging dialogue, both the feedback provider and the AI professional can gain valuable insights and drive innovation: 1. Create a two-way conversation: Encourage AI professionals to share their thoughts and reasoning behind their approaches. 2. Reveal insights: Open dialogue can uncover the decision-making process and create learning opportunities for both parties. 3. Provide nuanced feedback: By understanding the AI professional's perspective, you can offer more tailored and respectful guidance for improvement.

You can be humble and as open in your opinions at the same time. Honest feedback is most likely well-received and appreciated if you are willing to share with the most humble, respectful and supportive manner and when your team knows that for sure.

Feedback should be a two-way conversation. Encourage AI professionals to share their thoughts and reasoning behind their approaches. This dialogue can reveal insights into their decision-making process and open opportunities for learning on both sides. Engaging in a discussion helps you understand their perspective and provide more nuanced feedback that respects their expertise while guiding them towards improvement.

Encouraging dialogue when providing objective and unbiased feedback involves creating a two-way communication channel. Start by presenting your feedback clearly and respectfully, then invite the recipient to share their perspective or ask questions. Phrases like "I'd love to hear your thoughts on this" or "Do you have any insights on how we can improve this approach?" can foster an open discussion. This not only makes the feedback process collaborative but also ensures that both parties understand each other, leading to better outcomes and continuous improvement.

Human connection can not be discounted even if we have moved to a world of AI. We are not working to chase and become robots, we are working to free time to become more human. We are all learning, and there are know true "experts" in AI, we are all learning and with the rapid transformation, we will all continue to be life-long learners. As we give feedback, we should alway try to give resources to support helping another gain knowledge. This helps the person get the resources they need and builds a better relationship between you.

Feedback often necessitates AI professionals to gain new skills or knowledge. Provide resources like tutorials, courses, or research papers to assist them in addressing feedback areas. This approach not only identifies areas for improvement but also provides them with the necessary tools to enact those enhancements. Such support is especially beneficial in the dynamic and complex realm of AI.

Providing AI professionals with resources like tutorials, courses, or papers not only addresses feedback but also equips them with the latest knowledge and skills, essential in a rapidly evolving field.

Sometimes, feedback requires the AI professional to acquire new skills or knowledge. Offer resources such as tutorials, courses, or papers that can help them address the feedback points. By doing so, you're not just identifying areas for improvement but also empowering them with the tools to make those improvements. This supportive approach can be especially beneficial in a field as complex and rapidly changing as AI.

Feedback should be supported by relevant resources like Responsible AI metrics dashboard, survey results, error logs, use cases and error documentation etc.

Regular follow-ups not only demonstrate commitment to the AI professional's growth but also provide critical checkpoints to adjust strategies and ensure alignment with evolving project goals and industry standards.

A few months ago, Emma S, an AI project manager, launched an ambitious new initiative. The initial excitement was palpable, but over time, enthusiasm waned, and progress slowed. Emma realized the importance of following up. She scheduled regular check-ins with her team, not just to monitor progress, but to offer support and guidance. During one follow-up meeting, a junior developer voiced concerns about a challenging algorithm. Emma connected him with a mentor, and within weeks, the problem was solved. By consistently following up, Emma kept the momentum alive, addressing issues promptly, celebrating small wins, and ensuring the team felt valued and heard. This commitment to follow-up turned a faltering project into a success story.

Here's my approach: ??Set clear goals. Outline specific, achievable targets during the feedback session. ??Regular updates. Schedule periodic check-ins to discuss progress and any obstacles. ??Support resources. Provide the necessary tools or training to help your team succeed. ??Celebrate wins. Acknowledge improvements and milestones to motivate continued growth. While good leaders might approach this slightly differently, I believe the principals for success are pretty consistent.

Check in periodically to track their progress and offer further guidance. This reinforces the feedback and demonstrates your commitment to their development.

Performance become better when people know they have a superior authority who keeps checking on them. Keep asking them for any problems or issues and address them immediately. Your team is your responsibility. You cannot leave them on their own. It's like providing them nourishment to grow.

In addition to the strategies mentioned, consider leveraging AI-driven tools for real-time feedback analysis. These tools can provide immediate, actionable insights, enhancing the feedback loop for AI professionals.

Effective comms between AI professionals and non-AI professionals is crucial. 1. Simplify Complex Concepts: avoid jargon. Explain complex concepts in simple terms. 2. Use Visual Aids: visuals such as charts, graphs, and infographs help convey complex data and results in an easily understandable manner. 3. Focus on Business Impact: Emphasize how AI projects and technologies can solve specific business problems or improve processes. 4. Create Collaborative Environments: Foster a culture of collaboration where AI and non-AI professionals work together closely 5. Provide Training and Education: training sessions and resources to non-AI professionals to help them understand basic AI concepts and the potential impact of AI on their work.

Adding to all above sections: Provide objectives and unbiased feedback to AI professionals involves several steps: 1. Use Metrics: Base feedback on clear, quantitative metrics like accuracy, precision, recall. 2. Benchmark: Compare work against industry standards. 3. Peer Reviews: Get input from multiple experts to minimize individual biases. 4. Clear Criteria: Define transparent criteria for assessment. 5. Anonymity: Anonymize work to focus on the quality rather than the person. 6. Constructive Feedback: Provide specific, actionable suggestions for improvement rather than vague critiques.

Might sound funny, but use AI to help provide feedback. This could be using OpenAI, Anthropic and more to help transcribing meetings and providing individualized unbiased feedback. In many cases team members will be more open to feedback based on AI being the thing that is providing the recommendations.

Generative AI can be a valuable asset in planning and implementing its own projects, provided that there is a strategic approach and a clear understanding of the goals and challenges involved. It can automate and optimize various aspects of project management, leading to more efficient and effective outcomes.