What do you do if AI professionals need constructive and supportive feedback?

In my experience having a subject matter expert (SME) in the area of your AI project is a key for success. SMEs see things differently, embracing their input and looking at how they interpret the results of your experiments open new dimensions for successful implementation. Data is key in AI, but data is a reflection of human experiences, looking at the process of how the data was generated is sometimes overlooked, always try to relate the process to actual human experience. My colleagues have pointed out many key points about ethics and data privacy, take your time in considering how your work might be re-used in other domains, brainstorm with your team, each team member can bring new perspectives.

Before giving feedback to AI specialists, it is important to establish clear goals and expectations. This helps to link the professional's attention with the strategic goals of their projects or broader business goals. Clear goals give a framework for feedback, allowing AI professionals to better understand how their work fits into the bigger picture and what specific areas they need to work on for progress. By defining what success looks like, you make feedback more relevant and directly related to outcomes that can be measured. This method not only explains the direction and priorities, but it also helps in tracking progress over time, providing possibilities for detailed feedback that is related to particular targets.

Constructive and supportive feedback is crucial for the professional growth and development of AI professionals. It should strike a balance between objective criticism and empathetic encouragement, fostering a collaborative and psychologically safe environment for continuous learning and improvement. The feedback should be delivered in a way that promotes open communication, collaboration, and a growth mindset. It should encourage AI professionals to embrace challenges, learn from mistakes, and continuously strive for excellence in their field.

Some other key additions would definitely be: 1) Consider the environment, location and surroundings by which you give the feedback. Positive feedback is well suited to one-on-one or group settings, however, more specific feedback related to performance is always best conducted in private. 2) Documenting feedback that you have given and the individuals response along the way - is wise. Especially considering feedback conversations and next steps are a great reference point to help measure improvements and areas of needed change for year end reviews, ratings and bonuses. 3) When in doubt, ask the individual if they have a preference on how to receive feedback when you first start working with them.

You should listen to their ideas first. Then, ask then what they are solving and why? Act like a coach. Let then relook at the problem from outsider view. Focus on being helpful and encouraging.

Encouraging a two-way dialogue is crucial in fostering a collaborative environment for AI professionals. It shifts the focus from criticism to learning and improvement, inviting professionals to share their thoughts and reasoning. This exchange not only deepens understanding but also uncovers innovative solutions. Plus, it builds trust and ensures effective implementation of feedback. Well articulated!

Dialogue with Domain experts understand their intuitions, make AI based solutions explainable is a way to get practical AI solutions

Fostering a two-way dialogue is crucial not just for AI professionals but for any collaborative setting. When feedback is given and received openly, it creates an atmosphere of mutual respect and understanding. From the AI professional's perspective, being able to explain their reasoning can lead to valuable insights for both parties. It helps them refine their approach and consider different viewpoints they might not have previously thought of. This kind of open communication is also a great way to validate the hard work and effort put into developing AI systems. It's not just about pointing out what can be improved, but also recognizing what has been done well.

Creating an environment of open dialogue fosters collaboration and mutual understanding between AI professionals and those offering feedback. By encouraging two-way communication, we promote a culture where feedback is viewed as a tool for learning and improvement rather than criticism. This approach builds trust and ensures that feedback is well-received and effectively implemented, benefiting both parties.

Feedback should initiate a technical exchange, encouraging AI professionals to articulate their decision-making processes, from the choice of algorithms to data preprocessing steps. This conversation can unveil underlying assumptions, highlight alternative strategies, and foster a culture of critical thinking and open inquiry.

Through my engagements, linking to business outcomes is a key strategic focus. Try to link the solution to business outcomes. For instance, if AI is being implemented to work in a specific area and has led to cost savings or increased efficiency, highlight these achievements. Technical and operational leadership should work together to provide a clear path for solution development. Feedback should not only identify areas for improvement but also provide suggestions for professional development. This could include recommended training, resources, or mentorship opportunities.

When facing AI obstacles: 1. Define the issue clearly. 2. Analyze root causes thoroughly. 3. Research existing solutions. 4. Brainstorm potential fixes. 5. Evaluate feasibility and impact. 6. Prioritize solutions based on impact. 7. Develop detailed implementation plans. 8. Iterate and test solutions. 9. Document learnings and best practices. 10. Celebrate successes and learn from failures.

Emphasizing solutions over problems empowers AI professionals to overcome obstacles and drive innovation. By guiding them towards identifying and implementing solutions within their projects, we not only boost morale but also cultivate a culture of proactive problem-solving within the AI landscape. This positive approach encourages critical thinking and creativity, leading to tangible improvements in their work.

Define the issue with context, what happened, and where we are. Ensure they’re not alone in this, be supportive. Listen to why things went awry. Spend time getting clear on the expected experience with meticulous detail. Chart out potential roadblocks, pitfalls, mistakes. Set a plan to check in and you’re off to the races

Encountering challenges is an inevitable part of the development process. When they receive feedback that is solution-oriented, it not only helps them address the immediate issue but also broadens their understanding of potential improvements. By offering feasible suggestions tailored to their project's scope, you're providing them with actionable insights that they can implement. This kind of constructive guidance encourages them to think critically and creatively, ultimately enhancing their skills and expertise. Moreover, when they see that their efforts to overcome challenges are supported and valued, it boosts their morale and motivation to innovate further.

Providing resources alongside feedback is invaluable for AI professionals' learning and development. Recommending books, online courses, or communities shows commitment to their growth and equips them with tools to address feedback constructively. Whether it's technical documentation or discussion forums, offering resources enhances their ability to further their knowledge and skills in AI. It's about empowering them to continuously improve and excel in their field.

Alongside feedback, offering resources such as books, courses, and communities supports the continuous learning and development of AI professionals. Sharing relevant resources demonstrates a commitment to their growth and equips them with the tools needed to address feedback constructively. By facilitating access to knowledge and support networks, we empower AI professionals to further their skills and expertise in the field.

Recommend specific technical resources tailored to the feedback. This could include advanced machine learning courses, documentation on cutting-edge tools, or access to communities of practice. Highlighting resources that address the nuanced challenges faced in their projects demonstrates a commitment to their continuous learning and professional growth in the AI field.

In the dynamic field of AI, professionals often seek avenues beyond traditional learning resources. Closed AI communities and podcasts represent untapped wells of knowledge, often hidden from the mainstream yet brimming with insights from the vanguard of AI innovation. These communities, selective and often invitation-only, provide AI professionals a sanctuary to discuss, debate, and divulge the latest in AI advancements and challenges. Here, members are not just observers but contributors to the evolving narrative of AI. The Voice of AI's FinestPodcasts have become a crucible for thought leadership in AI. They offer a platform for voices ranging from established experts to emerging mavericks, sharing stories, trends, and predictions.

Accompanying my feedback with relevant resources is another key aspect I've found valuable. One time, when discussing potential areas for a machine learning engineer to upskill, I provided them with a list of online courses, technical documentations, and communities where they could further develop their knowledge and skills in the field of AI. Sharing these resources demonstrates my commitment to their professional growth and equips them with the tools to implement the feedback constructively.

AI is not the healthcare magic wand, nor is it a panacea. It’s important to recognize that the roadmap to AI differs from organization to organization and between departments within an organization reflective of the overall strategic goals vs initiatives in focus at each level. It’s crucial to start with What, Why, and How. What’s the problem that AI will solve? Why AI vs another technology? How will AI be used to achieve the desired outcomes? Equally important is strong leadership with deep understanding of the problem and the power of AI. Bringing clinical endusers, leaders, and subject matter experts with decision making authority to the table can help to drive adoption. Without adoption, none of the rest matters.

continuous support is crucial for the effective development of AI professionals. Feedback should be a recurring element of their growth journey, not a sporadic or isolated occurrence. Periodic check-ins where you offer guidance and encouragement not only keep professionals motivated but also emphasize the importance of their ongoing development. This consistent engagement allows you to celebrate their achievements and guide them through new challenges, reinforcing their progress and learning. Ultimately, such a supportive framework not only aids in personal career growth but also enhances the collective success and innovation of AI projects and initiatives.

Due to the rapidly-changing nature of the field, AI professionals should be encouraged to actively research and explore new solutions to existing problems. An implementation may be good enough for current purposes, but as we acquire more data of different types and learn how to distinguish useful data from the noise, AI professionals need to be aware of new tools and models that might provide even better results. Empowering AI teams to innovate in this regard not only drives them to seek excellence in their current tasks, it also helps to mitigate the risks involved in depending on one technology or vendor. Teams learn to think strategically, rather than focusing on particular tactical implementations and limited-use solutions.

Feedback should be part of an ongoing process of support and guidance for AI professionals. Regular check-ins allow us to monitor their progress, provide encouragement, and assist them in overcoming challenges. By offering continuous support, we demonstrate our investment in their development and contribute to their long-term success in the field of AI.

Offering continuous support is essential when providing feedback to AI professionals. Feedback should not be a one-time event, but rather an ongoing process where I periodically check in and offer guidance. This consistent support helps the AI team stay motivated and aware that their development is a priority. It also allows me to monitor their progress, celebrate their successes, and provide additional support as new challenges arise. Maintaining this continuous engagement is crucial for the long-term success of the AI initiatives.

It's important to create a space for sharing additional examples, stories, or insights that may not fit neatly into the previous sections. This could include highlighting the importance of mentorship, fostering a culture of diversity and inclusion within the AI community, or addressing ethical considerations in AI development. These broader considerations contribute to creating a supportive and inclusive environment for AI professionals to thrive.

One additional aspect I would like to highlight is the importance of fostering a culture of psychological safety within the AI team. By creating an environment where team members feel comfortable sharing their ideas, acknowledging mistakes, and asking questions, I've found that they are more receptive to feedback and more willing to implement the suggested improvements. Cultivating this kind of supportive and collaborative culture is essential for driving continuous growth and innovation in the AI field.

If your AI professionals require constructive and supportive feedback within the framework of tailored feedback, consider the specific roles and expertise involved. For instance, if your data scientists are grappling with data bias issues, provide insights on mitigating bias in the datasets they use. Similarly, offer guidance to machine learning engineers on optimizing algorithms for better performance. Tailoring feedback to individual responsibilities ensures it's both relevant and actionable, fostering professional growth within your team.

AI covers many techniques and technologies. For example, Machine Learning and Deep Learning are two different techniques that are applicable to different use cases, for example structured versus unstructured data. Not every use case requires compute intensive technology, sometimes a simple linear regression may be sufficient. Matching a use case with the appropriate technology requires skill and up front design. Blindly moving ahead with whatever tool is available is unlikely to lead to a successful outcome, especially where models are left to handle messy data - incurring technical debt that should be fixed in a data pipeline.

For me, the most important factor is the human behind the person: What are the current feelings? Is there maybe a personal problem or situation in the family you are not aware of? What is the burning desire of the person? This helps to better understand and feel your colleagues. Not to manipulate them, but to really understand and support them in their daily work.