Was tun Sie, wenn Ihre KI-Arbeit kritisches Feedback erhält?

Take these 5 tips into account -Keep an Open Mind -Don’t Take It Personally -Analyze and Reflect -Appreciate the Feedback -Develop an Action Plan

As a person that develops products, in my experience the only thing worse than no feedback is something we call “mom” feedback. This is feedback you get from people that are just being polite. Feedback your mom would provide. Mom feedback is common from friends and family. It is also common for entrepreneur to receive. Most people don’t want to hurt your feelings - especially for entrepreneurs and developers that are excited about their new product or feature. You should thank people that provide harsh feedback. This is how you learn and make things better. They may not be right but their critical feedback should be appreciated.

Criticism creates an opportunity - to learn, improve, and build better AI. Embrace the feedback constructively: - Detach emotion and hear the substantive points objectively - Investigate which aspects of the work sparked valid concerns - Pinpoint potential blind spots, flawed assumptions or oversight areas - Consider diverse perspectives you may have missed initially - Devise strategies to course-correct and rebuild trust - Use it as motivation to raise the bar for your next iteration By accepting critique as a catalyst for growth, you'll iterate toward more robust, ethical and impactful AI systems. Critical feedback, when handled maturely, is a gift.

Ao lidar com críticas em seu projeto de IA, é essencial abraçá-las. Compreenda que a crítica não é um ataque pessoal, mas sim uma oportunidade de crescimento e aprimoramento. Analise o feedback de forma objetiva e reconheça que a IA é um campo complexo, em constante evolução e que muitas vezes falha, no qual até os profissionais mais experientes podem aprender com críticas construtivas. Adote uma mentalidade positiva, você pode transformar o que pode parecer um contratempo em uma ferramenta poderosa para melhorar seu trabalho.

Constructive criticism is a gift as it offers someone else's perspective, revealing areas for improvement we might not see ourselves. Catalysing both personal and professional growth encourages us to challenge our limits and refine our skills. Embracing criticism isn't easy, but putting the ego aside and listening and understanding the criticism fosters a culture of openness and continuous learning, which is crucial in fast-evolving fields like AI. When delivered respectfully, constructive criticism strengthens relationships, building trust and collaboration. Ultimately paving the way for excellence, driving us to surpass our current capabilities.

Feedback plays a pivotal role in any project, especially in AI development. It's essential to gather insights on how end-users interact with AI solutions, focusing on the system's learning process from user transactions and its subsequent application. My experience leading AI projects at Aster hospitals in the UAE taught me the importance of observing user interactions firsthand. Continuously refining the AI based on user and manager feedback ensures the solution becomes increasingly efficient and autonomous.

Após acolher as críticas construtivas, é fundamental analisá-las minuciosamente. Desmembre os comentários para entender áreas específicas que precisam de melhorias. A crítica está relacionada ao desempenho da IA, considerações éticas, interface do usuário ou talvez aos dados subjacentes, overfitting? Ao identificar os problemas exatos, você pode criar um plano de ação focado para resolvê-los. Lembre-se de que o feedback frequentemente envolve uma mistura de opiniões subjetivas e observações objetivas; você deve filtrar e priorizar os pontos mais relevantes.

Clarify the Issues: Ask clarifying questions to fully understand the specific points of criticism. Ensure you grasp the core concerns raised in the feedback. Analyze the Feedback: Reflect on the feedback objectively. Is the criticism valid? Are there areas where your approach could be improved? Respond Constructively:

Upon receiving critical feedback on any work, actively engage with the feedback provider to clarify their concerns and perspectives. This approach fosters open communication and demonstrates your commitment to improvement. Use this interaction to understand specific issues, whether they relate to the AI's performance, ethical considerations, or user experience. Clarifying feedback allows you to pinpoint areas for refinement or adjustment. This process not only improves your AI solution but also builds trust and credibility with your audience, showing that you value input and are dedicated to delivering the best possible outcome.

With insights gained from feedback, the planning phase focuses on outlining actionable steps for enhancement. Prioritization is key here, ensuring that efforts are directed towards the most impactful changes, balancing potential benefits against the feasibility of implementation.

I am sharing what I will do in this case. If I get some tough feedback on my AI work, here's my plan: First off, I won't freak out. It's all part of the game. I'll take a deep breath and really listen to what's being said. Then, I'll break down the feedback into manageable chunks and figure out my next steps. What can I actually do to improve? I'll make a game plan. Set some goals and deadlines for myself so I know what I'm aiming for. And I'll prioritize, focusing on the changes that'll make the biggest impact first. As I start making improvements, I'll keep checking in with my progress. How am I stacking up against the feedback? After all, every bit of feedback is a chance to make my AI work even better.

Be Open to Change: If necessary, be open to modifying your approach based on the feedback. Learn from Mistakes: Analyze the feedback to identify areas where you can improve your future AI projects. Seek Additional Support: If needed, seek guidance from senior colleagues or mentors to address specific technical challenges raised in the feedback.

The execution of improvements is where concepts are tested in the real world. This phase involves the practical application of adjustments to algorithms, data, or interfaces, underscored by the importance of documenting these changes for subsequent review and analysis.

The next step is to implement changes based on that feedback. Prioritize the issues identified, focusing first on those with the most significant impact on performance, user experience, or ethical considerations. Update your AI models, algorithms, or user interfaces as needed, ensuring your changes address the feedback effectively. It's important to document these modifications and the reasoning behind them, not only for transparency but also for future reference. After implementing changes, test the updated AI system thoroughly to ensure that the modifications have positively addressed the concerns raised.

Embrace feedback as a stepping stone for growth and innovation in AI 🤔 Introspection Is Key: View feedback as an opportunity for self-reflection and learning. This mindset applies broadly, encouraging a deeper understanding of both successes and areas for improvement. 🔄 Openness to Change: Be willing to adapt not just your projects but also your approach and perspective. This openness is essential for personal and professional development in the fast-evolving AI landscape. 🌱 Foster a Culture of Growth: Encourage an environment where change is welcomed, honesty is valued, and humility is a strength. This attitude not only enhances your work but also inspires your team and peers.

Reflection upon the entire process and the changes implemented is crucial for professional growth. This phase is an opportunity to evaluate the effectiveness of adjustments, learn from the process, and understand how feedback has led to a more refined and user-centric AI solution.

- Be a good listener: Listen to the feedback without getting defensive, make notes - Understand the specific points raised, and see how you can deal with it - Reflect on how it can be improved, share the revised work with them - Learn from it

Beyond addressing specific criticisms, consider the broader implications of feedback on your work. For instance, if the feedback highlights a lack of inclusivity or fairness, it may signal the need for a more diverse dataset or different perspectives in your development team. Recently a team developing an AI for job matching realized their algorithm inadvertently favored candidates from a specific demographic. This realization prompted them not only to revise their algorithm but also to overhaul their dataset collection process, engaging with a wider range of sources for more balanced input. Feedback isn't just about fixing a problem; it's an opportunity to innovate and make your AI systems more robust, ethical, and effective.

Don't be Defensive: Avoid getting defensive or argumentative when responding to criticism. Focus on having a constructive dialogue. Use the Feedback to Improve: Utilize the feedback to enhance your future work. Seek Out Feedback Regularly: Don't wait for formal reviews. Proactively seek feedback from colleagues or mentors throughout the project lifecycle. This allows for early course correction and avoids major issues later in the development process. Remember, critical feedback is a valuable tool for growth in any field, including AI. By maintaining a positive attitude, understanding the criticism, and using it to improve your work, you can turn critical feedback into a learning opportunity and advance your skills as an AI professional.

Beyond the structured approach to feedback, this open space invites the sharing of broader insights, personal experiences, or unscientific evidence that may not align directly with the previous steps but are equally valuable for holistic development.

1. Critically analyse the feedback for any insight. 2. Create a plan to action on insights worth acting on. 3. Disregard what's not. 4. Communicate back to provider 5. Keep track of results of action for learning and continuous improvement