Shape Principles of Collaborating with AI
without Compromising Quality and Integrity - and contribute to creating a Manifesto.
In today's rapidly evolving technological landscape, the collaboration between humans and Artificial Intelligence (AI) is becoming increasingly prevalent. The integration of AI in various domains presents both opportunities and challenges, especially when it comes to maintaining the highest standards of quality and integrity.
This initiative, that started with SeaStar Quality Conference, organized by Test Masters Academy, brings together professionals, experts, and enthusiasts to explore, define, and establish a set of guiding principles for effective collaboration with AI while upholding the core values of quality and integrity.
Our Objectives
- Define Core PrinciplesCollaboratively establish a framework of principles that should underpin any collaboration with AI, ensuring the preservation of quality and integrity in all endeavors.
- Case StudiesAnalyze case studies that highlight both successful and unsuccessful AI collaborations to draw lessons and insights. Several Experience Reports are included in the in the conference program, that started this initiative.
- Interactive DiscussionsEngage in open and insightful discussions with fellow participants, experts, and facilitators to gain diverse perspectives on the subject.
- Practical ApplicationsExplore practical applications of the defined principles in various industries and scenarios, considering the unique challenges each may present.
- Group ExercisesParticipate in group exercises and brainstorming sessions to develop practical strategies for applying the principles in real-world situations.
- Promote and EducateShare our findings through education initiatives and public platforms, guaranteeing widespread awareness, while also strategically targeting key decision-makers to maximize our impact.
Key Discussion Areas
- Transparency and AccountabilityExplore ways to ensure transparency and accountability in AI collaborations to maintain integrity.
- Bias and Results Invalidation MitigationStrategies to identify and mitigate biases and invalid outcomes in AI systems to ensure fair outcomes, and how to identify and validate the hallucinations in GenAI.
- Testing of AI and Testing with AIDiscuss principles and good practices of testing AI products and services and using AI as a tool to assist testing activities.
- Human-AI InteractionDefine guidelines for effective communication and interaction between humans and AI.
- Data EthicsDiscuss the ethical handling of data in AI collaborations and its impact on quality.
- Continuous EvaluationDiscuss the importance of ongoing evaluation and adaptation to maintain quality standards.
Outcome
Participants will collectively craft a set of principles that can serve as a foundation for ethical, high-quality collaborations with AI. This collaborative effort aims to provide valuable insights and guidelines for individuals and organizations looking to harness the power of AI while upholding essential values of quality and integrity in their work.
Based on the principles that we discover, we aim to create a Manifesto.
The set of principles will be transformed into the Manifesto through the community collaboration efforts.
Join us to be a part of the conversation shaping the future of AI collaboration.
Together, we can define a roadmap that ensures that quality and integrity remain at the forefront of AI-collaboration endeavors.
Join us to be a part of the conversation shaping the future of AI collaboration.
Together, we can define a roadmap that ensures that quality and integrity remain at the forefront of AI-collaboration endeavors.
What has been done
- Experience reports from the SeaStar Quality Conference (Oct 16th-18th)Listen to the program on October 18th, which focuses on AI collaboration experience reports
Get the recordings - Workshop to define the initial set of Principles (October 18th)The principles of collaboration with AI were discussed among conference speakers and attendees
See the summary
To contribute to the Manifesto on Collaboration with AI without Compromising Quality and Integrity, follow these steps
- 1Collaborative Drafting (November 2nd - November 25th)Join the group effort to draft the initial set of principles for collaboration with AI without compromising quality and integrity:
- Attend discussion meetings and contribute your insights during this period to help refine and finalize the manifesto.
- 2Manifesto Publication (November 30th)The Manifesto, which will incorporate the collective efforts and insights of contributors like yourself, is scheduled to be published on November 30th.
By actively participating in the process, you will be officially recognized as a contributor to the Manifesto.
Your involvement in these steps will not only help shape the Manifesto but also ensure that your valuable insights and perspectives are included in the community effort to define principles for AI collaboration that prioritize quality and integrity.
Join the discussion meetings
Visual communication takes place through pictures, graphs and charts, as well as through signs, signals, and symbols. It may be used either independently or as an adjunct to other methods.
Symbolization is an important definition for this perspective. Cultural perspective involves identity of symbols. The use of words that are related to the image, the use of characters, etc. is the symbolization of the image.
The view of images in the critical perspective is when the viewers criticize the images, and the critiques have been made in the interest of the society, although it’s an individual perspective nevertheless.
Symbolization is an important definition for this perspective. Cultural perspective involves identity of symbols. The use of words that are related to the image, the use of characters, etc. is the symbolization of the image.
The view of images in the critical perspective is when the viewers criticize the images, and the critiques have been made in the interest of the society, although it’s an individual perspective nevertheless.
Visual communication takes place through pictures, graphs and charts, as well as through signs, signals, and symbols. It may be used either independently or as an adjunct to other methods.
The view of images in the critical perspective is when the viewers criticize the images, and the critiques have been made in the interest of the society, although it’s an individual perspective nevertheless.
Visual communication takes place through pictures, graphs and charts, as well as through signs, signals, and symbols. It may be used either independently or as an adjunct to other methods.
Symbolization is an important definition for this perspective. Cultural perspective involves identity of symbols. The use of words that are related to the image, the use of characters, etc. is the symbolization of the image.
Partner with us
Our aim is to make it a global gathering where anyone interested can actively participate in the discussions. We extend an invitation to your community, group or organization to join us as partners in this meaningful endeavor. Several communities and organizations are partnering with us already, and I look forward to your community valuable inputs to this project.
Our partners and supporters organize the meetings and help this project to reach everyone who wants to contribute.
Our partners and supporters organize the meetings and help this project to reach everyone who wants to contribute.
Sponsorship
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$20
26 sessions from the industry experts on Advanced Modern Practices in Quality Engineering, Software Testing, DevOps, Test Automation, Business Analysis, Agile Process, CoP and Project management, and Experience Reports and Discussions on AI collaboration techniques for quality engineering, software development, software testing, test automation, and productivity.
$75
26 sessions from the industry experts on Advanced Modern Practices in Quality Engineering, Software Testing, DevOps, Test Automation, Business Analysis, Agile Process, CoP and Project management, and Experience Reports and Discussions on AI collaboration techniques for quality engineering, software development, software testing, test automation, and productivity.
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