Teaming Activities

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This is a working resource page for the Teaming activities of the Bridge2AI program.

The Bridge2AI program will use this wiki to bring together resources relevant to the Teaming Modules within the Data Generation Projects and the Teaming Core within the BRIDGE Center. The resources on this Teaming Activities page are designed to assist with the formation of diverse teams that will work together to create responsive applications


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June 9, 2021—2-3:30 pm ET


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June 14, 16, and 18, 2021—2-4 pm ET each day


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Teaming Resources

INSciTS and toolkits  Establish a collaborative agreement template to clarify each person’s responsibility towards the teaming approach.  Instill guidelines, such as the Eight Key Qualities for Effective Teams:

  1. Clear communication among all team members
  2. Regular brainstorming sessions with all team members participating
  3. Consensus among all team members
  4. Problem solving done by the group
  5. Commitment to the project and the other team members
  6. Regular team meetings are effective and inclusive  
  7. Timely hand off from team members to ensure the project is moving in the right direction  
  8. Positive, supportive working relationships among all team members

NCI Collaboration and Team Science: A Field Guide:…

The Native BioData Consortium

Genomic Data Science Community Network

Collaboration and Team Science: Mindset Matters

IMAG wiki Presentation/Recording: More Information

    Teaming – Interdisciplinary Biomedical Data Science for Intelligent Systems of AI

    Appropriate teaming requires a marriage between IT experts and domain experts. Diversity adds dimension to AI projects and provides new perspectives to finding innovative solutions. People with different disciplines, backgrounds and personalities tend to focus on different project details and constraints. This facilitates the likelihood that all important details will be addressed, and provides a holistic approach to identifying solutions, while combating against biases, such that the AI system will be more effective with the best efficiencies for all population groups, including the marginalized. This required concept of “teaming’ will lead to revolutions in the production, dissemination, acquisition, and impact of scientific knowledge in AI.  Two areas are important in “teaming.”  First, the team members from diverse disciplines, demographics, and perspectives, which includes biomedical, AI technical and non-technical experts. Second, mitigating and facilitating the team dynamics, patterns of interactions and relationships among team members, which is best understood through the principles of team science. Understanding these patterns is critical, as the resolution of complex issues requires deliberative within-group interaction processes in which alternative courses of action are surfaced, evaluated, and acted upon.

    1. Technical and Non-technical Roles to Facilitate an Intelligent System

    Although roles on an AI team are important to address all aspects of the project, it also is important to involve those who understands the scientific process, thinks in terms of complex interactive systems, has critical thinking skills, values a social consciousness over profit, and can work together collaboratively with others that are different from self in discipline, culture and perspectives.  The teaming core must ensure that members from diverse disciplines, racial/ethnic and other marginalized populations, and beliefs in transparency and collaboration constitute this initiative. Examples of needed roles:

      • Biomedical Domain Experts - subject matter experts include but not limited to:
        • Disease/Disorder/Conditions
        • Spectrum of Science Researchers– basic, translational, clinical, applied
        • Health care – pathology, imaging, diagnostic, treatment, surgeon, providers, etc
      • Data Scientist-different disciplines charged with finding and using patterns to forecast and advance knowledge-Examples include but not limited to:
        • Applied Machine Learning
        • Deep Learning
        • Complex Network Science / Analysis
        • Convex Optimization
        • Multi-agent Systems
        • Natural Language Processing
        • Network Mining
        • Graph Signal Processing
        • Transfer Learning
        • Data Mining
      • Data Engineers- programmers who apply the “how” by taking the ideas, models and algorithms from the data scientists and formalize into code and applying results
      • Product Designers – helps design what is most desired and useful
      • Statisticians, Mathematicians (Applied) and Economist – algorithm development and analytic approaches
      • AI ethicists, sociologist, psychologist-determine the impact on people, society, scientific disciplines.  Can also facilitate teaming.
      • Community Stakeholders – provides practical application perspectives to mitigate biases
      • Lawyers – oversight to current and potential laws that emerge as the AI field flourishes in utility
      • Executives and strategists – manage the team operations and business model by weighing new opportunities and risks, such as data privacy, HIPPA, cost, transparency etc
      • IT leaders – ensures privacy and security of data, storage, and retrieval of data, includes expertise in cloud-native technologies, like chat bots, containers, and orchestration
      • Marketing and sales leaders – with expertise in technologies like sales automation tools and robotic process automation in order to best socialize the use of the AI systems in the real world (comment to be removed - learn from the history of nanotechnologies who lingered because society wasn’t ready)
      • Operations pros – assesses new data collection processes to continually train and improve models, links to existing data, automate processes as much as feasible, auditors to mitigate biases in training and in applications
    1. Team Science

    Coordinated teams of diverse multidisciplinary experts with sundry skills and knowledge may be especially beneficial for this innovative B2AI project.  Teaming requires more than bringing together a group of diverse disciplines and individuals.  As each brings their own knowledge set, life experiences and personalities, there is an inherent need to coalesce these differences into a functional operation that values and utilizes the different perspectives rather than disregards differences.  Consequently, the teaming core must create an important platform for further promoting and positioning interdisciplinary cooperation.    

    Team science is the collaborative effort to address a scientific challenge that leverages the strengths and expertise of professionals trained in different field who come from different backgrounds and life experiences. This includes understanding how teams connect and collaborate to achieve scientific breakthroughs that would not be attainable by either individual or simply additive efforts. It is concerned with understanding and managing circumstances that facilitate or hinder the effectiveness of collaborative research. 

    It is important to build quantifiable informative models of teams as dynamical systems interacting over multiple networks; analyze dynamic team behavior by developing rigorous models that relate interaction patterns and network evolution to task performance; and break new ground in team design by scaling teams to solve complex tasks in the field of AI, as well as advancing social science theories of team performance.

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