Moral and executive skill set required to work with GenAI

When working with generative AI, the intertwining of ethics and responsibility becomes crucial. These technologies have the power to shape perceptions, influence decisions, and impact societies. Both moral (values-driven) and executive (action-driven) skills are required to navigate this landscape responsibly. Here's a breakdown of these skills:

Moral Skills:

  1. Empathy: Understanding the potential human impacts of generative AI outputs is crucial. This involves recognizing how AI might affect individuals emotionally, psychologically, or socially.
  2. Integrity: Standing firm in ethical principles even when it might be easier or more profitable to ignore them. This involves being honest about the capabilities and limitations of a generative AI model.
  3. Forethought: Anticipating the potential long-term consequences, both positive and negative, of implementing generative AI in various scenarios.
  4. Broad Perspective: Recognizing that technology impacts different communities in varied ways. This requires an understanding of cultural, societal, and individual differences.
  5. Open-mindedness: Being willing to listen to concerns and criticisms about generative AI implementations and to adjust approaches based on feedback.

Executive Skills:

  1. Transparency: Clearly documenting and communicating the workings, training data, and potential biases of a generative AI model. This might also involve using explainability tools to interpret AI decisions.
  2. Fairness Assessment: Regularly testing and refining generative AI models to ensure they do not perpetuate or amplify biases. This involves understanding and applying fairness metrics.
  3. Continuous Learning: AI and its ethical landscape are continuously evolving. Professionals should be committed to ongoing education about the latest techniques, concerns, and best practices in the field.
  4. Stakeholder Collaboration: Engaging with a diverse set of stakeholders, including ethicists, community representatives, and users, to gain a holistic understanding of the AI's impact.
  5. Risk Management: Developing strategies to identify, assess, and mitigate potential risks associated with generative AI. This includes understanding the legal and regulatory landscape.
  6. Accountability: Taking responsibility for the outputs of generative AI systems, including setting up mechanisms for redress when things go wrong.
  7. Robustness Testing: Ensuring that AI models are thoroughly tested for a variety of edge cases and unexpected inputs to minimize unintentional harmful outputs.

Incorporating these moral and executive skills into the development and deployment of generative AI models ensures a more holistic and responsible approach to technology. Given the significant influence of generative AI, it's essential that professionals not only have the technical expertise to develop these models but also the ethical grounding to guide their implementations responsibly. As the field progresses, these skills will likely become integral components of AI-related curricula and professional development programs.