Shaping the Future of Work: Responsible Design and Public Policy for Generative AI
Document Type
Conference Proceeding
Publication Date
1-1-2023
Abstract
As Microsoft 365 Copilot and other generative AI technologies reconfigure our daily work, the potency of large language models (LLMs) in synergy with diverse user data sources has sparked debate on the transformative nature of generative Artificial intelligence (AI) in the workplace. The potential for AI to bolster communication, collaboration, ideation, and automation is merely an overture to the anticipated singularity of technological systems, with generative AI attaining unprecedented intelligence levels that transcend human capabilities, honing its knowledge processing, and increasingly making autonomous decisions. To cultivate a harmonious co-evolution of humans and machines, an innovative approach to generative AI design governance and associated public policy, including responsible AI design, is crucial. Such a strategy empowers policymakers to forge a transparent, equitable generative AI ecosystem that spurs innovation while maintaining digital transformation and preserving stakeholder well-being. Potential public policies for responsible AI design should address these societal concerns: (1) Design Standards: Create responsible AI design criteria, linking compliance to legal accountability for AI-caused damages. Possible research question (RQ): How can legal frameworks incorporate emerging ethical considerations and best practices in responsible AI design standards? (2) Limited Tort Liability: Offer limited tort liability to compliant companies, balancing innovation and legal accountability. Possible RQ: What criteria or metrics should determine limited tort liability for companies adhering to responsible AI design? (3) Responsible Digital Innovation: Promote equitable access, end-user control, and algorithmic transparency with technology policy advancements. Possible RQ: What policies and measures can encourage equitable access, control, and transparency in generative AI systems? (4) Continuous Evaluation: Regularly review and update standards and guidelines, considering AI evolution, ethical concerns, and legal landscape changes. Possible RQ: How can evaluation methodologies effectively adapt to assess and update AI standards in response to technological and ethical developments? (5) Stakeholder Engagement: Collaborate with diverse stakeholders for a balanced regulatory environment. Possible RQ: How can stakeholder groups best collaborate in generative AI policy, including multiple perspectives and addressing conflicts of interest? (6) Public Awareness and Education: Raise responsible AI design awareness via educational resources and public dialogue. Possible RQ: What strategies can effectively increase public awareness and understanding of responsible AI design and its societal implications? (7) International Cooperation: Foster global cooperation for consistent legal frameworks and responsible AI design practices. Possible RQ: What mechanisms can facilitate harmonizing generative AI legal frameworks and sharing best practices internationally? (8) Human Rights and Dignity: Safeguard human rights and dignity in AI design and implementation. Possible RQ: How can AI design principles prioritize human rights and dignity, aligning AI systems with societal values and ethical standards? A holistic approach, responsive to the burgeoning landscape of generative AI technologies, melds legal, ethical, and engineering insights to ensure AI systems enrich life quality while conforming to societal values and ethical tenets.
Identifier
85192878759 (Scopus)
ISBN
[9781713893592]
Publication Title
29th Annual Americas Conference on Information Systems Amcis 2023
Recommended Citation
Abhari, Kaveh and Eisenberg, David, "Shaping the Future of Work: Responsible Design and Public Policy for Generative AI" (2023). Faculty Publications. 2104.
https://digitalcommons.njit.edu/fac_pubs/2104
