Enterprise AI Analysis
Evolving Generative AI: Entangling the Accountability Relationship
By Marc T. J Elliott, Deepak P, Muiris MacCarthaigh
Since ChatGPT's debut, generative AI (GenAI) has surged in popularity, offering cutting-edge language processing and human-like conversations. This paper argues that GenAI's adoption in critical public domain applications, such as healthcare triaging, fundamentally transforms the accountability relationship. Traditionally, accountability involved an 'actor' and a 'forum,' but GenAI introduces a 'dual-phase' model where the initial interaction shifts to the AI system. This creates potential challenges, including the risk of 'transferred judgment' from the GenAI to human actors when the AI fails to satisfy the forum. The authors recommend monitoring GenAI interactions, setting clear citizen expectations, and carefully selecting tasks for AI integration to mitigate these complexities and ensure robust accountability.
Executive Impact
Generative AI introduces transformative shifts in public administration, promising efficiency gains and improved citizen services, but also new complexities in accountability.
0%
Reduction in Public Service Wait Times
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Increase in Service Accessibility Potential
0%
Projected Increase in Accountability Complexity
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Boost in Citizen Engagement Potential
Deep Analysis & Enterprise Applications
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Foundational Concepts
The Dual-Phase Model
Key Recommendations
Traditional Accountability Process
Bovens' classical actor-forum accountability relationship where the actor explains/justifies, and the forum judges.
Enterprise Process Flow
ACTOR
→
Provides Information
→
FORUM
→
Asks Questions & Judges
→
ACTOR Faces Consequences
GenAI in Healthcare Triaging
The paper uses healthcare triaging as a case study to illustrate how GenAI introduces a new dynamic. Citizens interact directly with the GenAI for initial assessment, ESI classification, and recommendations, altering the traditional actor-clinician dynamic.
Generative AI in Patient Triage
Challenge: Traditional patient triaging is time-consuming and resource-intensive, leading to long wait times and potential delays in care.
Solution: Integrate GenAI systems to handle initial patient interactions, providing immediate assessment, Emergency Severity Index (ESI) classification, and recommended actions.
Impact: Reduces waiting times, offers instant personalized access, but complicates accountability as the GenAI becomes the first point of contact, potentially leading to 'transferred judgment' if the forum is dissatisfied.
Quote: "GenAI systems are well-equipped to manage long waiting times for initial responses."
Dual-Phase Accountability Cycle with GenAI
Illustrates the new dual-phase accountability relationship involving the actor, forum, and GenAI, where GenAI acts as the initial point of contact.
Enterprise Process Flow
Phase 1: GenAI-Forum Interaction
→
GenAI provides Information/Justification
→
FORUM judges GenAI response
→
Is Forum Satisfied? (No)
→
Phase 2: Questions Redirected to ACTOR
→
ACTOR Provides Explanation & Justification
→
Accountability Claim Resolved?
→
Yes: Update GenAI Model
Transferred Judgement Risk
A key challenge where forum dissatisfaction with GenAI's responses can transfer to the human actor, who then faces preconceived negative judgments, impeding the relationship's effectiveness.
High Risk
of Transferred Negative Judgement
GenAI Maintenance Imperative
Updating GenAI systems with new information from forum interactions is crucial for mitigating recurring issues, rectifying wrongdoings, and ensuring long-term meaningful citizen interactions.
CRITICAL
for System Quality & Citizen Trust
Strengthening GenAI Accountability
Strategies to mitigate accountability dissonance and maintain robust relationships.
| Challenge with GenAI | Recommended Action |
|---|---|
| Lack of direct accountability for AI outputs |
|
| Citizens' unrealistic expectations of GenAI |
|
| Potential for negative judgements to actors |
|
| Undefined roles in GenAI accountability chain |
|
Call to Action: Deliberate Before Deployment
Public servants, policymakers, and system designers are urged to deliberate on the potential accountability impact of generative systems prior to their deployment.
URGENT
Deliberation Needed
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Your AI Implementation Roadmap
A structured approach to integrating Generative AI, ensuring ethical deployment and robust accountability from concept to deployment.
Phase 1: Conceptualization & Impact Assessment
Begin with a thorough assessment of potential accountability impacts, ethical considerations, and power dynamics before deployment. Define clear objectives for GenAI integration.
Phase 2: System Design & Expectation Setting
Design GenAI systems with clear boundaries. Inform citizens transparently about the system's purpose, limitations, and how to escalate concerns, managing initial expectations effectively.
Phase 3: Iterative Deployment & Human Oversight
Deploy GenAI in an iterative manner, starting with well-defined tasks. Incorporate human-in-the-loop verification for critical decisions and ensure mechanisms for tracing and recording GenAI-forum interactions.
Phase 4: Continuous Monitoring & Skill Enhancement
Implement continuous monitoring and feedback loops to identify and rectify unsatisfactory GenAI responses. Invest in public servant training to interpret AI outputs and manage complex accountability demands.
Phase 5: Refinement & Accountability Enhancement
Utilize feedback from interactions to update and fine-tune GenAI models. Ensure the accountability chain remains clear, empowering human actors to provide comprehensive justifications when GenAI falls short.
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