AI Ethics Scoring for Automated Workload Placement Compliance
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Solution Overview
Problem
Automated orchestration in IT environments often leads to unethical system behavior due to the lack of ethical considerations in workload placement, resulting in ethical ambiguity or violations, which can expose companies to fines and disadvantage.
Innovation Solution
Implementing AI ethics scores and automation rules to determine ethically compliant asset placement, providing ethical audit trails, and enabling human intervention when necessary, using an AI ethics datastore architecture to manage workload orchestration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated orchestration algorithms are used for workload placement decisions, then business agility and decision speed are improved, but ethical compliance and risk control deteriorate
Solution Approach 1:
An ethics evaluation module is introduced as an intermediary between the automated orchestration algorithm and the workload placement decision. This module evaluates potential placement decisions against ethical guidelines and compliance requirements, allowing the system to maintain automated speed while ensuring ethical compliance through an intermediate review layer.
Solution Approach 2:
The system implements feedback mechanisms where ethical compliance outcomes from past workload placements are fed back into the orchestration algorithm. This allows the system to learn from previous decisions and adjust future automated placements to maintain both speed and ethical compliance, creating a closed-loop control system.
2Extent of automation
If automated orchestration algorithms make placement decisions without human involvement, then operational efficiency is improved, but ethical ambiguity and violation risk increase
Solution Approach 1:
The system performs preliminary ethical evaluation of workload placement decisions before final execution. The ethics evaluation module assesses potential placements against ethical guidelines in advance, preventing ethically problematic decisions from being implemented while maintaining automated operation for compliant cases.
Solution Approach 2:
The system proactively identifies and prevents potential ethical violations before they occur by evaluating placement decisions against ethical guidelines. This preliminary anti-action blocks ethically problematic decisions from being executed, countering the harmful effects of purely automated decision-making.
3Reliability
If ethical evaluation mechanisms are added to automated orchestration, then ethical compliance is improved, but system complexity increases
Solution Approach 1:
The ethical evaluation functionality is segmented into a separate, modular ethics evaluation module that interfaces with the existing orchestration algorithm. This segmentation allows the ethical compliance layer to be developed, maintained, and adjusted independently without redesigning the entire automated orchestration system, managing complexity through functional separation.
Solution Approach 2:
The ethics evaluation module is designed as a universal component that can assess multiple types of workload placements against various ethical guidelines using a common evaluation framework. This multi-functionality reduces overall system complexity by providing a single reusable evaluation mechanism rather than separate evaluation logic for each placement scenario.
Data Source
AI summary
One example method includes receiving an orchestration automation request for an asset, identifying an ethics rule that applies to the asset, comparing the ethics rule to asset values contained in an AI ethics datastore, based on the comparing, identifying a list of assets that conform to the ethics rule, and when the asset appears in the list of assets that conform to the ethics rule, automatically placing the asset at an entity of a computing infrastructure.