Organizations who want to reap the benefits of the artificial intelligence (AI) boom currently sit at a crossroads: how do they maximize the advantages delivered by AI while mitigating the inherent risks? Bias, privacy, security, regulatory, and ethical concerns, as well as global news headlines of bad AI causing brand, reputation, and monetary damages, make it difficult for organizations to realize the full value of AI.
Leading organizations know that a successful AI strategy starts with ensuring data is reliable, protected and used responsibly. And you also need to be able to prove it.
Powered by Collibra Data Intelligence Cloud, Deloitte developed Ethikit, based on their Trustworthy AI Framework. Built with Collibra’s flexible operating model and workflow capabilities, Ethikit’s assessment tool makes it easy to implement responsible AI practices that will become a competitive advantage — maximizing the ROI of your AI strategy while minimizing risks..
What actions do we prioritize?
Data lifecycle process: Assess principles/risks in the context of each stage of the data
lifecycle. Understand the breadth of data ethics considerations.
Who is accountable?
Accountability model: Identify appropriate teams from across the organization to be
responsible for assessing the relevant and appropriate actions to address data ethics.
How do we operationalize actions?
Measurement and monitoring: Document ethical considerations to understand adoption
issues and aggregate as possible/appropriate.
Processes and controls: Ensure an understanding of mechanisms (processes) that can be
used to embed the data ethics actions.
Tools and techniques: Identify and leverage tools and techniques to systematize a set of
relevant data ethics considerations.
How do we accelerate adoption?
Communications, training and change management: Ensure customers, employees,
shareholders and other stakeholders are kept informed of the organization’s perspectives and
actions as they relate to data ethics. Upskilling and training are key components to embed from
- Poses a risk-based set of questions to AI system developers and owners
- Provides real-time, action-oriented guidance to ensure development of responsible AI system
- Embeds a workflow engine to allow for real-time approvals and decisions
- Collects AI use cases for inventorying and aggregates enterprise-wide insights through dashboards
- Data ethics embedded across the enterprise
- Trust built across stakeholders promoting enterprise data ethics adoption and standardization
- Common language and reporting metrics to ensure alignment across the organization and stakeholders
- Configurable tool to accommodate rapid changes in risk assessments
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