Creating an AI Ethics panel

A data ethics council helps maintain an organization’s values-based intentions, and increases transparency into how we use Data & AI. This enables a focus on three areas for growth:

• Establish governance for data ethics & AI and consider the importance for data collection and sharing.
• Describe how/when fairness happens and how/what biases have been accounted for
• Provide mechanisms for recourse.

Harvard business review has an article Create an Ethics Committee to Keep Your AI Initiative in Check

Accenture have a summary page Building data and AI ethics Committee in your business

Accenture have a full report on building a data ethics committee. 

Ethics Frameworks to help implementation

Four frameworks are covered below, the UK government data ethics framework, Data Ethics Decision Aid (DEDA), the UK statistics authority ethics self-assessment tool and Microsoft Responsible AI Framework. 

The government data ethics framework  has a self-assessment for the 3 overarching principles.

The self-assessment has 5 specific actions. A score lower than 3 requires review.

The areas covered in each area:

1 define public benefit and user need

• understand unintended and or negative consequences 
• human rights considerations
• justify the benefit
• make user needs and public benefit transparent
• check everyone understand user need and how to use the data

2 involve diverse expertise

• ensure diversity within your team
• involve external stakeholders
• effective governance structures with experts
• transparency

3 comply with the law

• compliance with GDPR and DPA 2018
• data protection by design
• accountability
• transparency
• project complainant with the equality act 2010
• ensure effective governance of your data

4 review the quality and limitations of the data

• data source being used
• meta data understood
• processes to maintain integrity
• is synthetic data appropriate for the project evidence based caveats
• bias in data to train the model
• determine proportionality
• data anonymisation
• robust practices - demonstrated reproducibility, quality of the model
• make data open and shareable whenever possible
• think about transparency of sensitive models
• explainability

5 Evaluated and consider wider policy implications

• repeatability
• project influences
• accountability structures
• skills, training and maintenance for longevity of the project
• share learnings

The 3 areas summary

Another model is the Data Ethics Decision Aid. DEDA is a tool-kit facilitating initial brainstorming sessions to map ethical issues in data projects, documenting the deliberation process and furthering accountability towards the various stakeholders and the public.

Ethics Self-Assessment Tool from the UK statistics authority is another framework .The self-assessment tool provides a timely means to identify ethical issues, shape future discussions and support an accurate and consistent estimation of the “ethical risks” of research proposals.

The Microsoft Responsible AI Framework looks at 6 principles

 

• Fairness - should treat all people fairly

• Reliability & Safety -  should perform reliably and safely

• Privacy & Security - should be secure and respect privacy

• Inclusiveness - should empower everyone and engage people

• Transparency - should be understandable

• Accountability - People should be accountable for AI systems