Generative AI-powered data projects, while holding immense potential, also carry significant responsibility. There is a growing recognition of the crucial role that ethics and governance play in this field. As organisations leverage the capabilities of Generative AI to drive decision-making, the imperative for ethics and governance becomes critical to building trust and unlocking the full value of this rapidly developing technology.
Ethics refers to the principles and guidelines that govern every stage of development and usage, ensuring its implementation in a responsible and morally acceptable manner. These ethical principles not only reduce the risks associated with Generative AI but also enhance its societal acceptance, effectiveness, and long-term viability. Governance, in turn, provides the infrastructure and oversight required to enforce ethical guidelines and respective accountability, through a set of rules, policies, and frameworks.
According to the EU’s Ethics Guidelines for Trustworthy AI released in April 2019, an AI system is deemed trustworthy if it meets the following seven key requirements:
- Human agency and oversight
- Technical robustness and safety
- Privacy and data governance
- Transparency
- Diversity, non-discrimination, and fairness
- Societal and environmental well-being
- Accountability
When Generative AI solutions are designed, developed, and used with the seven ethical requirements in mind, stakeholders are more likely to trust these systems in various applications.
Ultimately, ethical behaviour in Generative AI may attract more customers and partners who value responsible data usage, leading to a strong competitive advantage.
The benefits of Ethics and Governance in Generative AI
Fairness and Non-Discrimination: Ethics and governance in generative AI promote fairness, non-discrimination, and equity, through a set of bias mitigation techniques and training and awareness practices.
Transparency and Explainability: Ethics and governance can mandate transparency and explainability requirements for Generative AI solutions, ensuring that the decision-making process is understandable to stakeholders and users.
Data Integrity and Compliance: Ethics and governance practices include procedures to ensure data and algorithm integrity - including accuracy, completeness, and consistency, as well as adherence to security policies and data privacy regulations.
Robustness and Safety: Ethics and governance help enforce mechanisms to mitigate and prevent harm, ensuring the reliability, resilience, and safety of generative AI systems, which must be resistant to adversarial and malicious perturbations.
Accountability: Ethical frameworks and governance establish clear lines of accountability and escalation routes for Generative AI-powered solutions, specifically when issues arise.
Why is It important?
Failing to employ ethics and governance in Generative AI-powered projects can lead to a range of risks and negative consequences, including:
Bias and Discrimination: Generative AI can perpetuate and amplify biases present in the data they are trained on, leading to skewed insights, biased recommendations, and discriminatory outcomes.
Privacy Violations and Litigation: The use of Generative AI can increase the risk of data breaches and privacy violations if adequate ethical considerations and measures are not in place. Non-compliance with data protection, privacy, and ethical regulations can result in legal actions, fines, and penalties.
Dependency on Third-Party AI Providers: Using generative AI-enabled solutions creates risks related to vendor lock-in, data ownership and service disruptions, if adequate governance mechanisms are not in place to assess and monitor the practices of external partners.
Inaccurate Insights: Lack of ethics and governance in Generative AI can result in data quality issues and inaccurate or unreliable insights, leading to poor decision-making and potentially significant financial and operational consequences.
Customer Churn: Erosion of trust due to unethical data practices can result in customer churn and a decline in revenue – users and stakeholders are less likely to trust and accept Generative AI applications, hindering their wider adoption.
Loss of Competitive Advantage: Organisations that do not prioritise ethics and governance in generative AI-fuelled data projects may lose their competitive advantage and miss market opportunities, as customers and partners increasingly seek ethical and responsible practices.
Ethics and Governance Best Practices
Organisations must develop, communicate, and adhere to clear ethical guidelines and governance principles specific to their Generative AI projects. These guidelines should align with broader organisational ethics and industry standards. Moreover, it is crucial to engage with relevant stakeholders throughout the entire project life cycle to gather input on ethical considerations and identify and address challenges more effectively.
This section highlights key best practices to ensure the trustworthiness – and ultimately, success - of Generative AI solutions. Given that ethical and governance considerations can be highly context-specific, it's crucial to adapt these practices to the specific Generative AI project.
Employ human oversight mechanisms, such as human-in-the-loop and human-in-command to ensure humans can intervene and override Generative AI outputs in critical decision-making processes. This process should be carried out by a diverse range of people to aid the likelihood to detect and eradicate any biases which may occur.
Establish mechanisms for accountability for Generative AI decisions, actions, and outcomes, including:
- Designate an AI ethics officer and/or a governance committee to oversee Generative AI projects and their unique risks and legal complexities.
- Develop protocols for handling ethical breaches or failures, clearly defining roles and responsibilities for managing the underlying generative AI systems.
Employ traceability mechanisms and explainability practices to help ensure the data and Generative AI systems are transparent to technical teams and business stakeholders:
- Plan release communications to users on when and how Generative AI is being used, including its capabilities and limitations.
- Create and provide technical and non-technical documentation on how the Generative AI solution works, including a privacy notice to help users understand how the organisation handles their data.
Implement robust data quality management practices to maintain the accuracy, consistency, and completeness of data used in Generative AI projects, including:
- Employ mitigation practices by including ethical considerations – such as gender bias and discrimination – in evaluation criteria and data validation checks.
- Establish feedback mechanisms for reporting quality issues or suggestions for improvements.
Establish robust data security rules and implement procedures and controls to enforce the adherence to data privacy regulations - such as GDPR or HIPAA - and data retention and deletion policies, ensuring that personal and sensitive data are handled responsibly.
Establish procedures for continuous monitoring and auditing of Generative AI data projects, including:
- Perform routine audits and assessments of Generative AI solutions to evaluate their adherence to ethical guidelines and compliance with relevant regulations and policies.
- Establish feedback mechanisms for users to report data ethics concerns or violations confidentially.
Provide training and awareness programs for employees and stakeholders on data ethics, privacy, and security in Generative AI, emphasizing ethical principles and responsible practices; educate users and stakeholders about how the Generative AI solution works, its limitations, and potential risks.
In the rapidly evolving landscape of Generative AI, ethics and governance serve as the compass guiding responsible innovation.
As technology continues to reshape our world, the adherence to ethical principles and governance best practices becomes not only a competitive advantage but a moral imperative.
By implementing these principles, organisations can foster trust, ensure fairness, and navigate the complex challenges of Generative AI with confidence. In doing so, they contribute to a future where technology enhances our lives while upholding the values and principles we hold dear.
Contributors
Maria Oliveira
Senior Data Consultant | Redkite
Maria is a senior business intelligence analyst with over 5 years’ experience working with clients in CPG and Retail, including Burberry and Imperial Brands.
She has worked across business intelligence and business analysis, playing a leading role in assessing client visualisation requirements, defining operating models and using tools such as Power BI to realise client analytics needs.
Prior to Redkite, Maria was an Insights and Analytics Consultant at Hitachi Vantara, and she holds a Masters in Engineering and Industrial Management from Instituto Superior Tecnico.
Simon D’Morias
Director of data platforms | Redkite
Simon is a Data and Technology architect, with 20 years of experience. His focus areas include data architecture, data technology platform architecture, and DevOps process design.
As a data architect, Simon is familiar with both modern data lake techniques as well as more traditional data warehousing approaches.
Prior to Redkite, Simon worked in gaming with Betway and Rank.
Simon is a certified scrum master by the Scrum Alliance.
Phil Jones
Associate Director | Redkite
Phil has over 30 years of experience, including Data Governance Frameworks and Implementation, Data Cataloguing, Data Quality, Data Ethics and Change Management.
Prior to Redkite, Phil worked for nearly 25 years in Marks & Spencer where he held a diverse range of roles including setting up and operationalising the Data Governance function and implementing a range of technology enablers.