Genio AI Framework

This framework is designed to align with the requirements of ISO 9001 and ISO 42001.

Genio believes in accountability and taking responsibility for the impact of our usage of AI. This framework applies to all employees, contractors and third-parties that develop products or services on behalf of Genio. We will always respond to feedback on our use of AI to ensure we understand its impact on individuals, communities and society as a whole.

Genio will always seek to avoid working with products or services that do not align with our values or would require us to work against guidance outlined in this framework. By adopting this framework, we uphold our commitment to the responsible and ethical deployment of AI in our work. Our technology should provide a positive contribution to our users and wider communities.

#	Principle	Objective	Guideline
1.1	Accountability	We will take accountability for the impact of our work that leverages AI	All teams working on AI projects should demonstrate leadership and commitment to upholding our values and policy
1.2	Inclusivity	We will recognize the importance of engagement, both within our teams and with users who may be affected by our AI work	Actively seek input, feedback, and collaboration from diverse stakeholders to ensure that AI benefits everyone and proactively address any potential concerns. Gain insights from experts and domain specialists to ensure a comprehensive understanding of any social, cultural, and economic implications.
1.3	Fairness	We will strive to avoid biases and discrimination in AI systems, paying particular attention to protected characteristics	Undertake an ethical review ahead of agreeing to going ahead with an AI project for any product or service. Implement quality assurance processes that uncover any that may exist.
1.4	Privacy	We care about every individual’s right to privacy	Do not leverage AI for any purposes we deem impact a person’s rights or privacy, including reproducing a person’s likeness (visual, audio or otherwise) unless a valid business case is signed off (through executive approval), and explicit, signed permission is obtained from the person in question. Developing systems that focus on secure processing of Institutional IP, Personally Identifiable Information (PII)* and minimizing PII collection. For any other situations that may have any potential direct or indirect impact on one or more individuals, seek executive approval and signed, written consent before taking action
1.5	Transparency	We commit to providing clear and accessible information about the use of AI in our products and services	Produce documentation that explains the purpose, values, data and governance of each AI feature/product/service/system.
1.6	Human-centricity	We believe in prioritizing the needs and preference of users, taking a human-in-the loop approach to designing products and systems	We will not design systems with fully automated AI-decision making, where that decision could negatively impact one or more individuals, unless sign-off is approved by the executive team. We will always incorporate human oversight in AI systems, supporting ethical decision-making and accountability. Take a user-centered approach to design, clearly indicating where AI has been used and how to interact with it. Establish mechanisms for providing feedback and use this feedback to improve our systems.
1.7	Education	For all of our principles, we will invest in continually upskilling our teams to improve and share our AI knowledge, understanding and best practice approaches, as well as hiring appropriate expertise where necessary	Evolve company and hiring policies and approaches along with new AI technologies. Include AI training as part of mandatory employee training. Provide opportunities for individuals to develop AI competencies as part of wider learning and development. Contribute to public awareness and understanding of AI in Education, its benefits, and potential risks, fostering a broader understanding of responsible AI.
1.8	Innovation	We strongly believe in fostering a culture of creativity and innovation and will experiment ethically with AI technologies and tools	Testing new AI tools or technologies is acceptable, as long as no user data is used and an initial risk assessment has been completed prior to use. Add dates to your calendar to cancel trials, or put together a business case to obtain a licence or purchase a product that leverages AI.

Principle

Objective

Guideline

2.1

Risk and Impact Assessed

We will conduct assessments of the social, economic, and environmental risks and impacts of AI in our products and services

Identify the risk level of any potential functionality, aligned with the capAI system.

Ensure AI-specific risk assessments are completed, considering key elements like privacy, safety etc.

Address potential negative impacts promptly.

2.2

Security

We commit to implementing best-in-class security measures to safeguard AI systems and the data they process

Leverage encryption and access controls wherever appropriate.

Conduct regular security audits and penetration tests.

2.3

Compliance & Governance

We will adhere to all relevant laws and regulations governing the use of AI, including proactively implementing guidance and processes for legislation under development

Adhere to all relevant laws and regulations governing the use of AI, including data protection, privacy, and anti-discrimination laws.

Enforce data governance practices that adhere to data protection regulation.

Keep abreast of any upcoming or evolving legislation.

Ensure any third-party tools or services that leverage AI have been risk assessed.

#	Principle	Objective	Guideline
3.1	Interpretability & Transparency	We’ll aim to ensure that stakeholders can interpret and explain the rationale behind the outputs of our AI	Produce thorough, user-friendly documentation for every project or process that leverages AI,
3.2	Lifecycle Quality Assurance	We will ensure quality at each of the key AI system lifecycle stages, from design through to development, evaluation, operation and if applicable, retirement	Implement knowledge transfer processes to ensure insights gained from ongoing quality assurance efforts are shared across Product & Technology teams.
3.3	Testing & Monitoring	We will take measures to enhance the reliability, accuracy, and performance of our AI systems and monitor against these our benchmarks	Test under a range of conditions, including diverse environments, input variations, and different user demographics. Ensure systems maintain reliability and performance across a spectrum of scenarios. Set up mechanisms for continuous monitoring of performance in real-world conditions. Implement alert systems to detect deviations from expected behavior.
3.4	Sustainability & Scalability	We will seek to develop AI products and systems that have longevity and can scale aligned to the goals of our users	Take an agile and progressive approach to development and deployment. Where possible build reusable capabilities rather than tying delivery tightly to a narrow use-case. Consider adopting third party tooling where it allows for, experimentation and changes to implementation, whilst adhering to our AI policy.
3.5	Automated Evaluation	Measure the accuracy and quality of AI systems	Use Evals to automatically test before every deployment
3.6	Maintainability	Maintain necessary expertise and resource within the business to support AI projects post-launch	Automatic tooling for security updates, which re-run tests.

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