Artificial Intelligence (AI) Policy Writing
Artificial Intelligence (AI) and Machine Learning (ML) are rapidly advancing technologies that are transforming how organisations operate and interact with their customers. As a policy writing company, we understand the importance of developing policies that govern the use of AI and ML in a way that is ethical, responsible, and aligned with the organisation’s values and goals. This arctile covers areas of AI to fold into policies, as well as delves into the ICO’s AI and data protection risk toolkit.
Examples of AI Bias
One key area that AI and ML policies address is bias and fairness. AI and ML systems can perpetuate and amplify existing data biases, leading to unfair or discriminatory outcomes. For example, a facial recognition system trained on a dataset predominantly composed of light-skinned individuals may have difficulty accurately identifying individuals with darker skin tones. Policies can help minimise bias in data, algorithms, and decision-making processes by requiring organisations to regularly audit and test their systems for bias and make adjustments as necessary. Tech and AI Consultancy companies such as EfficiencyAI are able to advise in these areas in more detail.
Artificial Intelligence Policies Transparency
Another important aspect of AI and ML policies is transparency and explainability. Policies can require that AI and ML systems are transparent and explainable, so stakeholders can understand how decisions are made and why. Transparency can help build trust with customers and employees and assist organisations in identifying and addressing any issues that may arise with their systems.
Data Privacy and AI Policies
Data privacy and security is also a critical concern regarding AI and ML. Policies can address how organisations handle and protect sensitive data used to train and operate AI and ML systems. This can include encrypting data, implementing access controls, and regularly auditing systems to ensure they are secure.
Governance and Oversight
Governance and oversight are also important considerations in AI and ML policies. Policies can establish a framework for the governance and oversight of AI and ML systems, including the roles and responsibilities of different stakeholders. This can include appointing a chief AI officer or an AI ethics board to oversee the organisation’s AI and ML systems and ensure that they align with its values and goals. In addition to governance and oversight, policies can also require human oversight for certain decisions made by AI or ML systems. This can ensure that any decisions that significantly impact customers or employees are reviewed and approved by human.
Auditing Automation and AI
Regular auditing of AI and ML systems is also important – as is documenting this in policies. Auditing can help organisations identify and address any issues that may arise with their systems, such as bias or security vulnerabilities. Well considered policies can ensure that AI and ML systems comply with relevant laws and regulations. AI (Artificial Intelligence) tools are very difficult to decouple into logical data protection risks, primarily because effectively, they can process and release any information that they have access to. For this reason, the ICO has produced an AI Data Protection Toolkit.
What is the AI Data Protection Assessment Toolkit?
The ICO has been working on the foundations of data protection guidance on AI systems by publishing its AI and Data Protection Toolkit. This toolkit breaks down AI Lifecycle stages into 5 stages grouped by a Risk Domain area – and then offers guidance through practical steps for each area for your assessment. The 5 stages of the AI Assessment Toolkit, along with example Risk statements, are below:
Business Requirements and Design
Risk Statement Examples: Failure to take a risk-based approach to data protection law when developing and deploying AI systems because of an immature understanding of fundamental rights, risks and how to balance these and other interests. This may result in a contravention of individual’s rights and freedoms, and the principle of accountability.
Data Acquisition and Preparation of AI Data Protection Toolkit
Risk Statement Examples: Choosing to rely upon the same lawful basis for both the AI development and deployment stages because of a failure to distinguish the different purposes in each stage may lead to unlawful processing and a contravention of the purpose limitation principle. Inaccurate outputs or decisions made by AI systems are caused by insufficiently diverse training data, training data that reflects past discrimination, design architecture choices or another reason. This leads to adverse impacts on individuals, such as discrimination, financial loss or other significant economic or social disadvantages. Inaccurate outputs or decisions made by AI systems are caused by insufficiently diverse training data, training data that reflects past discrimination, design architecture choices or another reason. This leads to adverse impacts on individuals such as discrimination, financial loss or other significant economic or social disadvantages. Inaccurate outputs or decisions made by AI systems caused by insufficiently diverse training data, training data that reflects past discrimination, design architecture choices or another reason. This leads to adverse impacts on individuals such as discrimination, financial loss or other significant economic or social disadvantages. Inaccurate outputs or decisions made by AI systems caused by insufficiently diverse training data, training data that reflects past discrimination, design architecture choices or another reason. This leads to adverse impacts on individuals such as discrimination, financial loss or other significant economic or social disadvantages. Inaccurate outputs or decisions made by AI systems caused by insufficiently diverse training data, training data that reflects past discrimination, design architecture choices or another reason. This leads to adverse impacts on individuals such as discrimination, financial loss or other significant economic or social disadvantages.
Training and Testing The AI System
Risk Statement Examples: Failure to take a risk-based approach to data protection law when developing and deploying AI systems because of an immature understanding of fundamental rights, risks and how to balance these and other interests. This may result in a contravention of individual’s rights and freedoms, and the principle of accountability. Choosing to rely upon the same lawful basis for both the AI development and deployment stages because of a failure to distinguish the different purposes in each stage, may lead to unlawful processing and a contravention of the purpose limitation principle.
Deploying and Monitoring the AI system
Risk Statement Examples: Inaccurate outputs or decisions made by AI systems are caused by insufficiently diverse training data, training data that reflects past discrimination, design architecture choices or another reason. This leads to adverse impacts on individuals, such as discrimination, financial loss or other significant economic or social disadvantages. Inaccurate outputs or decisions made by AI systems are caused by insufficiently diverse training data, training data that reflects past discrimination, design architecture choices or another reason. This leads to adverse impacts on individuals, such as discrimination, financial loss or other significant economic or social disadvantages. Failure to explain the processes, services and decisions delivered or assisted by AI to the individuals affected by them where AI systems are difficult to interpret. This can lead to regulatory action, reputational damage and disengaged public.
Procurement
Risk Statement Examples: Inaccurate outputs or decisions made by AI systems are caused by insufficiently diverse training data, training data that reflects past discrimination, design architecture choices or another reason. This leads to adverse impacts on individuals, such as discrimination, financial loss or other significant economic or social disadvantages. Inaccurate outputs or decisions made by AI systems are caused by insufficiently diverse training data, training data that reflects past discrimination, design architecture choices or another reason. This leads to adverse impacts on individuals, such as discrimination, financial loss or other significant economic or social disadvantages. Failure to explain the processes, services and decisions delivered or assisted by AI to the individuals affected by them where AI systems are difficult to interpret. This can lead to regulatory action, reputational damage and disengaged public.
The Complexity of AI Policies
It’s important to note that AI and ML policies can be complex and require significant technical expertise to develop and implement effectively. Additionally, it’s important to involve different stakeholders in developing AI and ML policies, such as employees, customers, and experts in the field of AI ethics. By involving these stakeholders and taking a holistic approach to policy development, organisations can ensure that they are using AI and ML in a way that is ethical, responsible, and aligned with their values and goals. At Policy Pros, we recognise the importance of developing policies that govern the use of AI and ML in a way that is ethical, responsible, and aligned with the organisation’s values and goals. By addressing areas such as bias and fairness, transparency and explainability, data privacy and security, governance and oversight, human oversight, auditing and compliance, organisations can ensure that they are using AI and ML in a way that benefits both the organisation and its stakeholders.
How We Can Help
Our company offers a variety of standard, custom, and fully bespoke policies. Please contact us using the form provided below for more information.
