The integration of AI into virtually every Australian industry today has naturally opened up plenty of new doorways and opportunities – not only for larger corporations, but for small and medium-sized enterprises as well. But with all these new capabilities, there are also serious risks of misuse.
For instance, irresponsible use of AI has contributed to the sharing of misleading or inaccurate information which can place businesses in breach of Australian Consumer Law. And whilst we all love to have a good laugh over some of the funnier mistakes made by AI tools, it definitely becomes less funny when a company’s reputation is on the line.
This is why so many industry bodies are advocating for regulations surrounding AI for commercial applications. Thankfully, frameworks for AI policymaking are finally starting to be developed, both independently and by governmental agencies and even non-governmental organisations like the International Organization of Standardization (most notably with their release of ISO/IEC 42001:2023 for AI Management Systems).
Using these frameworks, Aussie business owners can invest in AI governance for their enterprises, safeguarding their business ops from all the most prevalent risk factors of AI integration.
What is AI governance exactly and what does it entail? We’ll be answering that question below alongside sharing some of the key components that go into a good company AI policy.
What is AI Governance?
AI governance refers to the implementation of policies, practices, and procedures for facilitating the responsible and ethical utilisation of AI tools and the implementation of AI systems.
Much like ESG or CSR governance, AI governance considers how AI usage can align with a company’s social values whilst also abiding by legal and regulatory requirements (i.e. avoiding AI bias, data breaches, etc.).
8 Key Elements to Include in your Company AI Policy
1. Standards & Guidelines for Ethical AI Usage
The core of any corporate AI policy is to define the standards for ethical AI usage. This could be as simple as implementing guidelines for how and where staff can use AI, alongside specifying which AI tools are approved for company use.
For instance, your business may opt to only use ethical AI tools developed by entities that have invested in quality assurance and reducing risks of copyright infringement across the output of their AI offerings. Adobe Firefly’s generative AI is a great example here, as Adobe’s trained their gen AI tool on their own Stock photo and video content. This ensures that all generated photos and videos can avoid copyright infringement, which is also why Adobe refers to Firefly as a ‘commercial-safe’ or ‘brand-safe’ AI tool.
Business owners and leaders are encouraged to develop AI ethical standards and guidelines that also work towards keeping their company’s AI use as ‘brand-safe’ as possible.
2. AI Compliance Requirements
Are you looking to adhere to ISO 42001:2023 frameworks for managing AI systems? If yes, then you may already be thinking about your company’s AI compliance requirements. But even if you aren’t working with ISO frameworks, more industries are actually including AI compliance considerations into industry licensing and certification requirements.
If you don’t already have AI compliance guidelines to abide by, however, it’s well worth expecting to have them in the near-future. In other words, you can help save your staff from having to take a reactionary approach to AI compliance by actually using ISO 42001:2023 frameworks and other valuable guidelines for implementing your own AI compliance requirements in your company’s AI policies.
3. AI Risk Management Procedures
The great thing about the AI management systems frameworks outlined in ISO 42001:2023, is that it also provides great structural suggestions for integrating ethical AI operational processes and procedures. And one of the most important processes for supporting compliance is risk management procedures.
For AI, risk management procedures can help enterprises avoid potential litigation resulting from copyright infringements or even neglected duty of care (i.e. due to providing misinformation generated by AI hallucinations, for instance). These risk management procedures can be as simple as implementing cross-reference processes designed to fact check any AI-generated work. These double-check measures can also help businesses avoid releasing content that fails to read as human (i.e. fails AI detection testing). Editing AI content for humanisation is a cornerstone of avoiding the crackdown on AI content not only worldwide but across the web too.
4. AI Process Improvement Procedures
Process improvements are the positive counterpart to risk mitigation. Instead of focusing on danger areas for your procedures, process improvements focus on how your procedures can actually be updated and improved. Naturally, processes that utilise tech offerings like AI tools may be improved greatly in response to software updates. If your AI tools release new features, you can add these new capabilities into your business’ operating processes to boost your productivity and efficiency.
Process improvements can also be found through auditing existing systems in order to pinpoint any areas for development. When conducted routinely, these process improvement measures can naturally help keep your AI procedures fresh and evolutionary, making sure your enterprise stays on the cutting edge of AI integration and utilisation. This makes process improvement procedures a must-add to any company AI policy.
5. AI Data Governance
AI governance is incomplete without consideration to data governance as well. This involves the management of data recorded by using AI tools. Managing data can help minimise risks of misuse or even data breaches within AI models.
The main danger of data breaches affecting AI models is that companies often input sensitive client and business data into generative AI tools. When you’re using AI to write emails, for instance, your data records can include the names and email addresses of not only company staff, but also stakeholders and customers.
Managing this sensitive data is paramount to ensuring the continued ethical use of AI tools. Brand-safe AI tools like Adobe Firefly keep this in mind, ensuring that user data records are stored securely and are only accessible to authorised personnel.
6. Privacy & Security Protocols
Granted, all you need to be approved as an authorised person to access in-app data is the login for the tool. Thankfully, applications like Adobe Firefly use MFA (multi-factor authentication) to further reduce security risks. But without strong passwords for your accounts and other standardised security measures for your staff to follow, you may still be putting your enterprise at risk of privacy breaches.
This in a nutshell is why security considerations are part and parcel of any AI company policy. Working with cybersecurity specialists in your IT team (if applicable), you can develop and maintain methods for combating cyber threats to your AI tools, and preventing unauthorised access to accounts which may lead to data vulnerabilities.
7. Mitigating Bias & Discrimination
Mitigating bias and misinformation is foundational to ethical AI usage. As such, this may be one of the most important components for your company’s AI policy.
Thankfully, ensuring reduced data bias and discrimination of output can be ensured by using responsible AI prompting processes. For instance, if you’re writing content for a particular industry, prompting AI tools to include reference to relevant regulatory information can help make sure your content is responsibly written and won’t be contributing to misinformation.
AI prompt engineers can also develop prompt libraries that are specifically designed for your business and organisational workflows. Not only will this help boost the efficiency of your processes, but it will also help make sure your company’s AI usage stays consistently ethical.
8. Transparency & Accountability
Finally, any AI-generated assets are still technically authored by the user that prompted the tool to generate those assets. Monitoring who in your team created which outputs is important not only for giving credit where credit is due, but also for investigating any usage of AI that breaches your company’s AI policy.
Accountability can be monitored in a few different ways, including the use of specific logins for each team member, or even stipulations that team members should store any of their generated assets in their own dedicated computer folder. That way, you can both securely store assets in your company cloud or intranet whilst also easily keeping track of ownership.
And of course, if there are still any errors with authorship, you can look at the contents of the generated assets to determine which department they came from. Any AI-generated assets that speak to a creative output, for instance, most likely came from your creative team.
Invest in AI Governance to Futureproof your Business Ops
AI policies aren’t static, just as AI tools themselves aren’t static. They’re constantly updating and evolving, which means that you can also expect your AI protocols and best practices to be subject to change over time.
Even so, it’s better to have all your templates for procedures and protocols down as early as possible so your enterprise can waste no time in sustainably integrating AI tools into your operations and processes. Early adoption means you can better position your organisation to be at the forefront of AI innovation in your market or niche. And nobody should say no to the opportunity to grab a competitive edge.
image supplied by client
