Artificial Intelligence (AI) has long been a part of the business landscape, serving as a catalyst for growth and innovation. From machine learning to natural language processing, AI has been helping business owners automate repetitive tasks, analyse data more effectively, and gain valuable, actionable insights.
In 2022, the arrival of Large Language Models (LLMs) like ChatGPT sent many people into a minor panic. For others, it was simply another AI tool they could use to improve performance and productivity. Regardless of which camp you fell into, the hype around AI has been manic, to say the least.
Will AI be as “revolutionary” as the hype merchants insist? That’s still up for debate. What we do know is that in a landscape increasingly shaped by AI, it’s crucial for business owners to formalise their approach to its development and deployment. And any formalised approach worth its weight in kilobytes must include a consideration of ethics.
Ethics in AI: Why Is It Important?
Ethics plays a pivotal role in AI, safeguarding your business against potential risks and biases while ensuring you uphold human rights, privacy, and values. As AI’s influence spreads through sectors such as finance, healthcare, and customer service, the need for ethical guidelines grows evermore acute.
Real-world examples of ethical issues in AI include biased algorithms, privacy violations, and discriminatory outcomes. And the solutions to these issues aren’t simple. For example, search engine technology is not neutral. Indeed, it cannot be neutral as, by design, it has to prioritise some results over others. If problematic biases and other ethical issues impact the search results, this can create unintentional echo chambers that uphold faulty beliefs and further entrench prejudices and stereotypes online.
Regardless of the size of your business, similar ethical issues may present themselves as you experiment with and incorporate AI. For this reason, it’s vital to work from a set of ethical guidelines that can help you avoid moral quandaries and AI dramas, both of which could potentially damage your company’s reputation.
Since we specialise in enterprise-level business IT support, the Invotec team has extensive experience with AI and automation. With more than 20 years in the industry, we know what it takes to develop a set of ethical guidelines that are clear, practical, and actionable.
The seven golden rules below will help you prioritise transparency, accountability, fairness, and human oversight in your use of AI. The aim is to ensure you gain all the benefits from these technologies while upholding ethical standards. In doing so, you’ll build trust with your employees, customers, stakeholders, and the wider community.
The 7 Golden Rules for Responsible AI Use
Transparency
Transparency refers to the openness and clarity of your AI systems. The idea is to ensure that your operations and decision-making processes are understandable and easily explainable. You need to be ready and able to offer insights into how your AI systems work, including your processes for detecting biases and identifying potential risks or errors. This level of transparency will help you maintain trust with customers, employees, and regulatory bodies.
To ensure transparency in your AI systems, consider:
- Documenting AI Processes: Maintain detailed documentation pertaining to all of your AI models. Include data sources, pre-processing techniques, and model architecture. Make this documentation available to relevant stakeholders, and regularly update it to reflect any changes or improvements.
- Using Explainable AI (XAI): Your AI models must be interpretable. This means using algorithms that can explain their decisions, such as rule-based systems or model-agnostic methods. Explainable AI ensures users understand how decisions are made, and with this understanding, they’re better able to identify potential errors or biases.
Accountability
This rule asks that you take ownership of the outcomes and impacts of any AI system you use in your business. It’s one of the tougher aspects of creating a set of ethical guidelines as it means you’ll be held responsible for anything your AI technology produces. However, it’s one of the most important golden rules as it gives you a framework for addressing any issues that arise.
To work accountability into your AI systems, consider:
- Formalising Your Ethical Guidelines: Write a set of ethical guidelines specific to the AI you’ll be developing and/or deploying in your business. Your guidelines must align with industry standards and broader legal requirements while addressing potential ethical concerns specific to your business.
- Implementing Human Oversight: This includes regular monitoring, auditing, and evaluation of the performance of your AI tools and systems. The aim is to detect biases, errors, or unintended outcomes, ensuring the decisions made by AI systems align with your values and ethical principles.
Privacy
Privacy involves the steps you take to safeguard sensitive data, ensuring it is protected throughout the AI lifecycle. Privacy is central to developing trust with your customers, your team, and other stakeholders.
To minimise the risk of unauthorised access or data breaches, you must ensure you’re compliant will all privacy regulations relevant to your industry. You’ll also need to implement robust data protection measures. These should include:
- Encryption: To protect sensitive data, convert it into a secure format that can only be accessed with the right decryption key.
- Access control: Restrict access to personal data through a robust access control system, ensuring only authorised personnel can view or modify it.
- Anonymisation: Remove or alter personally identifiable information from datasets to preserve privacy while still allowing for analysis.
- Privacy impact assessments: Conduct periodic assessments to evaluate the potential privacy risks associated with your AI system and take necessary measures to mitigate those risks.
- Data minimisation: Collect and retain no more data than is necessary for your AI system to function effectively. This will reduce the potential damage that could be caused by a data leak or breach.
- User consent: Obtain informed consent from anyone whose data will be processed by your AI system. Clearly communicate how you will use their data, who can access it, how long it will be retained, and how it will be protected.
- Employee training: Provide regular training for employees who work with your AI tools. Place an emphasis on privacy protection, and ensure they’re equipped with the knowledge and skills needed to work with AI responsibly.
- Third-party audits: If you work with any third-party vendors or service providers, conduct regular audits to ensure they’re also adhering to privacy best practices.
Fairness
Fairness is all about favouring unbiased decision-making and ensuring equal treatment for all individuals. It involves weeding out biases and ensuring your AI tools don’t discriminate unfairly based on factors such as race, gender, or socioeconomic status.
To ensure fairness in your AI systems, you may have to do significant work to address potential biases in data collection and model training. Thorough data analysis lies at the heart of this step, allowing you to identify biases in a given dataset. From there, you can implement techniques like algorithmic fairness to address these biases.
Unfortunately, this is not a task you can complete and then leave behind. Addressing biases is an iterative process that requires regular monitoring and auditing of your AI systems. If you’re unsure whether this is something you need to do or whether it’s the domain of your AI service provider, you can contact your provider for support or speak to a managed services provider like Invotec.
Reliability & Safety
Reliability refers to the consistency and accuracy of your AI technology. To achieve reliability, you need to ensure your AI tools perform as intended under a range of relevant conditions. On the safety front, your focus should be on minimising risks and preventing harm.
To ensure your AI systems are safe and reliable, you’ll need to conduct rigorous testing and validation. You or your IT team will need to evaluate the performance of your AI models across various scenarios, ensuring they meet your minimum accuracy thresholds. Fail-safe mechanisms like error-handling strategies and the aforementioned human oversight can enhance the reliability and safety of your AI technologies.
Non-Maleficence (aka Avoiding Harm)
Non-maleficence is an ethical principle that guides you to avoid harm or, at the very least, minimise any negative impact caused by AI. To hit the mark with this golden rule, you’ll need to identify potential risks associated with your AI systems and take proactive measures to mitigate them.
Thorough risk assessments are the foundation of non-maleficence, and they must be carried out regularly throughout the development lifecycle. You’ll need to:
- Identify potential biases, security vulnerabilities, or unintended consequences that may arise from using your AI tools.
- Implement robust governance frameworks and continuous monitoring to mitigate risks and avoid potential harm.
Beneficence (aka Doing Good)
Moving beyond the basic foundation of avoiding harm, it’s also worth seeking positive outcomes that contribute to the well-being of individuals and society as a whole. This is where the final golden rule, beneficence, comes in.
Beneficence involves using the capabilities of your AI system to improve decision-making processes in a way that supports your team, your customers, and your other investors. If possible, it’s worth extending beyond your immediate stakeholders to address broader societal challenges. The idea is to focus on creating social value and making a positive impact in your community.
Thankfully, if you’ve followed the golden rules described above, you’ll already have most of the tools needed to tick beneficence off your to-do list. You’ll have a set of ethical guidelines written specifically for your business. You’ll also have a clear overview of the potential benefits and risks associated with your AI applications.
From here, you’ll need to seek feedback from all relevant stakeholders to gather feedback and insights on how your AI technologies can better meet their needs. You’ll also need to adopt a social responsibility framework. You can use this to identify and mitigate potential risks and promote responsible AI practices within your company.
Got questions about implementing AI in your business? Contact Invotec today. As a Melbourne-based MSP with more than 20 years in the industry, Invotec has a strong track record for delivering high-quality outsourced IT services to Australian businesses. We take the time to listen to your questions, ideas, and business goals before developing a customised plan that will work for your needs and budget. Give us a call at 1300 468 683 or fill out the form below to arrange a free consultation.
