61% of consumers say it’s increasingly important for businesses to build and maintain trust as AI technologies advance, according to a Salesforce Research statistic mentioned in HubSpot Blog.
With the explosion of AI tools within the past few years, this trust matters now more than ever. Consumers are more aware of data misuse, and there is rising regulatory scrutiny over AI practices. As a result, developing and maintaining ethical privacy and security policies for the use of AI will be crucial for businesses hoping to grow and maintain a strong customer base.
While AI is a powerful tool with the potential for transforming businesses, it can erode trust if misused. By implementing AI ethics into your company policy, you can:
-Stand out from the competition. Customers and stakeholders alike appreciate companies that hold themselves to high ethical standards. They want their information kept safe, so being transparent about your policies will give them greater confidence in you.
–Manage risks. Your chances of having data leaks, hallucinations, or other unwanted data breaches decrease when your company follows ethical guidelines. In addition, if/when incidents do occur, your company’s ethical policies can prepare you to address them professionally.
–Invest in your brand. Implementing AI ethics is an investment. Since it can help you retain customers long-term, it’s one of the most important steps you can take to set your company up for stability and success.
Without further ado, let’s talk about why AI ethics is essential and how you can follow these principles.
I. Why Ethics in AI Is No Longer Optional
AI ethics is no longer optional because the stakes are high for AI misconduct.
Consumers expect privacy. According to HubSpot, 81% of consumers have become more concerned with how companies are using their data. They also expect the information they receive and give to be accurate. Lastly, they expect you to be transparent about your data collection and retention methods.
AI increased the demand for privacy because automated decisions feel less personal and accountable; moreover, “black box” systems have increased skepticism.
Along with this increased skepticism has also come the concern over companies producing “AI slop”: content that is fully generated by AI with seemingly no human touch.
When people feel like they’re reading content that was written by a robot rather than a human, they may have several concerns. First, the quality of the information comes into question. Since AI can make mistakes, AI-generated content can’t always be trusted. Secondly, low-quality automated content can have a bland, impersonal feel, leading audiences to feel unseen, jaded, and uncared for. Finally, if content looks over-automated and lacks human oversight, it’s hard for companies to build the authentic connection they seek to maintain with their audiences.
Ultimately, if companies use AI in this way without checking their work for quality and accuracy, they are likely to lose trust with their audience, and it’s a lot harder to rebuild trust with an audience than to lose it. This is because one mistake or a series of unpleasant interactions can lead customers to feel betrayed. Considering this, it’s important to keep customers in mind when creating content that will reach them.
Furthermore, data risks come with using AI. Companies could easily cross customer boundaries by leaking their personal information using AI. For example, companies could accidentally write prompts with sensitive data or train AI models on proprietary or personal information. The risks that come with these errors could include but are not limited to data leakage, regulatory penalties, and a loss of customer trust.
While it’s crucial to be aware of these risks, companies should not let these potential pitfalls stop them from using AI; instead, they just have to be informed about how to avoid the pitfalls mentioned above. Let’s look at how ethical AI practices can prevent those outcomes:
II. Core Ethical Pillars for Responsible AI Use
In AI systems, accountability is all about taking ownership of your work. It’s about defining clear roles and responsibilities for each team member and acting responsible for the success or failure of every task. Establishing an organizational hierarchy, model oversight committees, and incident escalation processes can help you ensure you have the systems in place to own your work.
Accountability builds both internal and external trust. By establishing and following set procedures, you send the message to your company and your customers that you take your work seriously in all circumstances, and that AI is no exception to that rule.
Secondly, reliability is ensuring that AI systems will follow through with the tasks they’re supposed to execute. It also means that they’ll be accurate, fair, and predictable in all that they do.
To maintain reliability, make sure to check data for quality, continuously monitor data, and retrain models as needed to help them work properly. You may want to consider having a few reliable data analysts on your team to check the data, and a few AI model engineers to help you with the overall health of your system.
Unreliable models can result in poor outputs, business disruption, and an overall loss of confidence in your company. Therefore, it’s necessary to go through validation cycles and human review checkpoints to avoid AI blunders.
Thirdly, explainability is needed at the enterprise level because when every member of your team can understand and explain how the model works, they’ll be more likely to recognize if anything goes awry.
Industries where trust and personal information protection is paramount to client safety, such as healthcare, finance, and insurance, would especially benefit from explainable models. When everyone in the company is informed on how the model works, they can each contribute to the maintenance of it.
Explainability helps companies identify bias, justify decisions, and comply with regulatory requirements. Members’ abilities to explain how the model works helps them easily identify and describe each part to anyone who asks, which builds credibility with both internal and external stakeholders.
Furthermore, AI can be vulnerable to model poisoning, data breaches, and ransomware targeting AI systems. Therefore, strong security measures are paramount.
Encryption, identity and access management for each team member, and secure training environments can keep your data stored safely and prevent leaks. By building these practices ahead of time, you can avoid the need to react to security issues as they come up.
Finally, privacy is a common concern when it comes to AI. AI’s heavy reliance on cloud infrastructure can make it more prone to unwanted privacy leaks. As such, prioritize PII protection, anonymization of personal data, and minimizing the data entered into AI prompts and models. These practices will build trust with your customers.
III. Beyond Governance: Social and Environmental Considerations
If not careful when developing AI models, they could potentially demonstrate historical bias, marginalization, and discrimination.
So, in addition to caring about privacy, security, and transparency, we must also consider social and environmental justice when implementing AI systems.
According to European Commission, this looks like making sure AI doesn’t use unfair bias, such as marginalization of specific groups or exacerbating prejudice and discrimination. It also should be accessible to anyone with any disability and should be vetted by relevant stakeholders. Adding inclusive datasets and testing these rigorously will be crucial to upholding equity standards.
Furthermore, AI should take into account the health of our planet and our future generation’s wellbeing. They should take the environment and other living beings into account, and consider their social and societal impact. Environmental policies, such as measuring AI resource use and energy consumption, should be a part of company policy alongside ethical guidelines.
Sasha Luccioni created a tool called Code Carbon that runs parallel to AI systems and measures their energy use and carbon emissions. She said a tool like this can help leaders make smarter choices about which models to use and deploy models based on renewable energy.
Code Carbon isn’t the only sustainable-friendly tool that’s been created; Hugging Face runs a leaderboard called AI Energy score to score models based on how much energy they use across 10 different tasks, such as image and text generation, according to Science News.
Tools like these can help companies align their AI goals with sustainability goals.
To further promote ethical AI practices in general, UNESCO’S “Recommendation on the Ethics of Artificial Intelligence” suggests 10 ethical principles for implementing AI: Proportionality and do no harm, safety and security, right to data privacy and protection, multistakeholder and adapative governance collaboration, responsibility and accountability, transparency and explainability, human oversight, sustainability, public education, and fairness and non-discrimination.
Doing no harm is about not doing what is beyond necessary for an AI system to accomplish. It’s also about conducting thorough risk assessments to minimize and prevent harm.
Safety and security is about preventing safety risks and vulnerabilities to attacks. Privacy and protection is about ensuring privacy throughout the AI lifecyle and taking steps for data protection.
Multistakeholder and adaptive governance collaboration is about respecting international and national law and being inclusive of diverse perspectives in the making of AI governance. There should also be responsibility and accountability to make AI systems auditable and traceable. There should be human oversight and due diligence mechanisms in place to avoid harm. Transparency and explainability means that the level of openness and explainability should be appropriate to the context the AI model is placed in, and it should not interfere with privacy, safety, and security.
Human oversight is about making sure that humans remain accountable and responsible for the AI systems they create, as well as the work and consequences that result from those systems. Sustainability is about assessing AI technologies against the sustainability policies set by organizations such as the UN. Awareness and literacy is about giving the public the education they need to understand and use AI ethically. Finally, fairness and non-discrimination are about ensuring social justice and inclusivity in AI actors.
These principles serve as a guiding framework for using AI responsibly.
IV. From Principles to Practice: How Psycray Ensures Ethical AI Usage
At Psycray, we prioritize ethics by ensuring customer privacy and creating high-quality messaging. Our humans lead while AI supports what we do.
For example, when we build any kind of tech solution for a client, we always implement security features such as a secure single sign-on or enhanced domain protection. This helps our clients feel safe working with us, knowing that their internal and external data will be protected.
In addition, we hold ourselves to high standards when creating content or cold outreach emails, taking care to base our creative work in human thought and refine with AI as necessary instead of relying on AI to do the work for us. This helps us maintain our creativity and use AI as a tool to improve. As a result of these policies, we enjoy high quality work, more authentic communication with our audiences, and reduced risk of spreading misinformation.
V. Concluding Thoughts: Ethical AI as a Competitive Advantage
In conclusion, practicing ethical AI use is imperative for any business looking to grow. Customers care about their privacy and want to trust that the businesses they rely on will tell them the truth.
It’s up to us to uphold the highest standards for ourselves and our clients.
If our privacy, security, and transparency solutions interest you, feel free to contact us — we’d love to learn more about how we can help your business through ethical, practical AI solutions.