Trustworthy AI: The Ethical Dilemma and How to Mitigate Risks

Trustworthy AI: The Ethical Dilemma and How to Mitigate Risks

By: TEAM International | May 3, 2022 | 15 min

Nowadays, we all hear the buzzwords and terms “artificial intelligence” and “AI” coming from everywhere. Your company might even have adopted this trend already. We can all agree that there are almost no barriers to AI development and deployment anymore. But what about the effect on human values and ethics? Reputational, legal, and regulatory risks are still dangerous in case of technology misuse. So, we recommend implementing ethical AI practices to mitigate any current and potential risks.

At TEAM International, we know how tricky the road to meeting AI transparency and accountability standards can be. So, our engineers have put together this quick guide to help you navigate the benefits and risks of AI when it comes to morality.

Behind the curtain, where the ethical dilemma hides

AI is a powerful tech tool able to bring real value to society and your organization alike, but only if handled correctly. For instance, healthcare professionals diagnose patients faster and more accurately using AI-enabled systems. Moreover, AI technology allows us to detect plant diseases early on, predict wildfire progress based on satellite data, and do social good within hundreds of other use cases.

However, neglecting the ethical risks of AI can lead to data leakage and corruption or systematic bias against disadvantaged and vulnerable groups. What does that mean? It means that your enterprise might face a threat of lawsuits, damaged reputation, and wasted resources. Remember the scandal when Amazon had to scrap its AI-based recruiting software because of its bias against women? We bet you’d like to avoid such situations.

Emphasizing AI transparency and accountability is crucial if you want to ensure your company’s artificial workforce won’t misbehave.

What is trustworthy AI?

The idea lies in trust as the foundation of sustainable development and the belief that businesses won’t benefit from artificial intelligence systems (AIS) unless we safeguard ethics. A majority of global IT specialists reported that building trust in AI systems is imperative for business success and regulatory compliance.

Top barriers to developing trusted ai

But how do you establish trust in all phases of AI system development and implementation? The only way to achieve reliable artificial intelligence is by building fair, transparent, reliable, and accountable AI systems committed to data privacy. Let’s explore these key elements that embody a trustworthy AI model.

5 pillars of an ethical artificial intelligence system

As we often emphasize, it’s better not to rush your enterprise’s digital transformation. So, while welcoming an artificial workforce, assess each component of trustworthy AI carefully to avoid going down the rabbit hole of risks.

  • Fairness. Artificial intelligence systems learn from ready-made datasets. So, if the provided data is even a little bit biased, it will affect your software behavior. We’ll put it like this—the gender pay gap can be responsible for AI showing ads of higher-paying jobs to men compared to women. So, in the interests of AI bias mitigation, you should be careful with what you’re feeding your algorithms. We recommend that you start with labeled training data and analyze the results before going all in with the implementation.
  • Transparency and explainability. Be open with your stakeholders and customers about what data you collect and utilize. Explain how your AI predicts behavior and give examples showcasing specific use cases. Take extra measures to build trust—publish transparency reports that will demonstrate your AI’s algorithms, attributes, decisions, and correlations. You need to forget a black-box development approach if you’re committed to responsible AI development.
  • Reliability and robustness. You’ll be able to trust your AI solution only when it produces accurate and reliable results. Imagine you’re building intelligent software to help emergency dispatchers detect heart attacks by analyzing the caller’s voice and background noise. Surely, you’d expect it to perform exceptionally under any conditions since people’s lives are at stake. So, ensure that your data is accurate, run regular tests, make necessary adjustments, and install tech updates on time.
  • Accountability and responsibility. Having trustworthy AI tools means having clear policies that define roles and responsibilities for your team. You can’t blame technology for its poor decisions or misbehavior anymore. So, you should always know who’s accountable for the results and who can fix the problems. Identify what standards and laws regulate your AI’s legal liability and whether it’s auditable, then articulate the information to personnel.
  • Data privacy. Sixty percent of modern consumers worry about the ethical risks of AI compromising their information, and for a good reason. As you train your AI system using sensitive information, your company should treat that data with caution. Does your software comply with data protection laws such as GDPR, HIPAA, or others? Can users control their personal information and withhold it when needed? How do you store the collected data? Your customers and employees’ interests come first, so make sure your AI system is well protected against unauthorized access and disclosure.
trustworthy ai framework

How to build a trustworthy AI system

Organizations can no longer ignore the ethical risks of AI, acting as if only technology is the core of it all. People still create, manage, govern, and update it. So, it’s necessary to learn how to mitigate those risks using our best practices that we share below.

1. Create clear guidelines

Having an enterprise-wide AI “code of ethics” is the easiest and most reliable approach to ensure that you build artificial intelligence solutions responsibly. These guidelines should cover, at least, the basic ethical standards for creating and using AI tools and implementing trustworthy machine learning algorithms. Along with this, you need to provide a detailed description of how you’ll measure and enforce those standards. We advise you not to develop any algorithms until you finalize a corporate AI ethical framework and approve it with all stakeholders.

stats on ethical ai guideline implementation

The most efficient ethical guidelines for trustworthy AI will be those based on your industry needs. If you apply the technology to decide who will get a mortgage loan, it shouldn’t discriminate against candidates based on race, national origin, sex, or other factors. Likewise, healthcare institutions focus AI ethics on data privacy.

Finally, your “code” should cover every aspect, from making informed consent mandatory to transparently notifying customers about using their data. You can’t hide such things in lengthy documents that no one reads.

2. Ensure timely tech assessment and risk mitigation

You should monitor AI systems for trustworthiness, and address detected risks immediately to see your ethics management program in action. AI assessment consists of two parts:

  • Determine the efficacy parameters to target
  • Create test cases for AI audit. Here, it’s worth noting that you should test both your data and a trained AI system as a final product.

Once you detect inaccuracies, you can adjust your software accordingly. The main idea is to accommodate your raw data to reflect the desired outcomes better and, thus, improve your AI system. It must work properly even when pushed to its limits. In this case, your risk mitigation methods for achieving trustworthy AI are the fully-fledged algorithms themselves.

3. Offer end-to-end technical support

Most business owners mistakenly see AI fairness and equity as a development stage that they can complete and never return to. However, it’s better to keep ethics in mind during the entire project life cycle, continually striking a balance between the benefits and risks of AI. So, we encourage you to experiment with timing throughout your assessment—explore your test environment and consider all possible conditions, even the least likely ones.

tips for ethical ai projects

4. Introduce education and knowledge-sharing

Does your company regularly employ robust AI security measures? We’re sure it does. However, it’s often hard to keep up with emerging threats. Indeed, you can overlook AI ethics risks among all the others due to a lack of awareness. That’s why we recommend educating employees on the importance of ethical artificial intelligence for the company’s reputation and customer loyalty. You can update corporate values to make continuous learning and knowledge sharing a part of your internal culture and so encourage staff to keep up.

Balance the benefits and risks of AI

Are your engineering and management teams aware of trustworthy AI practices? Are you sure that your software is bias-free, secure, and reliable? Because just like any disruptive technology, artificial intelligence brings pros and cons alike. So, it’s essential to address the ethical dilemma once you decide to take full advantage of explainable AI tools.

If you don’t know where to start, hire an experienced AI-focused partner to support you on every stage of your AIS development journey and ensure commitment to ethical principles.

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