AI is gaining recognition as a cornerstone of innovation across various domains in this digital era. It is transforming industries at a rapid pace, from predictive analytics in healthcare to automated trading systems in the finance domain.

But at the same time, one misstep in AI applications is possibly all it takes to result in significant repercussions.

For instance, Microsoft’s Tay Twitter bot, an AI chatbot designed for learning and conversing with Twitter users had to be shut down only 16 hours after its launch. Turns out that it was attacked by people who exploited its learning capabilities and the bot ended up producing inappropriate and offensive content.

So, you wouldn’t want to sail a boat without a life jacket.

So, as a developer, it’s important to create a technological vision and strategy that makes effective and safe usage of AI.

What is Ethical AI?

Ethical AI aligns with AI safety in terms of minimizing harm and mitigating the newest risks the application of AI brings forth.

Simply put, it’s artificial intelligence that complies with a set of properly defined ethical guidelines to protect individual privacy, and rights, and ensure non-discrimination so that it benefits not only a specific group of individuals but the wider society around it.

So, as a developer, you’ll have to ensure that your overall organization technology vision has a safe and ethical usage of UI. But, when doing so, you’re bound to run into some challenges:

• Data privacy: AI systems often require access to vast amounts of data than any other technology. Most of the time, this data is highly sensitive personal information or business-related data that you can’t risk exposing. So, a data breach or a misuse of information can put your entire organization at risk.
• Bias and fairness: AI tends to reflect the data they are trained on. Therefore, there is a possibility that AI-powered solutions may provide biased results based on biases present in the training datasets, causing unexpected outcomes. For example, such algorithms can produce biased credit scoring systems and unfair hiring selections.
• Security vulnerabilities: Security vulnerabilities and AI-targeted attacks like model theft and data poisoning can compromise not only the system integrity but also the user’s safety.
• Company reputation and trust: Altogether, such challenges ultimately converge to pose a huge threat to the company’s reputation and trust. It can end up tarnishing the company’s image, reducing market share, and losing customer loyalty.

How Can You Employ AI Safety?

Situations like the Cambridge Analytica scandal that occurred in 2018 highlighted the need for better AI regulations.

Therefore, it is important to have proper AI safety practices and principles in place from the beginning to mitigate potential challenges and risks. Naturally, it falls upon the developers and other higher-ups to advocate building and testing AI systems with safety as a higher priority element from the design phases.

This is a shift-left approach where you integrate AI safety and ethical considerations early in the development life cycle, rather than as an afterthought. However, it may require a cultural shift emphasizing the importance of AI safety from the outset, but it is evidently an essential mechanism that must be embraced throughout the organization.

When establishing AI safety principles in your organization, Google’s Responsible AI Principles and the European Union AI Act can offer comprehensive frameworks for responsibly using AI. They highlight the importance of being accountable for AI decisions, mitigating biases, and ensuring transparency.

Implementing Robust AI Safety Measures

With the improvements and evolving AI has undergone in recent years, it is no longer in the infancy period it once was. Therefore, robust and sophisticated AI safety measures are a necessity to keep things under control and secure from threats.

Apart from establishing a proper set of AI safety principles, it is also a good practice to develop and use secure and explainable AI frameworks.

For instance, tools like IBM’s AI Fairness 360 toolkit help identify and handle bias and discrimination in machine learning models throughout the entire AI lifecycle. Additionally, organizations such as Google DeepMind and OpenAI adopt rigorous AI security measures including adversarial training.

By doing so, the AI systems are intentionally exposed to malicious inputs during the training phase as a measure to improve the system’s resilience against potential threats by identifying vulnerabilities early on. In addition, it is also a best practice to monitor and audit the AI system’s behavior in real time with a focus on fairness, explainability, and accuracy. Monitoring these metrics will help you identify anomalies before they grow into complex risks.

Compliance and Transparency

A proper AI governance framework is the key to ensuring compliance and transparency of AI operations.

A comprehensive governance framework includes multiple aspects of AI safety including the policies, procedures, and oversight mechanisms to encourage ethical development, deployment, and usage of AI technologies. This also includes conducting regular assessments to ensure that the AI systems operate as intended and comply with ethical and regulatory standards.

Apart from that, it is important to maintain well-defined roles and responsibilities and conduct AI decision-making with clear and precise transparency. This involves not only explaining the outcomes of AI decisions to the cross-functional stakeholders, but also documenting the underlying logic, data sources, and algorithms used where necessary according to proper guidelines. Engaging with external auditors, and educating the employees on AI safety and organization-specific AI principles can also support you greatly along the journey.

In the end, AI safety is not limited to adhering to legal and regulatory requirements, but also about building an environment where safety and ethical considerations are at the forefront of the beneficial use of AI technologies in an organization.

Wrapping Up

Adopting AI into your product life cycle is a task that requires careful consideration, patience, and sound technical knowledge. As a developer, the responsibility grows even more when AI safety and regulation come into the picture. It involves not only understanding the multifaceted nature of AI risks, but also addressing challenges through a proactive approach, employing AI safety principles, and implementing sophisticated measures for compliance and transparency.

I hope you find this article helpful.

Thank you for reading!