Artificial intelligence (AI) is becoming more common in our daily lives. Experts in data science believe that its integration will bring positive outcomes for both current and future generations, contributing to economic growth and overall societal well-being.

Trusting this technology will be key in a world where limitless connectivity improves lives, redefines business, and pioneers a sustainable future. But what does it mean to trust a technology fully?

This article explores the negative effects of unethical AI use and biases within AI systems. It stresses the importance of consumer trust and industry-specific regulations to ensure successful and fair AI implementation. Qualitative data and examples back the arguments.

With AI becoming more prevalent daily, it begs the question: How vulnerable are our rights as users without ethical AI?

Why Ethical AI Matters

AI systems need large amounts of data to improve performance, but collecting and processing personal information can raise concerns about its use and access. There is also a growing concern about using AI for surveillance and monitoring. 

Law enforcement agencies have been using facial recognition technology to track individuals in public spaces and identify suspects. However, this has raised questions about the right to privacy and the possibility of these technologies being abused.

Following GDPR when collecting, using, and processing data through AI is compulsory to protect personal data. AI algorithms must minimize personal data collection and processing while ensuring security and confidentiality.

From Human Bias to AI Bias

It is a valid concern that AI systems could uphold current biases and discrimination. When the data used to train an AI system contains biased preferences, the system may learn and continue to uphold those biases. This can have severe consequences, especially in employment, where AI algorithms may be used to make hiring decisions.

Responsible AI development and deployment demands transparent and secure data collection, bias identification, mitigation, and ongoing monitoring and oversight. We cannot compromise on any of these vital aspects.

Real-Life Case Studies

AI is advancing and will enable highly sophisticated systems that would never have been possible. Regarding AI ethics, one recent example discussed in the media concerns surveillance. 

In the fight against the COVID-19 pandemic, governments could introduce smart heat-measuring cameras, facial recognition, and aerial drones, for example, to spot individuals displaying symptoms and then contact anyone they’ve recently seen.

This policy’s obvious and likely unpopular downside is that all citizens would feel monitored and their actions judged. It could be argued that this infringes upon one’s integrity, privacy, and basic human rights.

Challenges and Solutions

Building trust is essential for users to accept AI-based solutions and those systems incorporating their decisions. There are, however, significant challenges in developing ethical and explainable AI models. 

One is the trade-off between attaining the simplicity of algorithm transparency and impacting the high-performing nature of complex but opaque models (when one increases the transparency aspect, privacy and the security of sensitive data come into question).

Many explainability methods concentrate on explaining the workings of an AI decision-making process, which can be detached from the context in which it is applied, leading to explanations that are not practical. To address this, researchers are working on incorporating knowledge-based systems to create explanations that apply to the context of their use.

Human biases must be minimized during deployment and implementation.

Deploying an AI system is just as crucial as developing it. Although it may not be entirely possible to eradicate biased data, there are methods to lessen its impact. One effective approach involves subject matter experts and the intended user group in the early stages of development. 

For instance, in Sweden, Trelleborg City used AI to manage social security concerns by engaging AI researchers and users during implementation. This strategy proved to be a binding factor for success.

AI guidelines are not enough.

When dealing with AI, it’s important to create tailored guidelines for different industries rather than a one-size-fits-all approach. Over 160 AI ethics frameworks and normative guidelines are published worldwide by international and non-governmental organizations and intergovernmental organizations like the UN and EU. 

While most of these guidelines follow the four basic bioethics principles of doing good, avoiding harm, promoting autonomy, and being just and fair, some also include a fifth principle of accountability and explanation. 

However, while these guidelines are a good starting point for assessing the ethical dimension of AI-enabled products, they are not enough. Due to the vast global product landscape, they may not always align with specific products’ target use and context. Guidelines must be improved to cover more aspects beyond consumer-focused ethics to support the ethical development of AI for business, industry, and society.

Conclusion

Ensuring ethical AI implementation typically falls on data scientists with extensive knowledge of AI’s internal workings. Nevertheless, navigating the ethical considerations associated with AI systems is an immense challenge that no individual can tackle alone.

A diverse team with diverse perspectives is required to map potential challenges and risks. Someone from an ethnic minority group, for example, maybe more aware of the discriminatory boundaries that the AI may inadvertently cross.

In short, even though AI cannot hold ethics, ensuring the ethical alignment of AI technology is paramount. It is pivotal to find a balance between building trust in its capabilities and avoiding excessive dependence on it.