Ethics in Artificial Intelligence: Balancing Innovation with Responsibility

mbalzyeni
Oct 22, 2024
6 min read

Written by Asante Nxumalo

Artificial Intelligence (AI) has moved from the realm of science fiction to being a core part of our daily lives, transforming industries, economies, and how we interact with technology. As the fastest growing technology of our era, AI is poised to continue revolutionising the world, with expectations that it will drive progress in areas ranging from healthcare and education to climate change solutions. However, with such powerful potential comes equally significant ethical concerns.

How do we ensure AI is developed and used in ways that benefit humanity while avoiding unintended consequences? In this blog, we explore four key ethical considerations: bias and discrimination, privacy and surveillance, transparency and explainability, and the role of human judgment in AI.

1. Bias and Discrimination: A Global Challenge with an African Perspective

One of the most pressing concerns in AI ethics is the issue of bias and discrimination. AI systems, which are trained on vast datasets, often reflect the biases present in those datasets. These biases can manifest in ways that disproportionately affect marginalised communities, whether it's through discriminatory hiring algorithms, facial recognition errors, or biased sentencing recommendations in the criminal justice systems.

From an African perspective, the challenge becomes even more pronounced. AI systems are primarily trained on data from the Global North, specifically the United States and Europe, leaving Africa significantly underrepresented. This leads to AI that struggles to understand the diverse realities of African populations. As Katherine Getao, former chief executive of Kenya’s state information and communication technology authority, put it during the Munich Cyber Security Conference, “Africa is a ‘shadow area’ in AI.” This underrepresentation means that AI systems do not have enough data from African countries to make fair and accurate decisions, further perpetuating inequality on a global scale.

Moreover, Gerao highlights the importance of closing the digital divide to address this issue. While internet penetration in Africa has grown from 9.6% in 2010 to 33% in 2021, it remains significantly lower than in developed countries like the U.S., where it stands at 92% (source). This digital divide prevents many Africans from participating in the data economy, making it difficult for AI to capture the full spectrum of human experiences and perpetuating biases that disadvantage the continent.

To mitigate these challenges, a concerted effort is needed to develop AI systems that are more inclusive and representative. This requires not only better data collection and dissemination across Africa but also addressing structural inequalities that limit access to digital technologies.

2. Privacy and Surveillance: When Data Becomes a Commodity

Image generated by DALL-E AI Image Generator

As AI systems become more sophisticated, so does their ability to collect and process vast amounts of personal data. This raises significant ethical concerns around privacy and surveillance. AI is increasingly embedded in technologies that track our online behaviour, personal preferences, and even physical movements, all of which are used to make predictions about our behaviour.

A prime example of this can be found in the HBO series Westworld. In the show, the AI system behind the lifelike robots, known as “hosts”, not only control their actions but also collects and stores vast amounts of data on the human guests who visit the park. This data is used to manipulate the lives of the guests in the real world, without their knowledge or consent. While this is a fictional depiction, it mirrors real-world concerns about the ways in which AI and big data are being used to influence decision-making and shape human behaviour.

In today’s world, companies and governments use AI to gather unprecedented levels of information on individuals, often without their explicit consent. The widespread use of facial recognition, for example, can lead to constant surveillance in public spaces. This raises questions about how much data should be collected, who has access to it, and how it is used. As AI’s reach extends, ensuring that privacy and surveillance concerns are addressed is critical to maintaining public trust in these systems.

3. Transparency and Explainability: The “Black Box” Problem

Another major concern in AI ethics is transparency. Many AI systems operate as “black boxes,” meaning their decision-making processes are opaque even to the people who develop them. This lack of explainability becomes especially problematic when AI is used in high-stakes environments like healthcare, law enforcement, or finance, where understanding the reasoning behind a decision is crucial.

For instance, AI systems in healthcare might recommend treatment plans or predict patient outcomes, but without transparency, doctors and patients cannot fully trust or challenge those recommendations. Similarly, in financial systems, AI might be used to approve or reject loan applications without the applicant ever understanding why they were denied credit.

The “black box” problem has led to the development of Explainable AI (XAI), a subfield focused on making AI systems more interpretable and accountable. Explainable AI seeks to ensure that decisions made by AI systems can be understood and scrutinised by humans. This is particularly important for maintaining ethical standards and ensuring that AI remains accountable to human oversight.

4. The Role of Human Judgement: Machines or Morality?

Ethics, at its core, involves determining what is right and wrong based on a set of moral principles. However, defining ethics is not straightforward, as ethical standards vary greatly across different geographies, societies, and cultures. What one society considers moral may be viewed differently in another, and these shifting norms make ethics a highly complex field. Ethical frameworks have evolved over centuries, shaped by historical, religious, and cultural contexts, and there is no single universal code that governs moral human behaviour.

Given this complexity, the fundamental question arises: Can machines, which are built on data-driven logic, ever truly replace human judgment in making ethical decisions? While AI systems are capable of processing vast amounts of information and making rapid predictions, certain decisions demand more than just data - they require moral reasoning. Machines lack the ability to contextualise ethical dillemas within the framework of human experience, cultural norms and societal values.

The challenge lies in determining how well AI can handle moral judgments when faced with ethically ambiguous situations. It remains unclear if or how machines can replicate the nuanced moral reasoning that humans rely on, especially when there is no universal ethical standard for them to follow.

This tension between machine efficiency and human judgment came to the forefront in 2023, when Elon Musk, along with over 1,000 experts in the field, signed a letter calling for a six-month pause on the development of advanced AI systems (source). The letter highlighted concerns that AI development was moving too quickly without adequate safeguards, raising the risk of AI systems making decisions with far-reaching consequences that we might not fully understand.

Similarly, global bodies like the European Commission and the OECD are developing regulatory frameworks to ensure that AI operates within ethical boundaries. The European AI Act, for instance, categorises AI applications by risk level and mandates stricter oversight for high-risk applications like facial recognition. These efforts underscore the importance of human judgment in guiding AI development and ensuring that it serves the public good.

Conclusion: A Path Forward

As AI continues to evolve, addressing its ethical challenges will be critical to ensuring that it benefits all of humanity. To combat bias and discrimination, especially from an African perspective, we need more inclusive datasets and better representation in AI training. Privacy and surveillance concerns can be mitigated by stronger regulations that protect individual rights and ensure data transparency.

As Katherine Getao emphasised, “There are several ways to address AI bias in Africa. First, focus more on data production and dissemination. Not just its protection. Second, close the gap between those who can access digital technology and those for whom it is still not available”. These recommendations, along with efforts to make AI more transparent and to maintain human judgment in its use, will help steer AI toward a future where it remains a tool for progress, not peril.

References

The Future of Ethics in AI:

https://link.springer.com/article/10.1007/s00146-021-01318-8

IBM’s AI Ethics Overview:

https://www.ibm.com/blogs/watson/2021/03/ai-ethics/

Ethics and Artificial Intelligence – Stanford HAI:

https://hai.stanford.edu/ethics-and-ai

Ethical Concerns in AI – Harvard Gazette:

https://news.harvard.edu/gazette/story/2021/02/ethical-concerns-mount-as-ai-takes-bigger-decis ion-making-role/

AI Bias in Africa – The Record Media:

https://therecord.media/lack-of-data-makes-ai-more-biased-in-african-countries-says-former-tec h-official

World Economic Forum – AI Bias: