Yes, ethical debt is a problem for AI software development

In the ever-accelerating race to innovate within the field of Artificial Intelligence (AI), developers and companies may find themselves accumulating what is known as "ethical debt."

Much like technical debt, where expedited solutions and shortcuts can lead to more significant problems down the line, ethical debt refers to the compromises made in ethical considerations during the developmental phases of AI technologies.

In other words, it’s time to “move fast and break things,” to quote Mark Zuckerberg’s old motto. Of course, when you break things, you might have to fix them later — at a cost.

From a recent article in Fast Company, Tech companies often ignore ‘ethical debt.’ That could be a real problem when it comes to AI:

The root of this dilemma is uncertainty, which is a common side effect of many technological revolutions, but magnified in the case of artificial intelligence. After all, part of the point of AI is that its actions are not known in advance. AI may not be designed to produce negative consequences, but it is designed to produce the unforeseen.

However, it is disingenuous to suggest that technologists cannot accurately speculate about what many of these consequences might be. By now, there have been countless examples of how AI can reproduce bias and exacerbate social inequities, but these problems are rarely publicly identified by tech companies themselves.

The problem stems from a widely held narrative, let’s call it the AI Inevitability Hypothesis. AI's inevitability, predicted by industry, academics, and industry analysts, goes without question. That the issues of bias, exclusion, and disinformation are social problems that cannot be addressed with pattern-matching and curve fitting and cannot satisfactorily be dealt with by technology is a strength, not a weakness of the inevitability narrative.

The false promise of "solving" bias computationally obscures the larger issue: bias is pervasive. But there is an industry-wide strategic switcheroo lurking here, by a beguiling diversion. Instead of being a problem, bias becomes part of the broad picture of AI innovation. Fear not, AI will solve the problem. It's like Hunter S. Thompson's definition of Gonzo Journalism: start a fire and report on it.

The rapid innovation in AI technology harbors the risk of accumulating ethical debt, a phenomenon with array of reasons ranging from a focused race to market dominance to unintended biases and consequences, a lack of accountability, and the undermining of societal norms and structures.

A focus on speed over ethical development

Companies in the highly competitive AI landscape often prioritize speed over ethical diligence to secure a dominant market position. This rush can overlook critical ethical implications, thereby accumulating ethical debt. In the bid to innovate swiftly, developers might neglect to sufficiently address the potential misuse of the technology, setting a stage for ethical dilemmas in the future.

AI technologies learn from existing data, which can sometimes harbor inherent biases. Rapid development cycles may not allocate sufficient time to identify and correct these biases, perpetuating discrimination, and unfair practices. Developers might overlook or under-estimate the unintended consequences of AI applications. These technologies can sometimes be used in ways not initially intended, leading to a variety of ethical issues, including privacy violations and security breaches.

Transparency and accountability

AI technologies, especially deep learning models, can be incredibly complex and opaque. The fast pace of development can forgo the necessary steps to ensure transparency, making it difficult to ascertain how decisions are being made. Quick development timelines might overlook establishing clear lines of accountability for AI's actions, creating a future where it's challenging to hold systems or their creators accountable for potential harms.

Rapid innovations might not sufficiently incorporate mechanisms to obtain informed consent from the users, thereby accumulating ethical debt by undermining the autonomy and privacy of the users. When the pace of development is frenetic, there might be inadequate engagement with a diverse range of stakeholders, leading to a lack of representation and inclusivity in technology development, which can foster ethical debt.  

Here are factors that lead otherwise reasonable people to pursue innovation at the expense of the “good.”

A focus on speed over ethical development

1. Rush to market

Companies in the highly competitive AI landscape often prioritize speed over ethical diligence to secure a dominant market position. This rush can overlook critical ethical implications, thereby accumulating ethical debt.

2. Ignoring potential misuse

In the bid to innovate swiftly, developers might neglect to sufficiently address the potential misuse of the technology, setting a stage for ethical dilemmas in the future.

Unintended Consequences and Biases

3. Inherent biases

AI technologies learn from existing data, which can sometimes harbor inherent biases. Rapid development cycles may not allocate sufficient time to identify and correct these biases, perpetuating discrimination and unfair practices.

4. Unintended consequences

Developers might overlook or under-estimate the unintended consequences of AI applications. These technologies can sometimes be used in ways not initially intended, leading to a variety of ethical issues, including privacy violations and security breaches.

Transparency and accountability

5. Lack of transparency

AI technologies, especially deep learning models, can be incredibly complex and opaque. The fast pace of development can forgo the necessary steps to ensure transparency, making it difficult to ascertain how decisions are being made.

6. Accountability

Quick development timelines might overlook establishing clear lines of accountability for AI's actions, creating a future where it's challenging to hold systems or their creators accountable for potential harms.

Inadequate stakeholder engage,emt

7. Undermining user content

Rapid innovations might not sufficiently incorporate mechanisms to obtain informed consent from the users, thereby accumulating ethical debt by undermining the autonomy and privacy of the users.

8. Insufficient engagement with diverse perspectives

When the pace of development is frenetic, there might be inadequate engagement with a diverse range of stakeholders, leading to a lack of representation and inclusivity in technology development, which can foster ethical debt.

Technological determinism

9. Reinforcing existing power structures

AI technologies developed rapidly can sometimes reinforce existing power structures and societal inequalities, as they might not allocate the time to interrogate the ethical dimensions and the broader impacts of the technology on society.

10. Underestimating technology's influence on society

Developers might underestimate the profound influence AI can have on societal norms and structures, inadvertently encouraging a form of technological determinism where technology shapes society, sometimes in ethically undesirable ways.

Resource constraints

11. Limited resources for ethical oversight

Start-ups and even established companies might allocate limited resources for ethical oversight in the rush to innovate, thereby accumulating ethical debt that can manifest as regulatory backlash and loss of public trust in the future.

12. Inadequate legal framework

In many jurisdictions, the legal framework has not kept pace with advancements in AI. This discrepancy allows companies to innovate without stringent regulatory checks, fostering environments where ethical debt can accumulate rapidly.

My take

To foster a sustainable AI ecosystem, it is imperative to balance the pace of innovation with ethical deliberations, thereby ensuring that the technology serves humanity while adhering to the highest moral principles. It is only through a diligent approach to development that embraces ethical considerations that we can hope to mitigate the accumulation of ethical debt in the AI landscape.

In the next installment, I’ll provide some solutions to ethical debt.