Ethics of Automated Decision-Making

After hundreds of applications, you finally land an interview for your dream job. To your shock, the first round isn’t with a person, it’s with an algorithm that scans your responses to pre-recorded questions. It scores you poorly for a few “ums,” your discomfort with the AI format, and your confusion about the process—shutting down your opportunity before a human ever sees your application. 

Scenarios like this raise pressing questions about the ethics of automated decision-making (ADM), a domain within AI ethics that examines the moral and societal implications of delegating choices to algorithmic systems.  In this context, ADM encompasses algorithmic systems, ranging from rule-based to self-learning, that collect and analyze data to generate outputs guiding or substituting human decision-making. ADM now impacts nearly every aspect of society, representing a monumental shift in decision-making environments that previously relied on human experts.¹ In its application spanning from approving credit cards and guiding autonomous vehicles to suggesting medical diagnoses and predicting mental health disorders, the ethics of ADM must be discussed to fully understand both the benefits and risks.^{2, 3, 4} The ethics of ADM judges how algorithms guide choices with respect to human agency and oversight. 

When we think about ADM, technical frameworks like decision trees or neural networks might come to mind, but we must take a broader perspective to understand the ethics behind it.¹ Key features like autonomy, efficiency, and scalability for complicated decision-making scenarios factor into our evaluation, as well as the reality that many ADM models are now AI-based decision-makers that replace or supplement human experts. With automated systems taking on levels of agency previously reserved for humans, the behavioral design and application of ADMs need to embody humanist values and ethics in its interventions.⁵

What primary ethical challenges come with ADM systems?

In our discussion on the ethics of ADM, we must consider what the key ethical concerns are when employing these systems. These challenges highlight how automated systems not only make technical decisions, but also shape trust and accountability in human terms:

While we’ve captured some of the core ethical challenges with ADM, they are just some of the key areas where guidelines are needed. As a versatile solution to potential issues, researchers suggest that ethics-based auditing may be a feasible way to support the governance of organizations that are using ADM to make high-stakes choices.¹ Ironing out ethical regulations, ensuring ADM systems abide by them, and earning the trust of stakeholders is no small feat. To simplify this solution, we can visualize the dynamics as a series of relationships and paths of information exchange between organizations and human agents:¹ 

Ethical scrutiny for ADM, with ethical AI solutions

At the crux of ADM, there are promises of autonomy, efficiency, and scalability for solving complex problems. The paradox of these qualities is that they may magnify harms if we don’t analyze the ethics of automated processes. It is important to recognize possible trade-offs between efficiency and fairness, as well as consistency and trust. For these reasons, ethical scrutiny of ADM and AI systems is necessary to ensure they strengthen ethics in institutional and organizational decision-making.¹¹
If we pursue ethical scrutiny for ADM systems, then we need frameworks that can sufficiently capture the risks. Two key models that can guide how we design, use, and update ADM are responsible AI and ethical AI, which both pave the path forward for ADM systems that prioritize high ethical standards.^{12, 13, 14} While these models overlap in their goal of upholding principles like transparency and accountability, they differ in their specifics. Let’s take a closer look at these ethics-oriented AI models while considering the promises and risks of ADM in practice:

Responsible AI and ethical AI are complementary lenses that help us push ADM toward safer and fairer outcomes. In principle, they uphold governance and human rights alike without compromising human agency. With the rise of generative AI, the future of ADM demands powerful safeguards to manage novel risks and mandate responsible use.