Eliminating Algorithmic Bias Is Just the Beginning of Equitable AI

Eliminating Algorithmic Bias Is Just the Beginning of Equitable AI

Artificial intelligence (AI) is transforming our lives and workplaces, offering enormous promise for productivity gains and creativity. Applications range from automating repetitive chores to groundbreaking advances in healthcare. However, it's becoming more and more clear that not all of AI's promises will materialize; in fact, it runs the risk of widening social and economic divides, especially when it comes to racial and demographic imbalances.

Leaders in industry and government are being urged to guarantee that everyone can profit from AI-driven breakthroughs. However, it seems that every day brings up a new way for AI to cause injustice, leading to a patchwork of reactive responses or, more often than not, no response at all. We will need to take a proactive, comprehensive approach if we hope to solve AI-driven inequality successfully.

The first step in making AI more egalitarian for governments and corporate leaders is to identify the three ways that AI might exacerbate inequality. We suggest a simple, systemic approach that integrates these three drivers while emphasizing the complex social processes by which AI generates and maintains inequality. This framework has two advantages. First of all, its adaptability guarantees use in a variety of settings, including manufacturing, healthcare, and the arts. Secondly, it sheds light on the sometimes disregarded, interconnected ways in which artificial intelligence modifies the demand for products and services—a key channel via which AI reinforces inequality.
The three interrelated dynamics in our framework—technological, supply-side, and demand-side—are what drive AI's creation of inequality.

Technological forces: Algorithmic bias

When computers consistently make decisions that penalize particular groups of people, this is known as algorithmic bias. When it comes to important domains like credit scoring, criminal justice, and healthcare, it can have terrible outcomes. Researchers looking into a popular healthcare algorithm discovered that it drastically understated Black patients' requirements, which resulted in noticeably less care. This is not only incredibly damaging, but unfair as well. Algorithmic bias frequently arises from underrepresentation of particular demographics in the data used to train AI algorithms or from ingrained cultural preconceptions in the data.

Supply-side forces: Automation and augmentation

AI frequently reduces the cost of providing some goods and services by enhancing and automating human labor. According to studies conducted by economists like Daniel Rock and Erik Brynjolfsson, certain jobs are more likely than others to be mechanized or enhanced by artificial intelligence. "Black and Hispanic workers... are overrepresented in jobs with a high risk of being eliminated or significantly changed by automation," according to a telling report by the Brookings Institution. This isn't because the algorithms at play are biased; rather, it's because investing in AI might be a competitive advantage in certain industries because they include tasks that are simpler to automate. However, the concentration of persons of color in those particular jobs makes automation and augmentation of employment through AI and wider digital revolutions potentially racially unequal.

Demand-side forces: Audience (e)valuations

People's perceptions of the value of professions, goods, and services may change as a result of AI integration. To put it briefly, AI also modifies demand-side dynamics.

Let's say you find out your doctor treats or diagnoses patients using AI techniques. Does it mean it would affect your choice to see them? Then you are not by yourself. According to a recent survey, 60% of American citizens would feel uneasy if their doctor used artificial intelligence (AI) to treat and diagnose illnesses. They might have less of a need for AI-infused services from an economic standpoint.

Why AI-augmentation can lower demand

Our latest study clarifies why AI-augmentation can reduce demand for certain products and services. We discovered that when experts market AI-augmented services, customers frequently believe that they are less valuable and knowledgeable. This penalty for AI-augmentation applied to a wide range of services, including copyediting, graphic design, and coding.

But we also discovered that opinions on AI-assisted labor are not universally held. We dubbed 41% of survey participants "AI Alarmists" because they voiced doubts and worries regarding the application of AI in the workplace. In the meantime, 31% of respondents identified as "AI Advocates," fervently supporting the incorporation of AI into the workforce. The remaining 28% of people are "AI Agnostics," or those who are unsure about AI but aware of its possible drawbacks as well as advantages. This range of opinions highlights the lack of a coherent, unambiguous mental model on the worth of labor enhanced by AI. These results highlight clear differences in people's social (e)valuations of the uses and users of AI and how this influences their demand for goods and services. This is at the core of what we plan to investigate in more detail, even though the survey was conducted online and was based on a relatively small sample size.

How demand-side factors perpetuate inequality

This perspective, which concerns how audiences view and value AI-augmented labor, is often overlooked in the larger conversation about AI and inequality, despite its importance. Knowing the winners and losers of AI and how it can exacerbate inequality requires a grasp of demand-side analysis.

This is particularly true when prejudice toward marginalized groups collides with people's perception of AI's value. Professionals from dominant groups, for instance, are usually taken for granted when it comes to their competence, whereas similarly competent professionals from historically marginalized groups frequently encounter doubts about their knowledge. People in the scenario above are dubious of doctors using AI, but different professionals may have different reasons for having this mistrust. It is likely that doctors from underprivileged backgrounds, who already deal with patient mistrust, will be most negatively impacted by this AI-induced loss of trust.

It is less obvious how to address audience prejudice in their assessments of historically marginalized groups, even while efforts are already being made to address algorithmic bias as well as the consequences of automation and augmentation. However, there is still hope.

Aligning social and market forces for an equitable AI future

All three elements need to be acknowledged and understood in order to genuinely promote an equitable AI future. Even though these factors are separate, they are closely related, so changes in one have an impact on the others.
Imagine a situation where a physician chooses not to use AI technologies in order to prevent alienating patients, even if the technology enhances the quality of care. This is how this might play out. In addition to having a negative impact on the physician and their practice, this resistance denies their patients access to AI's potential benefits, such early cancer screening identification. Additionally, if this doctor works with diverse populations, this could exacerbate the underrepresentation of such populations in datasets used for AI training, as well as their health-related characteristics.

As a result, the AI tools lose their ability to adapt to the unique requirements of these communities, continuing the cycle of inequality. A negative feedback loop may develop in this manner.

The tripod analogy is a good one: if one leg is weak, it will affect the stability of the entire structure, which will affect how well it can shift angles and perspectives and ultimately affect how valuable it is to its users.

We would be well to look to frameworks that allow us to create mental models of AI-augmented labor that encourage fair gains in order to avoid the negative feedback cycle mentioned above. Platforms offering AI-generated goods and services, for instance, must inform customers about AI augmentation and the particular abilities needed to operate AI tools efficiently.Stressing that AI enhances human expertise rather than replaces it is a crucial element.

Reducing algorithmic biases and lessening the impact of automation are necessary but insufficient. Collaboration amongst stakeholders will be essential to usher in an era when the use of AI functions as a lifting and equalizing force. Industries, governments, and academics need to collaborate through leadership and thinking partnerships to develop innovative approaches that give human-centric and fair benefits from AI top priority. Accepting these measures will guarantee a more seamless, equitable, and stable transition to our AI-enhanced future.

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