AI and Inequality: How it Poses Tough Questions about Humanity

In 2035, a middle-aged woman named Maria lost her job as a cashier after the supermarket she worked for introduced self-checkout machines run by artificial intelligence. Despite her years of experience and good performance, Maria was replaced by a machine that could work 24/7 without complaining, getting tired or needing breaks. She spent months looking for another job, but most of the available positions required skills she didn't have, such as programming, data analysis or machine learning. She eventually became part of the growing army of people who were unemployed, underemployed or had given up looking for work due to the impact of AI on the labor market.

Maria's story is not unique. In fact, it's becoming increasingly common as AI systems continue to automate more tasks and occupations, disrupting not only the job market but also the social fabric of our societies. The rise of AI is creating winners and losers, with some individuals and organizations benefiting greatly from the new opportunities and efficiencies it offers, while others are left behind or even harmed by the negative externalities it generates.

Real-Life Examples of AI and Inequality

The impact of AI on inequality is not purely hypothetical or speculative. There are already numerous cases and studies that demonstrate how AI can exacerbate existing disparities and create new ones. Here are some examples:

• Automatic resume screening tools used by companies can discriminate against candidates from certain demographics or backgrounds, as they may have biased algorithms that favor or penalize certain keywords, experiences or schools.
• Predictive policing software used by law enforcement agencies can disproportionately target and harass communities of color or low-income neighborhoods, as they may have flawed models that overemphasize historical data and stereotypes over actual crime patterns and causes.
• Uber and Lyft drivers, who mainly depend on AI-based dispatch and rating systems to receive and keep their jobs, often face precarious conditions, low pay, and lack of benefits or protections, as they may have to compete with other drivers and comply with strict rules and ratings that often favor the companies over the drivers or the passengers.

Main Companies and Organizations Involved in AI and Inequality

The issue of AI and inequality is not isolated or limited to a few actors or industries. It involves a wide range of stakeholders, including:

• Google, Facebook, and other tech giants that dominate the AI research and development scene, often with little transparency, accountability, or democratization of the benefits and risks involved.
• Government agencies and policy-makers who are still struggling to catch up with the pace and scale of AI adoption and regulation, as well as to address the ethical, legal, and social challenges posed by AI.
• Labor unions, advocacy groups, and social movements that aim to protect the rights and interests of workers, consumers, and communities affected by AI, often with little influence or power compared to the corporations that control the AI systems and data.

Conclusion and Critical Comments

The rise of AI is both a blessing and a curse for humanity. On the one hand, it has the potential to revolutionize many aspects of our lives, from healthcare and education to transportation and entertainment. On the other hand, it poses tough questions about the nature, limits, and values of humanity, as well as about the distribution of benefits and harms in society. Here are some critical comments and recommendations:

• We need to rethink the assumption that AI is neutral, objective, or infallible, as it reflects and amplifies the biases, errors, and interests of its creators and users.
• We need to ensure that AI is developed and deployed with the participation, consultation, and representation of diverse stakeholders, including marginalized and vulnerable groups.
• We need to balance the advantages and disadvantages of AI, by considering not only the economic and efficiency gains but also the social and environmental costs and risks involved.