Robotics and Generative AI: Can Disabilities and Bias in AI Create a Dystopia in Cyborgs?

This article is inspired by a news item that also came with this picture: “South Korea's Gumi City Council is investigating a "robot suicide" after a cyborg administrative officer supposedly jumped to its own death from a staircase. Work pressure is now getting to robots, too. Yes, you read that right. South Korea's Gumi City Council announced on June 26 that its premier administrative officer robot apparently dropped "dead' after the cyborg seemingly leapt to its life's end down a six-and-a-half foot flight of stairs. The city council is speculating if the now-defunct robot's demise was, in fact, an act of suicide as an official caught the robot "circling in one spot as if something was there" before the supposed tragedy.”

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As technology progresses, the boundaries between humans and machines continue to blur. Robotics and generative artificial intelligence (AI) are at the forefront of this transformation, promising to enhance human capabilities and improve quality of life. We stand at a crossroads in an age where robotics and generative artificial intelligence (AI) are becoming integral to daily life. These technologies promise immense benefits, including enhanced mobility for the physically impaired and advanced cognitive assistance. However, they also pose significant risks, particularly when AI systems exhibit disabilities or biases. This article explores the potential dystopian scenarios that may arise from these shortcomings and examines solutions to mitigate these risks. We will delve into the concept of cyborgs—part human, part machine—and the impact of flawed AI on their lives, including the disturbing possibility of cyborg suicide. Whatever we are, we are creating ourselves or supplementing ourselves. This is the root cause of imbalances and biases.

Robotics and Generative AI, the current scenario -

Robotics Advances: Modern robotics has significantly progressed, particularly in assistive technologies. Robotic prosthetics, for example, have evolved from simple mechanical devices to sophisticated systems that interface with the human nervous system. Exoskeletons enable paraplegics to walk, while robotic arms provide dexterity to those who have lost limbs.

Generative AI: Generative AI refers to algorithms that create data, whether text, images or even music. These systems produce outputs that mimic human creativity. Applications range from automated content creation to designing personalized learning experiences.

Limitations and Vulnerabilities of AI

Despite their potential, AI systems are not infallible. They can exhibit disabilities, defined here as significant limitations in functionality and biases, which are systematic deviations from fairness and objectivity. These issues arise from several factors:

1. Data Bias: AI systems learn from data. If the training data is biased, the AI's decisions will reflect those biases. For example, facial recognition systems have been shown to have higher error rates for people with darker skin tones, leading to misidentifications and discriminatory outcomes.

2. Lack of Transparency: AI algorithms are often black boxes, especially deep learning models. Their opaque decision-making processes make it difficult to identify and correct errors.

3. Security Vulnerabilities: AI systems can be manipulated through adversarial attacks. Malicious actors introduce subtle changes to input data and can alter thus harming the AI's output.

4. Unintended Consequences: AI's autonomous nature can lead to unforeseen outcomes. For instance, an AI tasked with optimizing a delivery route might disregard traffic laws or ethical considerations if not properly constrained.

Let us look at some AI Disabilities and Bias in Action:

Facial Recognition Bias: Facial recognition systems have been found to exhibit significant biases, particularly against women and people of color. This bias can lead to discriminatory practices in law enforcement and other sectors.

Bias in Hiring Algorithms: AI systems used in hiring processes have been scrutinized for perpetuating gender and racial biases. An AI trained on historical hiring data from a male-dominated industry will always favor male candidates, reinforcing existing inequalities. An AI recruiting tool developed by Amazon was found to be biased against women. The tool used to screen resumes was discovered to penalize resumes, including the word "women's". The system was trained on resumes submitted to Amazon over a ten-year period, most of which came from men, reflecting gender imbalances in the tech industry.

AI and Disability Discrimination: In 2019, the New York Times reported on the case of Peter Lane, who has a form of muscular dystrophy. Lane had to resort to the increasing use of AI systems. These were not designed to accommodate disabilities. It became harder for him to receive fair treatment in various aspects of life, from job applications to healthcare.

AI in Healthcare Disparities: An AI system used by hospitals for patient identification who would benefit from extra care exhibited significant racial bias. The system was less likely to refer black patients for extra care compared to white patients, even when they were equally sick. This was because the algorithm relied on historical healthcare spending data, which is lower for black patients due to systemic inequities in access to care.

Healthcare Robotics: A healthcare robot designed to assist elderly patients was found to exhibit bias in its care recommendations. The AI was trained predominantly on data from younger, healthier populations. It failed to account for the specific needs of older adults. This led to inappropriate medication reminders and dietary suggestions, compromising patient safety.

Autonomous Vehicles: Autonomous vehicles (AVs) rely heavily on AI for navigation and decision-making. Bias in training data can lead to disparities. AVs respond differently to pedestrians of different demographics. Studies have shown that AVs are less likely to detect and stop for people with darker skin tones, raising serious safety concerns.

The Dystopian Potential: Cyborg Suicide

As we integrate AI more deeply into human bodies, creating cyborgs, the stakes become higher. Disabilities and biases in AI can have profound psychological impacts, potentially leading to cyborg suicide—a harrowing manifestation of technological dystopia. South Korea's Gumi City Council announced on June 26 that its premier administrative officer robot dropped "dead' after the cyborg seemingly leapt to its life's end down a six-and-a-half foot flight of stairs. The city council is speculating if the now-defunct robot's demise was, in fact, an act of suicide as an official caught the robot "circling in one spot as if something was there" before the supposed tragedy.

A Dystopian Scenario: When cyborgs are commonplace in the future, ultimately, these individuals will rely on AI for essential functions—movement, communication, and cognitive assistance. Let us assume a cyborg with a prosthetic arm controlled by an AI that occasionally malfunctions due to flawed programming. The arm may act unpredictably, causing frustration and self-doubt. Over time, these experiences can erode the individual's mental health, leading to a sense of hopelessness and, tragically, suicide.

Psychological Impact: It is a psychological holocaust of living with unreliable AI. Trust in one's own body is fundamental to mental well-being. When AI disabilities undermine that trust, it can lead to anxiety, depression, and suicidal ideation.

Ethical Implications: This scenario raises profound ethical questions. What responsibility do developers and manufacturers have to ensure the reliability of AI systems integrated into human bodies? How can we protect individuals from the psychological harm caused by flawed technology?

Solutions to Mitigate AI Disabilities and Bias

To prevent such dystopian outcomes, we must address the root causes of AI disabilities and biases. Here are some proposed solutions:

1. Representative and Diverse Data: Proactive efforts to collect and curate datasets that include marginalized and underrepresented groups will ensure that AI training data is diverse and representative of all user demographics. It will mitigate biases.

2. Transparency and Explainability: Developing transparent and explainable AI systems can help identify and rectify biases. Explainable techniques in AI will aim to make AI decision-making processes more understandable to humans, facilitating accountability.

3. Robust Security Measures: It is crucial to have robust security measures to protect AI systems from adversarial attacks. Regular security audits, adversarial training, and the incorporation of defensive mechanisms into AI models must be ensured.

4. Ethical AI Development: Ethical considerations must be integral to AI development. This involves adhering to ethical guidelines, conducting impact assessments, and involving diverse stakeholders in designing and deploying AI systems.

5. Mental Health Support: The psychological impact of AI disabilities can be managed by providing mental health support for individuals who rely on AI-integrated devices. Counseling and support groups tailored to the unique experiences of cyborgs are the important constituents.

6. Regulatory Oversight: Governments and regulatory bodies should establish and enforce AI safety and fairness standards. This includes certification processes for AI systems in critical applications, such as healthcare and autonomous vehicles.

Conclusion

As we navigate the complex landscape of robotics and generative AI, we must recognize and address the risks associated with AI disabilities and biases. By implementing robust solutions and ethical practices, we can harness the transformative potential of these technologies while safeguarding against dystopian outcomes. Ensuring the reliability and fairness of AI systems is not just a technical challenge but a moral imperative, essential for creating a future where technology truly enhances human well-being. Let us try to be different when we are dealing with machines, at least. They may be better hu-machines.