Self Driving Cars?

https://www.bitsathy.ac.in/autonomous-vehicles/

Imagine this scenario: you’re in a self-driving car. Suddenly, a pedestrian darts out into the street. The car must make a split-second decision: should it swerve and risk the passenger’s safety, or continue straight and protect the passenger at the pedestrian’s expense? Programming this kind of decision isn’t science fiction; it’s a pressing ethical issue.

This question breaks down into two main approaches:

• Passenger-first programming: The vehicle’s primary goal is to keep you (the occupant) safe.

• Harm-minimization programming: The vehicle prioritizes reducing overall harm, possibly at the expense of the occupant.

  
  

In my view, AVs should balance both. They must protect passengers as a baseline while also embedding ethical frameworks that reduce harm to others when possible. Life isn’t just black and white; there are shades of gray that need consideration.

The Importance of Regulation

If every automaker designs its own moral code, the result will be chaos. One company may protect passengers at all costs, while another tries to save the most lives. The consequence? Public trust collapses. People will avoid AVs because they won’t know what game the car is playing.

A federal standard could establish consistency and fairness, ensuring that every car on the road shares the same ethical baseline. This isn’t just a tech issue; it’s a moral imperative that affects everyone.

Trust, Fairness, and Public Adoption

Research from Park, Kim & Moon (2025) shows that public acceptance of AVs depends heavily on knowledge, perceived fairness, and transparency. If people believe the system is opaque or biased, they will resist. Conversely, engineering teams like Mehune & Dorle (2024) argue that reinforcement-learning vehicles can adapt, learn from penalties, and respond faster than human drivers. However, technological power doesn’t excuse ethical laziness.

The Balanced Approach — My Perspective

• Passengers should be protected; that’s the base expectation when someone buys an AV.

• The safety algorithm must recognize "others" on the road, including pedestrians, cyclists, and multiple vehicles, aiming to minimize overall harm.

• If AVs systematically favor passengers in wealthy cars over vulnerable road users, we risk embedding inequality into machines.

• This gap will hinder adoption faster than any technical failure.

  
  

Steps Toward Ethical AVs

1. Establish Federal Ethical Standards: Define what “safe” means for all road users, not just the person inside the car.

2. Ensure Transparency: Automated decision frameworks should be open to scrutiny. If you ride in the car, you should understand the logic behind its decisions.

3. Promote Public Education: Users need to understand how and why AVs make choices. Trust doesn’t come from secrecy.

4. Implement Continuous Monitoring and Accountability: The rules need to have consequences. If an algorithm repeatedly prioritizes one group unfairly, accountability must follow.

   
   

The Bottom Line

AVs have massive potential. We’re talking about millions of lives saved by removing human error. We all know that humans can make poor choices, but that’s a topic for another day. However, technology alone isn’t enough. If we hand over decision-making to machines without ethical oversight, we risk losing common sense, equality, and public trust.

By creating standards, enforcing fairness, and building transparency, we might not just achieve safer roads; we could also create fairer roads. That’s the future worth building.

“Tech can’t replace ethics, but it can enforce it.” - I think I'm the one that came up with that.

References

• Roff, H. M. (2018, December 17). The folly of trolleys: Ethical challenges and autonomous vehicles. Brookings Institution. https://www.brookings.edu/articles/the-folly-of-trolleys-ethical-challenges-and-autonomous-vehicles/

• Park, I. R., Kim, S., & Moon, J. (2025, February 6). Why do people resist AI-based autonomous cars?: Analyzing the impact of the risk perception paradigm and conditional value on public acceptance of autonomous vehicles. PLOS ONE, 20(2). https://doi.org/10.1371/journal.pone.0313143/

• P. Mehune and S. Dorle (2024). Robust and adaptive neural networks for self-driving cars in challenging road conditions. IEEE, International Conference on Computer, Communication and Control (IC4). https://doi.org/10.1109/IC457434.2024.10486747/

  
  

Song of the day: Nel blu dipinto di blu - Domenico Modugno