Essay Reflection
- Describe your writing process for this essay. For example, did you go through the conventional steps of prewriting (brainstorming, freewriting, listing, mapping, etc.), planning (whether by an outline or otherwise), drafting, getting feedback from others, and revising, or did you take another approach? You might include comments here about how your writing occurred (With pen and paper? On your phone/tablet/laptop? In a lab?) and also when it occurred (spread out over ten days vs. the night before?).
- Evaluate your writing process for this essay. What worked well for you? What is something you might do differently next time? What would possibly be improved by this change?
- On a scale of 1-10, with 1 being the easiest and 10 being the hardest, how difficult was this essay for you to write? What aspects of the assignment were easy for you, and what aspects posed challenges?
- What changes did you make to your essay as a result of feedback from others (peers, friends and family, or your professor)?
- Using a scale of 1-10, with 1 being the worst and 10 being the best, evaluate your written product; that is, how well did the essay turn out in your view? Was it successful? Based on your evaluation of the final draft, what are its strong points? Where could it continue to be improved?
- If there are any additional things to note about the grade distribution or essay writing process, you may note them below.
The reflection should be a minimum of 350 words
Student Response: 200 words
With the tremendous growth in the field of artificial intelligence in recent years, it has become imperative to prioritize the ethical integration of AI into new systems. This development compels us to be mindful of the moral responsibilities we have as citizens in ensuring fair AI systems. Moral responsibility is a complex issue with multiple stakeholders who may be deemed morally responsible depending on the circumstances.
A compelling example that illustrates the challenges of moral responsibility in AI is the case of Cambridge Analytica in 2018. The firm misused personal data collected from Facebook users without their consent to influence political campaigns. In this case, multiple parties could be implicated for moral responsibility. Firstly, the users who shared their data on Facebook could be seen as morally responsible for not being cautious while sharing their data. Secondly, Facebook, as the platform that allowed the data leak to occur, could also be morally responsible for failing to protect user data and prevent the unethical use of their platform. Lastly, regulators and policymakers could also be morally responsible for not implementing proper safeguards and regulations to prevent such abuses of artificial intelligence.
Another notable example that highlights the same issue is the incident in 2018 where an Uber self-driving car killed a pedestrian in Arizona, USA. This incident raises questions about the safety and moral responsibility of autonomous or AI-driven vehicles. Who is responsible for the accident? Is it the fault of the car or the person who incorporated the rules in the car and designed it? Should a car prioritize the safety of the passenger inside over other pedestrians?
There are measures that can be implemented to make AI safer and more reliable. Developers should consider and adopt ethical frameworks such as transparency, fairness, and accountability while designing AI systems so that they can make sound decisions even in worst-case scenarios. Human oversight of AI systems is also crucial, as there may be situations where AI cannot discern between very similar options. Additionally, implementing strict rules and regulations for the development, deployment, and use of AI systems is vital. This includes establishing specific guidelines for data privacy, security, and ethical behavior, and imposing penalties and fines for non-compliance.
In conclusion, moral responsibility in the realm of artificial intelligence is a critical aspect that requires immediate attention. It is a multifaceted issue that demands active involvement from various stakeholders, including developers, organizations, users, and data providers. All these parties share equal responsibility in ensuring the safe and ethical utilization of AI technologies.
