Ethical Issues in Generative AI: A case Study
We’ll probably look back on 2022 as the year generative Artificial Intelligence (AI) exploded
into public attention, as image-generating systems from OpenAI and Stability AI were
released, prompting a flood of fantastical images on social media. Last week, researchers at
Meta announced an AI system that can negotiate with humans and generate dialogue in a
strategy game called Diplomacy. Venture capital investment in the field grew to $1.3 billion
this year, according to Pitchbook, even as it contracted for other areas in tech. The digital
artist Beeple was shocked in August when several Twitter users generated their own versions
of one of his paintings with AI-powered tools. Similar software can create music and videos.
The broad term for all this is ‘generative AI’ and as we lurch to the digital future, familiar
tech industry challenges like copyright and social harm are re-emerging. Earlier this month,
Meta unveiled Galactica, a language system specializing in science that could write research
papers and Wikipedia articles. Within three days, Meta shut it down. Early testers found it
was generating nonsense that sounded dangerously realistic, including instructions on how
to make napalm in a bathtub and Wikipedia entries on the benefits of being Caucasian or
how bears live in space. The eerie effect was facts mixed in so finely with hogwash that it
was hard to tell the difference between the two. Political and health-related misinformation
is hard enough to track when it’s written by humans.
1. What are the ethical issues in the above case?
2. Can we have ‘ethical AI’?
3. Suggest measures that must be taken to prevent moral damage that can from AI.
1. Ethical Issues in Generative AI
Issue Explanation
Misinformation & AI systems like Galactica generated realistic but false or harmful
Harm content (e.g., how to make napalm), posing risks to public safety.
Outputs included racially biased content, indicating embedded
Bias & Discrimination
systemic biases in training data.
Intellectual Property Artists like Beeple were disturbed by unauthorized AI-generated
Concerns imitations of their work, raising copyright and originality issues.
When AI systems cause harm (e.g., spreading misinformation), it's
Lack of
unclear who is legally or morally responsible—developers, users, or
Accountability
companies.
Issue Explanation
Manipulation & Tools can be exploited to spread propaganda or influence public
Social Harm opinion through seemingly credible content.
2. Can We Have ‘Ethical AI’?
Yes, but it requires:
Human Oversight
Transparent Algorithms
Value Alignment
Legal and Ethical Standards
Accountability
Challenges include:
Defining universal ethical norms.
Encoding cultural and moral diversity.
Making complex AI decisions explainable.
AI must be continuously monitored, as ethical issues evolve with technological capabilities.
3. Measures to Prevent Moral Damage from AI
Measure Explanation
Adopt global AI ethics guidelines (like UNESCO or EU’s AI Act)
Ethical Frameworks
focusing on human rights and social good.
Regularly audit datasets and algorithms to identify and correct
Bias Audits
biases.
Transparency & Develop explainable AI to help users understand and verify
Explainability outputs.
Implement legal frameworks to assign responsibility and penalize
Accountability Laws
misuse.
Human Oversight Use human supervision in sensitive areas. (e.g., medicine, legal,
Measure Explanation
defense).
Public Awareness Educate users on AI's limits and risks.
Measure Details
Ethical Frameworks Follow global AI guidelines (UNESCO, EU AI Act).
Bias Audits Regularly check training data and models.
Explainable AI Build systems whose decisions can be understood.
Legal Accountability Assign responsibility for harmful AI outputs.
Human-in-the-Loop Use human supervision in sensitive areas.
Public Awareness Educate users on AI's limits and risks.
Case Studies
Meta Galactica: Taken offline due to harmful pseudo-scientific content.
Beeple Incident: AI imitated artwork without permission, raising copyright issues.
Microsoft Tay (2016): Turned offensive due to lack of filters and troll inputs.
Deepfakes: Misused for misinformation and character assassination.
ChatGPT in Exams: Used for cheating, raising academic integrity concerns.
Conclusion
Ethical AI is possible, but not automatic. It demands:
Ongoing regulation,
Transparent design,
Human-centered values, and
Vigilant societal engagement.
