Artificial  Intelligence in Food Science

1. Artificiel Intelligence in
Food Science
Lecturer: Mr. Muhammad Amjad Raza
Email: ch.amjadraza@gmail.com
amjad.raza@cs.uol.edu.pk
ِ‫ن‬ٰ‫م‬ْ‫ح‬َّ‫الر‬ ِ‫ه‬‫الل‬ ِ‫م‬ْ‫س‬ِ‫ب‬
ِ‫م‬ْ‫ي‬ِ‫ح‬َّ‫الر‬

2. Why Do Ethics Matter?
• AI systems make decisions that have real-world
consequences for individuals and society.
• Ensuring these decisions are fair, just, and beneficial is a
critical challenge.
• We must proactively address the ethical landscape to
prevent unintended harm.

3. Challenge 1: Bias and Fairness
• The Problem: AI models learn from data. If the data reflects existing societal biases
(gender, age, color etc), the AI will learn and often amplify these biases.
• "Garbage in, garbage out."
• Examples:
• Hiring tools that favor male candidates because they were trained on historical hiring
data from a male-dominated industry.
• Facial recognition systems that are less accurate for women and people of color.
• Loan application algorithms that discriminate against applicants from certain
neighborhoods.

4. Challenge 2: Privacy and
Surveillance
• The Problem: AI thrives on vast amounts of data. This creates a powerful incentive for
companies and governments to collect personal information on an unprecedented scale.
• Concerns:
• Constant Monitoring: Smart devices, social media, and CCTV with facial recognition
can create a state of perpetual surveillance.
• Data Misuse: Personal data can be used for manipulation (e.g., targeted political
advertising) or sold without consent.
• Anonymity is Disappearing: AI can de-anonymize data, linking seemingly
anonymous information back to specific individuals.

5. Challenge 3: Accountability and
Transparency
• The "Black Box" Problem: Many advanced AI models, like deep learning networks, are incredibly
complex. Even their creators don't always understand exactly how they arrive at a specific decision.
• Accountability: If an autonomous vehicle causes an accident or an AI misdiagnoses a patient,
who is responsible?
• The programmer?
• The user?
• The company that built it?
• The AI itself?
• Transparency: Without understanding the AI's reasoning (explainability), it's impossible to trust its
decisions, identify errors, or correct biases.

6. Challenge 4: Autonomy and
Decision-Making
• The Problem: As AI becomes more autonomous, we are ceding significant decisions to
machines that lack human empathy, morality, and contextual understanding.
• High-Stakes Areas:
• Autonomous Weapons (Lethal Autonomous Weapons Systems - LAWS): AI
making "life or death" decisions on the battlefield without direct human control.
• Criminal Justice: AI used for predictive policing or sentencing recommendations,
which could entrench bias and lead to unfair outcomes.
• Healthcare: AI recommending treatments or resource allocation.

7. Challenge 5: Socioeconomic Impact
• The Problem: AI-driven automation is transforming the labor market and has the
potential to widen the gap between the rich and the poor.
• Major Concerns:
• Job Displacement: AI could automate millions of jobs, from truck driving and
manufacturing to some white-collar professions like paralegal work.
• Economic Inequality: The benefits of AI may be concentrated among a
small group of tech owners and highly-skilled workers, while wages for others
stagnate.
• Digital Divide: Access to AI tools and the skills to use them could become a
new marker of inequality.

8. Moving Forward: Addressing the
Challenges
• There's no single solution, but a multi-faceted approach is essential.
• Ethical Frameworks & Guidelines: Developing clear principles for responsible AI development
(e.g., EU's AI Act).
• Regulation and Oversight: Governments must create laws to ensure accountability, protect
privacy, and enforce fairness.
• Diverse and Inclusive Teams: Building AI development teams with diverse backgrounds to better
identify and mitigate biases.
• Transparency and Explainability (XAI): Pushing for research into AI systems that can explain
their decision-making processes.
• Public Dialogue: Fostering open conversation among technologists, policymakers, and the public
about the kind of future we want with AI.

9. Questions?