6 Chapter 6 Ethical Delemmas in Biotechnology and Health

Euthenasia

Assisted dying, also known as physician-assisted suicide or euthanasia, is an ethical dilemma because it sits at the intersection of deeply held beliefs about life, death, autonomy, and the role of medicine. The controversy arises because it forces society to weigh individual rights against broader ethical, legal, and moral concerns. Euthanasia is the act of deliberately ending a person’s life to relieve suffering. It’s typically offered to patients with terminal illness, chronic pain, or irreversible conditions causing extreme suffering. Here’s why it’s ethically complicated, and how it’s increasingly being linked to AI:

Why Assisted Dying Is an Ethical Dilemma:

1. Autonomy vs. Sanctity of Life
   • Pro: People have the right to make decisions about their own bodies and lives, especially when suffering is unbearable or terminal.
   • Con: Some believe life is sacred and should not be intentionally ended, no matter the circumstances.
2. Medical Ethics
   • Doctors are traditionally seen as healers. Assisted dying challenges the Hippocratic Oath—“do no harm.”
   • But is it more harmful to prolong suffering?
3. Slippery Slope Argument
   • Critics worry legalizing assisted dying may lead to abuse—pressuring vulnerable people (like the elderly or disabled) into it.
4. Cultural and Religious Beliefs
   • Different cultures and religions have varying views on whether it is morally acceptable to end a life, even to alleviate pain.
5. Quality of Life vs. Quantity of Life
   • Some argue a life filled with pain and no hope of recovery may not be worth preserving, while others see value in every moment of life.

How This Relates to AI:

1. AI in Healthcare Decision-Making
   • AI is increasingly being used to assess patient outcomes and predict survival rates or quality of life.
   • If AI suggests a poor prognosis, could it influence decisions around assisted dying? Should it?
2. AI and Autonomy
   • When AI systems provide recommendations, especially in end-of-life care, there’s concern that they may subtly shape choices rather than just support them.
   • How much should AI “nudge” or influence someone toward such an irreversible decision?
3. Bias and Ethics in AI Models
   • If AI models are trained on biased data (e.g., underrepresented demographics), they might misjudge the quality of life or care needs.
   • This could lead to unethical outcomes—like recommending assisted dying disproportionately to marginalized groups.
4. AI in Evaluating Consent or Suffering
   • Some researchers are exploring AI that can detect pain or suffering using facial recognition or speech analysis. This could be used in end-of-life decisions, but it’s highly controversial.
5. Policy & Oversight Challenges
   • Who controls the algorithms? What if an insurance company uses AI to suggest that assisted dying is cheaper than long-term care?

So, assisted dying and AI intersect at a powerful ethical crossroads, one regarding human dignity, technology’s role in life-and-death decisions, and how we define compassion and control in an age of automation. Ethical frameworks and real-life case studies that help illustrate how assisted dying becomes complex, especially in a world with advancing AI.

ETHICAL FRAMEWORKS (APPLIED TO ASSISTED DYING & AI)

1. Utilitarianism – “Greatest good for the greatest number”

• Assisted dying: It can reduce suffering for individuals in pain, thus increasing overall happiness.
• With AI: If AI helps identify patients who would benefit most from assisted dying, it might improve efficiency and reduce suffering at a larger scale.
• May overlook individual rights or minority experiences—e.g., what if AI decides a life isn’t “worth living” based on flawed logic?

2. Deontology – “Do the right thing, no matter the outcome”

• Assisted dying: Some deontologists argue that killing is always wrong, regardless of pain or consent.
• With AI: Delegating moral decisions to machines may violate our duty to respect human dignity and moral responsibility.
• Can be rigid. Might deny a suffering person relief in favor of principle.

3. Virtue Ethics – “What would a good person do?”

• Focuses on compassion, wisdom, and empathy.
• Assisted dying: A virtuous doctor may see ending suffering as an act of kindness.
• With AI: AI lacks character or emotional intelligence—can it truly make moral decisions?
• Subjective: “virtue” varies across cultures and beliefs.

4. Care Ethics – “Focus on relationships and empathy”

• Emphasizes the needs of the patient, their family, and the doctor.
• Assisted dying: Advocates listening closely to the patient’s story and emotional pain.
• With AI: AI can’t form human bonds or understand nuanced suffering, so it may fail to truly “care.”
• Might miss the bigger societal picture (e.g., fairness or policy implications).

Medical Criteria for Euthanasia (varies by country)

Most countries or jurisdictions where euthanasia is legal require some combination of these conditions:

1. Terminal Illness (e.g., cancer, ALS, late-stage organ failure)
2. Unbearable Suffering (physical or psychological)
3. Competence & Informed Consent
4. Repeated Requests over time (not impulsive)
5. Second Medical Opinion (to reduce bias or mistakes)

6. Psychiatric illness as a reason for euthanasia is legally permitted in only a few countries (e.g., Netherlands, Belgium, Canada), and it’s very controversial.

Common Medical Methods of Euthanasia

1. Intravenous Lethal Injection (most common for active euthanasia)

• Usually a combination of:
  • Sedative (like midazolam)
  • Barbiturate (like thiopental or pentobarbital)
  • Neuromuscular blocker (e.g., rocuronium)
• Painless, quick (within minutes)

2. Oral Ingestion (used in assisted suicide cases like in Oregon, USA)

• High-dose barbiturates taken by the patient (not administered by the doctor)
• May take 30 minutes to several hours

3. Withdrawal of Life Support

• Includes ventilators, dialysis, feeding tubes, etc.
• Managed with palliative sedation to ensure comfort

Role of Medical Professionals

• Physicians: Evaluate patient eligibility, provide or administer euthanasia, ensure informed consent
• Nurses: Provide emotional support, monitor patients, assist with palliative care
• Psychiatrists: Assess mental health and capacity for decision-making
• Palliative Care Teams: Explore alternatives before approving euthanasia

Medical Arguments For and Against

✅ Pro-Euthanasia (Medical View)

• Prevents prolonged suffering and agony
• Respects patient autonomy
• Allows death with dignity
• Sometimes the only relief for conditions unresponsive to palliative care

❌ Anti-Euthanasia (Medical View)

• Potential for misdiagnosis or premature death
• Mental illness or depression may cloud judgment
• Could undermine trust in the healthcare system (“Will doctors give up too soon?”)
• Alternative treatments or palliative care might be underused

CASE STUDIES

Case 1: The Netherlands – AI and Euthanasia

• The Netherlands allows euthanasia, including for psychiatric patients in rare cases.
• AI angle: There are pilot projects using AI to support diagnostics and psychological assessments.
• Concern: Can AI accurately determine if someone with depression is making a rational request for death—or are they in temporary distress?
• Is AI “judging” someone’s capacity to consent?

Case 2: Canada – MAiD (Medical Assistance in Dying)

• Canada legalized MAiD, including for non-terminal conditions.
• There’s been criticism over disabled people being offered MAiD instead of proper social support.
• Potential AI role: Algorithms used by hospitals or social services might “recommend” MAiD as cost-effective.
• What happens if cost-cutting AI systems prioritize death over care?

Case 3: AI Predicting Mortality (Stanford Study)

• Stanford developed an AI that could predict if a patient would die within 6 months with 90% accuracy.
• Purpose: Help doctors have earlier conversations about end-of-life care.
• If AI predicts a short life, could that sway a patient or family toward assisted dying too early?

Case 4: Belgium – The “Wim Distelmans” Cases

• Belgium allows euthanasia even for non-terminal cases, including psychological suffering.
• Cases have included young people with depression and trauma.
• Raises ethical questions about how suffering is measured and whether AI could/should help in these judgments.
• Would an algorithm ever be capable of understanding deep personal trauma?

Cloning

Cloning is the process of creating an exact genetic copy of a biological organism. A researcher makes c copy of DNA. The result is a new organism with the same DNA as the original. A defective DNA is manipulated to create a flawless DNA specimen.

How cloning is achieved:

• Take a somatic cell (body cell) from the organism you want to clone.
• Remove the nucleus (where the DNA is stored).
• Take an egg cell from a female of the same species and remove its nucleus.
• Insert the donor DNA into the empty egg cell.
• Use electricity or chemicals to stimulate the cell to start dividing.
• Implant the developing embryo into a surrogate.
• If successful, the baby born will be a genetic clone.

Why is cloning performed:

• To preserve endangered or extinct species (experimental).
• For medical research and regenerative medicine.
• To create genetically identical animals for research or agriculture.
• Cloning or editing genes—especially in humans—raises questions about whether we’re overstepping nature or spiritual/moral boundaries.
• Should humans be designing life?

2. Identity & Individuality

• A clone may be genetically identical, but are they the same person?
• What rights and recognition do they have?

3. Genetic Discrimination

• If we can edit genes, will people with “undesirable” traits be viewed as lesser?
• Could we create a “genetic underclass”?

4. Designer Babies

• CRISPR allows editing embryos for intelligence, looks, or health.
• Ethically: Should we be enhancing humans for non-medical traits?

5. Consent

• A cloned or genetically edited baby cannot consent to being altered.
• Is it ethical to change a person before they exist?

ETHICAL CONCERNS WHEN AI IS INVOLVED IN CLONING & GENETICS

1. AI in Gene Editing Decisions

• AI is used to analyze genetic data and suggest edits (e.g., removing disease genes).
• What if AI starts making value judgments about what traits are “desirable”?
  Is it ethical for an algorithm to define “normal” or “better”?

2. Predictive Genetic Profiling

• AI can predict disease risk, intelligence potential, or personality traits from DNA.
  • Insurance discrimination
  • Selective abortion
  • Pressure to edit embryos

3. Cloning & AI-Enhanced Replication

• Future: AI might be used to simulate or guide the behavior, thoughts, or personalities of clones (like virtual “reconstructions”).
• Are we creating a human—or a digital puppet?

4. Data Privacy & Genetic Surveillance

• AI is used to mine huge genetic databases (like 23andMe, AncestryDNA).
• DNA is the most personal data you have, should corporations or governments use it to build AI models?

5. Bias in AI Genetic Models

• If training data is biased (e.g., from one ethnicity), AI might misinterpret DNA from other populations.
  • Misdiagnosis
  • Unequal access to gene therapies
  • Racist or ableist assumptions encoded in medical systems

ETHICAL FRAMEWORKS (APPLIED TO CLONING & AI)

CASE STUDIES

Case Study 1: CRISPR Baby Controversy (China, 2018)

• A scientist used CRISPR to alter twin embryos to be HIV-resistant.
• Global backlash: ethics committees weren’t consulted, and consent was unclear.
• AI angle: AI likely used in genome analysis—raises the question: should AI be guiding embryo selection?

Case Study 2: AI Predicting Embryo Viability (IVF Clinics)

• Some clinics now use AI to choose the “best” embryos for implantation.
• Are we letting AI define which life is worth creating?

Case Study 3: Designer Baby Future Scenario

• Parents use AI to choose embryo with highest predicted IQ.
• Genetic edits are made for appearance, personality traits.
• Raises:
  • Inequality: Only the rich can afford “superior” babies
  • Identity crisis for the child: “Was I chosen for who I am?”

MORE QUESTIONS FOR DISCUSSION / ESSAY

• Who gets to decide what’s a “good” or “normal” gene—humans or AI?
• Should AI be involved in decisions about human life at all?
• How do we protect people’s rights in a future of genetic enhancement?

IVF

In Vitro Fertilization (IVF) is a fertility treatment where an egg and sperm are combined outside the body in a lab dish. Once fertilized, the embryo is implanted into the uterus.

Ethical Dilemmas in IVF

IVF brings hope—but also a lot of ethical gray areas:

1. Embryo Selection

• Multiple embryos are often created, some are frozen, some discarded.
• Ethical questions:
  • Is it okay to discard “extra” embryos?
  • Should we select embryos for specific traits?

2. Accessibility & Equity

• IVF is expensive. Is it fair that only wealthy people can access it?
• Are we creating a two-tier reproductive system?

3. Parental Age & Limits

• Some people use IVF to have children at advanced ages (60+).
• Should there be an age limit to IVF?

4. Surplus Embryos

• Frozen embryos can remain unused for years.
• What happens to them? Donation, destruction, or indefinite storage?

5. Donor Anonymity

• Use of donor eggs or sperm can lead to identity and legal issues.
• Should children conceived via IVF have the right to know their genetic origins?

6. Preimplantation Genetic Diagnosis (PGD)

• Embryos can be screened for genetic diseases—or even sex, hair color, IQ potential.
• This leads into the “designer baby” debate.

IVF + AI: Ethical Impacts

1. AI in Embryo Selection

• AI models analyze embryos and predict which one is most likely to result in pregnancy.
• May consider factors like shape, movement, and genetic profile.
• Should AI decide what kind of life is most “viable”? Could this lead to bias or eugenics?

2. Sex & Trait Selection

• Some AI tools can indirectly assist in selecting embryos with specific traits.
• Can easily cross the line into “designer baby” territory.
• Should we allow choosing traits that aren’t health-related?

3. AI + PGD (Preimplantation Genetic Diagnosis)

• AI can analyze genetic risk of embryos for conditions like Alzheimer’s, autism, or even lower IQ.
• Parents might choose embryos based on predicted cognitive or physical outcomes.
• Are we eliminating difference and diversity?

4. Data Privacy

• IVF clinics using AI collect massive amounts of sensitive medical and genetic data.
• How is this data stored? Who owns it? Could it be sold or misused?

Case Studies

Case Study 1: AI Embryo Ranking (Israel & USA)

• Companies like Embryonics and Life Whisperer developed AI tools to rank embryos based on potential to implant successfully.
• Some clinics now use AI embryo scores to make implantation decisions.
• What if the AI is biased toward certain racial or biological markers? Who’s accountable if it fails?

Case Study 2: UK Baby With DNA From 3 Parents

• IVF + mitochondrial transfer used to avoid genetic disease.
• Involves DNA from mother, father, and donor.
• AI was used in embryo monitoring and selection.
• How many genetic contributors are too many? Are we changing the definition of parenthood?

Case Study 3: Genetic Risk Scoring for Embryos

• Some private clinics in the U.S. offer polygenic risk scores to predict future disease or intelligence.
• These scores are powered by AI models analyzing genomic data.
• It’s early science, not fully accurate, and could lead to discrimination or false hope.

Case Study 4: Failed AI-Based Selection

• In a few IVF cases, AI recommended low-scoring embryos, but human doctors overrode it—and those embryos led to healthy pregnancies.
• Shows AI is still learning—and raises trust issues.

More Ethical Questions

• Should AI be allowed to make life-and-death reproductive decisions?
• Who is responsible if AI makes a harmful or biased decision?
• Are we heading toward a future where AI helps “design” children?
• How do we ensure equal access to AI-enhanced IVF?

Medical Ethics in Organ Donation

Organ donation saves lives—but it raises serious ethical questions, especially about fairness, consent, and bodily autonomy. Organ donation is the process of giving an organ or tissue to help someone else who needs a transplant. This can happen:

• After death (deceased donors)
• While alive (living donors – e.g., kidney, part of liver)

Key Ethical Dilemmas:

1. Consent

• Should organ donation be opt-in (you must register) or opt-out (everyone is a donor unless they say no)?
• Is presumed consent ethical if families disagree?

2. Brain Death & Timing

• Organs must be harvested quickly after death.
• Some cultures and religions dispute brain death criteria.

3. Organ Allocation

• Who gets an organ when supply of the organ is limited?
• Is it fair to prioritize younger, healthier, or wealthier patients?

4. Living Donation Risks

• Living donors undergo major surgery.
• Is it ethical to encourage someone to risk their life to save another?

5. Commercialization

• Selling organs is illegal in most countries, but black markets exist.
• Could payment exploit poor or vulnerable people?

AI in Organ Donation: Opportunities & Ethical Risks

Benefits of AI:

• Match donors to recipients faster and more accurately
• Predict how well a transplanted organ will function
• Help determine transplant eligibility
• Monitor post-transplant health using predictive analytics

Ethical Concerns:

1. Bias in Matching Algorithms

• AI systems can reflect healthcare bias if trained on unequal or incomplete data.
• Could lead to inequitable organ allocation based on race, income, or geography.

2. Transparency

• Many AI systems are “black boxes.”
• If a patient is denied a transplant based on AI prediction, can they challenge that decision?

3. Data Privacy

• AI needs huge datasets—often including medical, genetic, and even behavioral data.
• How is that data protected? Who has access?

4. Algorithmic Triage

• In emergencies, AI could help decide who gets an organ.
• But should an algorithm make that decision instead of a doctor or ethics board?

Ethical Frameworks You Can Apply:

Case Studies

Case Study 1: United Network for Organ Sharing (UNOS), USA

• AI is being explored to optimize organ allocation for heart, liver, and kidney transplants.
• Algorithms analyze urgency, geography, blood type, etc.
• Critics argue the system favored urban areas and disadvantaged rural or minority patients.
• Raised concerns about equity in AI-driven allocation.

Case Study 2: DeepMind (UK) + Kidney Transplants

• DeepMind (owned by Google) developed an AI model to predict kidney transplant rejection.
• The system could spot early signs of failure before doctors could.
• Big ethical win—but raised concerns about data sharing, especially since it involved NHS patient data.

Case Study 3: AI Matching Systems in Eurotransplant

• Eurotransplant uses advanced algorithms for cross-border organ matching in Europe.
• AI helps rank recipients based on medical urgency and donor-recipient compatibility.
• Should someone with a lower survival probability be skipped for someone with a better match, even if they’re more stable?

More Questions

• Should organ allocation decisions ever be left to AI?
• Is it ethical to deny someone an organ based on predictive data?
• How do we balance fairness, efficiency, and transparency in organ donation systems?
• Could AI lead to better trust—or more suspicion—in life-or-death medical decisions?

ETHICAL DILEMMAS IN NUTRITION & FOOD INNOVATION

How Food Products Are Brought to Market (U.S.): Bringing a new food product to market involves several key stages:

1. Product Development

• Companies develop a food item (e.g., snack, drink, supplement, or meat alternative).
  • Ingredient sourcing
  • Nutritional profiling
  • Taste testing
  • Packaging and branding

2. Determine the Food Category

The FDA classifies food products into different categories:

3. Ingredient & Additive Safety Evaluation

If the product contains new or novel ingredients, especially food additives, the company must:

Submit a Food Additive Petition to the FDA

• This includes safety studies, toxicology reports, and manufacturing details.
• FDA evaluates if the additive is safe for human consumption.

OR

Claim it as GRAS (Generally Recognized As Safe)

• Based on scientific consensus or history of safe use.
• Companies can self-determine GRAS status and may notify the FDA, but it’s voluntary. This GRAS process is controversial, some say it lets companies bypass stricter FDA review.

4. Labeling & Nutrition Facts

• The FDA regulates food labels to ensure they are:
  • Truthful and not misleading
  • Include nutritional facts, allergens, and ingredient lists
  • Follow specific formats and language (e.g., “low fat,” “high fiber” must meet standards)

Labels must also comply with:

• Nutrient content claims rules
• Health claims (must be backed by scientific evidence)
• Structure/function claims (common on supplements, e.g., “supports immunity”)

5. Facility Registration & Compliance

• Food facilities must register with the FDA under the Food Safety Modernization Act (FSMA).
• They must follow Good Manufacturing Practices (GMP) and Hazard Analysis Critical Control Point (HACCP) systems to minimize contamination risks.

6. Post-Market Surveillance

Once the product hits the market, the FDA can:

• Conduct inspections
• Issue warning letters
• Recall unsafe products
• Monitor adverse health reports (especially for supplements)
  • For supplements, the FDA only steps in after harm is reported—they’re not pre-approved.

Lab-Grown Foods, AI-Generated Ingredients, and Novel Tech

If a food uses biotechnology (e.g., lab-grown meat, genetically engineered yeast, AI-generated enzymes), then:

1. Consultation with FDA or USDA may be required

• For cell-cultured meat, both the FDA and USDA are involved in a joint regulatory framework.

2. Novel Ingredients (like AI-designed proteins) might not be considered GRAS

• Companies often go through voluntary pre-market consultation.

Ethical & Regulatory Challenges

• Loop-holes in GRAS: Critics say companies sometimes exploit GRAS to avoid full review.
• Supplements = Light Oversight: FDA doesn’t pre-approve them; many harmful ones only get pulled after people get sick.
• AI-Driven Ingredients: Hard to evaluate using traditional risk models; some experts call for new frameworks for AI-designed foods.

Real Example: Impossible Burger

• Uses heme from genetically modified yeast to replicate meat flavor.
• The company submitted a GRAS notification to the FDA in 2014.
• FDA originally didn’t object—but consumer pressure led Impossible Foods to pursue full FDA approval later.
• Now widely sold, but the process highlighted gaps in regulation for biotech-based food.

Summary: FDA’s Role in Food Product Regulation

Ethical Dilemmas in Food Products and Nutrition

1. Health vs. Profit

• Many supplements and “healthy foods” are marketed aggressively, despite limited, or non-existent scientific backing.
• Is it right to profit from people seeking better health with questionable products?

2. Nutritional Misinformation

• Influencers and unqualified “experts” often promote diets or supplements with no medical oversight.
• Leads to eating disorders, nutrient deficiencies, or harm.
• Who regulates truth in food marketing?

3. Access and Food Inequality

• Nutritional innovation (like plant-based meats or personalized supplements) is often expensive.
• Is it fair if only the rich get access to “next-gen” healthy food while low-income communities rely on processed, unhealthy options?

4. Supplements and Regulation

• Supplements aren’t FDA-approved and don’t require clinical testing before going to market.
• Is it acceptable to sell products that haven’t been proven safe or effective?

5. Sustainability vs. Cultural Tradition

• New food products (like lab-grown meat or algae protein) aim to be sustainable.
• But some argue this erodes cultural, regional, or traditional food practices.

AI IN NUTRITION: WHAT IT’S DOING (AND WHY IT’S ETHICALLY COMPLEX)

AI is now being used to develop personalized diets, create new food products, and analyze health data at scale, but it’s not without problems.

• Personalized nutrition: AI tailors meal plans or supplements based on DNA, gut microbiome, lifestyle.
• Food innovation: AI models help design new ingredients, meat substitutes, or allergen-free recipes.
• Predictive health: AI can analyze eating habits to detect early signs of disease or nutrient deficiencies.

Ethical Concerns with AI in Nutrition

1. Data Privacy

• AI nutrition apps collect sensitive health, DNA, and behavioral data.
• Who owns it? Can it be sold to insurers or advertisers?

2. Bias in Algorithms

• If AI is trained on data from mostly wealthy, mostly white, Western populations, it may give poor advice to people from other backgrounds.
• This can lead to. unethical or ineffective nutrition plans.

3. Oversimplified Health Scoring

• AI apps may assign food “scores” without context—e.g., “apples are bad for your blood sugar.”
• Users develop disordered eating or reject healthy foods based on bad AI advice.

4. Nutritional Equity

• High-tech nutrition is often expensive.
• AI-based food innovation could worsen health divides between rich and poor.

ETHICAL FRAMEWORKS TO APPLY

CASE STUDIES

Case Study 1: ZOE Personalized Nutrition (UK/US)

• Uses AI to analyze microbiome, blood sugar, and fat responses to give custom diet advice.
• Some users report dramatic improvements, but critics express concerns:
  • High cost limits access
  • Microbiome science is still developing
  • Risk of overreliance on AI over real nutrition professionals

Case Study 2: Supplement Scandals (US)

• 2015: GNC, Walmart, Target, and Walgreens caught selling supplements that didn’t contain the ingredients listed, AI and DNA testing revealed the fraud.
• Raised serious ethical concerns about truth in labeling, lack of regulation, and false health claims.

Case Study 3: Brightseed AI + Plant Compounds

• Brightseed uses AI to discover new nutrients in plants (like previously unknown antioxidants).
• Goal: Develop next-gen supplements and food additives.
• Should companies patent and profit from naturally occurring compounds?

Case Study 4: Lab-Grown Meat & AI Flavor Mapping

• AI is used to replicate the flavor and texture of real meat in lab-grown or plant-based products (e.g., Impossible Foods, JUST Meat).
• Ethical debate:
  • Pro: More sustainable and cruelty-free.
  • Con: May impact farmers, traditions, or be less healthy than claimed.