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Algorithmic Bias Corporate Obligations.

📌 1. What Is Algorithmic Bias?

Algorithmic bias occurs when an AI/automated system:

Produces unfair outcomes

Disadvantages certain groups

Reflects skewed training data

Uses flawed decision variables

It becomes a legal issue when it affects rights, employment, credit, healthcare, or public services.

📌 2. Where Corporates Use Algorithms (High-Risk Areas)

📌 3. Legal Foundations of Corporate Obligation

Even without AI-specific laws, existing frameworks apply:

📌 4. Why Corporates Can’t Blame the Algorithm

Courts treat AI systems as tools controlled by the company.

Liability arises when:

Company deploys biased model

Fails to test for fairness

Uses automated decisions without oversight

Ignores complaints

📌 5. Core Corporate Legal Duties

🔹 1. Duty of Non-Discrimination

Decisions must not unjustifiably disadvantage groups.

🔹 2. Duty of Care

Reasonable testing before deployment.

🔹 3. Transparency Obligation

Explain automated decisions where required.

🔹 4. Data Governance Duty

Ensure training data not biased or unlawful.

🔹 5. Human Oversight

Avoid fully automated high-impact decisions.

📌 6. When Liability Becomes Severe

✔ Biased hiring rejection
✔ Denial of loan due to flawed scoring
✔ Insurance premium discrimination
✔ Exclusion of protected groups
✔ Automated termination decisions

📌 7. Important Case Laws Influencing Bias Liability

(AI-specific rulings are evolving, but principles come from broader law)

⭐ 1) E.P. Royappa v. State of Tamil Nadu (1974, SC)

Principle: Arbitrariness violates equality.
Relevance: Biased algorithmic decisions can be arbitrary.

⭐ 2) Maneka Gandhi v. Union of India (1978, SC)

Principle: Fairness in decision-making.
Relevance: Automated decisions must follow fair procedure.

⭐ 3) Justice K.S. Puttaswamy v. Union of India (2017, SC)

Principle: Informational privacy and autonomy.
Relevance: Profiling and automated decisions affect privacy rights.

⭐ 4) Donoghue v. Stevenson (1932)

Principle: Duty of care in using products.
Relevance: Companies must ensure AI systems are reasonably safe.

⭐ 5) Spring Meadows Hospital v. Harjol Ahluwalia (1998, SC)

Principle: Institutional negligence liability.
Relevance: Corporates liable for flawed AI-assisted decisions.

⭐ 6) Anvar P.V. v. P.K. Basheer (2014, SC)

Principle: Reliability of electronic records.
Relevance: Algorithmic decision logs must be reliable.

⭐ 7) Google India Pvt. Ltd. v. Visaka Industries (2020, SC)

Principle: Liability depends on control and knowledge.
Relevance: Corporates controlling AI cannot avoid responsibility.

📌 8. Corporate Compliance Measures

✔ Bias testing before deployment
✔ Diverse training data review
✔ AI ethics policy
✔ Human review for high-impact decisions
✔ Recordkeeping of model decisions
✔ Complaint and redress mechanism

📌 9. Contractual Safeguards

Vendor warranty on non-discriminatory design

Audit rights

Indemnity for biased model defects

Right to inspect training methodology

📌 10. Key Legal Takeaway

Algorithmic bias is treated as:

Unfair, arbitrary, or negligent corporate decision-making.

Courts will ask:

Did the company take reasonable steps to prevent bias?

If not → liability arises, regardless of who built the algorithm.

AI is a tool. Responsibility stays human and corporate.

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